From a5e9070f6993f86d0a4fad2baf3819436c51a079 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Thu, 12 Sep 2024 20:04:52 -0400 Subject: [PATCH 01/16] Complete Part 1: CPU Scan & Stream Compaction --- stream_compaction/cpu.cu | 63 ++++++++++++++++++++++++++++++++++++---- stream_compaction/cpu.h | 2 ++ 2 files changed, 60 insertions(+), 5 deletions(-) diff --git a/stream_compaction/cpu.cu b/stream_compaction/cpu.cu index 719fa11..366bbbc 100644 --- a/stream_compaction/cpu.cu +++ b/stream_compaction/cpu.cu @@ -17,9 +17,21 @@ namespace StreamCompaction { * For performance analysis, this is supposed to be a simple for loop. * (Optional) For better understanding before starting moving to GPU, you can simulate your GPU scan in this function first. */ + + void exclusivePrefixSum(int n, int* odata, const int* idata) { + int prefixSum = 0; + + for (int i = 0; i < n; ++i) { + odata[i] = prefixSum; + prefixSum += idata[i]; + } + } + void scan(int n, int *odata, const int *idata) { timer().startCpuTimer(); - // TODO + + exclusivePrefixSum(n, odata, idata); + timer().endCpuTimer(); } @@ -30,9 +42,19 @@ namespace StreamCompaction { */ int compactWithoutScan(int n, int *odata, const int *idata) { timer().startCpuTimer(); - // TODO + + int compactedCount = 0; + + for (int i = 0; i < n; ++i) + { + if (idata[i] != 0) { + odata[compactedCount++] = idata[i]; + } + } + timer().endCpuTimer(); - return -1; + + return compactedCount; } /** @@ -42,9 +64,40 @@ namespace StreamCompaction { */ int compactWithScan(int n, int *odata, const int *idata) { timer().startCpuTimer(); - // TODO + + if (n <= 0) { + timer().endCpuTimer(); + return 0; + } + + int* binaryMap = new int[n]; + int* exclusivePrefixSumResult = new int[n]; + + // map the input to an array of 0s and 1s + for (int i = 0; i < n; ++i) + { + binaryMap[i] = idata[i] == 0 ? 0 : 1; + } + + // scan the array of 0s and 1s + exclusivePrefixSum(n, exclusivePrefixSumResult, binaryMap); + + // scatter + int compactedCount = 0; + + for (int i = 0; i < n - 1; ++i) + { + if (exclusivePrefixSumResult[i] != exclusivePrefixSumResult[i + 1]) { + odata[compactedCount++] = idata[i]; + } + } + timer().endCpuTimer(); - return -1; + + delete[] binaryMap; + delete[] exclusivePrefixSumResult; + + return compactedCount; } } } diff --git a/stream_compaction/cpu.h b/stream_compaction/cpu.h index 873c047..110549b 100644 --- a/stream_compaction/cpu.h +++ b/stream_compaction/cpu.h @@ -6,6 +6,8 @@ namespace StreamCompaction { namespace CPU { StreamCompaction::Common::PerformanceTimer& timer(); + void exclusivePrefixSum(int n, int* odata, const int* idata); + void scan(int n, int *odata, const int *idata); int compactWithoutScan(int n, int *odata, const int *idata); From 1832fc0176d3e412a7740e2d0703565a0301b63d Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Thu, 12 Sep 2024 22:22:55 -0400 Subject: [PATCH 02/16] Part 1 formatting tweaks --- stream_compaction/cpu.cu | 19 ++++++++++++------- stream_compaction/cpu.h | 2 +- 2 files changed, 13 insertions(+), 8 deletions(-) diff --git a/stream_compaction/cpu.cu b/stream_compaction/cpu.cu index 366bbbc..025b9fe 100644 --- a/stream_compaction/cpu.cu +++ b/stream_compaction/cpu.cu @@ -18,10 +18,12 @@ namespace StreamCompaction { * (Optional) For better understanding before starting moving to GPU, you can simulate your GPU scan in this function first. */ - void exclusivePrefixSum(int n, int* odata, const int* idata) { + void exclusivePrefixSum(const int n, const int* idata, int* odata) + { int prefixSum = 0; - for (int i = 0; i < n; ++i) { + for (int i = 0; i < n; ++i) + { odata[i] = prefixSum; prefixSum += idata[i]; } @@ -30,7 +32,7 @@ namespace StreamCompaction { void scan(int n, int *odata, const int *idata) { timer().startCpuTimer(); - exclusivePrefixSum(n, odata, idata); + exclusivePrefixSum(n, idata, odata); timer().endCpuTimer(); } @@ -47,7 +49,8 @@ namespace StreamCompaction { for (int i = 0; i < n; ++i) { - if (idata[i] != 0) { + if (idata[i] != 0) + { odata[compactedCount++] = idata[i]; } } @@ -65,7 +68,8 @@ namespace StreamCompaction { int compactWithScan(int n, int *odata, const int *idata) { timer().startCpuTimer(); - if (n <= 0) { + if (n <= 0) + { timer().endCpuTimer(); return 0; } @@ -80,14 +84,15 @@ namespace StreamCompaction { } // scan the array of 0s and 1s - exclusivePrefixSum(n, exclusivePrefixSumResult, binaryMap); + exclusivePrefixSum(n, binaryMap, exclusivePrefixSumResult); // scatter int compactedCount = 0; for (int i = 0; i < n - 1; ++i) { - if (exclusivePrefixSumResult[i] != exclusivePrefixSumResult[i + 1]) { + if (exclusivePrefixSumResult[i] != exclusivePrefixSumResult[i + 1]) + { odata[compactedCount++] = idata[i]; } } diff --git a/stream_compaction/cpu.h b/stream_compaction/cpu.h index 110549b..4e044a8 100644 --- a/stream_compaction/cpu.h +++ b/stream_compaction/cpu.h @@ -6,7 +6,7 @@ namespace StreamCompaction { namespace CPU { StreamCompaction::Common::PerformanceTimer& timer(); - void exclusivePrefixSum(int n, int* odata, const int* idata); + void exclusivePrefixSum(const int n, const int* idata, int* odata); void scan(int n, int *odata, const int *idata); From 11c8290f6000ba559bacbb2a7055f00c8c210c50 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Sat, 14 Sep 2024 17:44:26 -0400 Subject: [PATCH 03/16] Complete Part 2: Naive GPU Scan Algorithm implementations - non-shared mem and shared mem (#2) --- src/main.cpp | 22 ++- stream_compaction/common.h | 3 + stream_compaction/naive.cu | 285 ++++++++++++++++++++++++++++++++++++- stream_compaction/naive.h | 20 ++- 4 files changed, 321 insertions(+), 9 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index 896ac2b..fede9e2 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -48,8 +48,8 @@ int main(int argc, char* argv[]) { printCmpResult(NPOT, b, c); zeroArray(SIZE, c); - printDesc("naive scan, power-of-two"); - StreamCompaction::Naive::scan(SIZE, c, a); + printDesc("naive scan, power-of-two, no shared memory"); + StreamCompaction::Naive::scan(SIZE, c, a, false); printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); //printArray(SIZE, c, true); printCmpResult(SIZE, b, c); @@ -61,8 +61,22 @@ int main(int argc, char* argv[]) { printArray(SIZE, c, true); */ zeroArray(SIZE, c); - printDesc("naive scan, non-power-of-two"); - StreamCompaction::Naive::scan(NPOT, c, a); + printDesc("naive scan, non-power-of-two, no shared memory"); + StreamCompaction::Naive::scan(NPOT, c, a, false); + printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + //printArray(SIZE, c, true); + printCmpResult(NPOT, b, c); + + zeroArray(SIZE, c); + printDesc("naive scan, power-of-two, shared memory"); + StreamCompaction::Naive::scan(SIZE, c, a, true); + printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + //printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); + + zeroArray(SIZE, c); + printDesc("naive scan, non-power-of-two, shared memory"); + StreamCompaction::Naive::scan(NPOT, c, a, true); printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); //printArray(SIZE, c, true); printCmpResult(NPOT, b, c); diff --git a/stream_compaction/common.h b/stream_compaction/common.h index d2c1fed..37ea45f 100644 --- a/stream_compaction/common.h +++ b/stream_compaction/common.h @@ -2,6 +2,7 @@ #include #include +#include #include #include @@ -10,6 +11,8 @@ #include #include +#define blockSize 32 + #define FILENAME (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__) #define checkCUDAError(msg) checkCUDAErrorFn(msg, FILENAME, __LINE__) diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 4308876..94e3909 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -11,15 +11,292 @@ namespace StreamCompaction { static PerformanceTimer timer; return timer; } - // TODO: __global__ + + __global__ void kernelInclusiveToExclusivePrefixSum(const int n, const int* idata, int* odata) + { + int g_index = (blockIdx.x * blockDim.x) + threadIdx.x; + if (g_index >= n) + { + return; + } + + if (g_index > 0) + { + odata[g_index] = idata[g_index - 1]; + } + else if (g_index == 0) + { + odata[g_index] = 0; + } + } + + __global__ void kernelNaiveInclusivePrefixSumPass(const int n, const int offset, const int* idata, int* odata) + { + int g_index = (blockIdx.x * blockDim.x) + threadIdx.x; + if (g_index >= n) + { + return; + } + + if (g_index >= offset) + { + odata[g_index] = idata[g_index - offset] + idata[g_index]; + } + else + { + odata[g_index] = idata[g_index]; + } + } + + __global__ void kernelNaiveInclusivePrefixSumByBlock(const int n, const int* idata, int* odata) + { + int g_index = blockIdx.x * blockDim.x + threadIdx.x; + + if (g_index >= n) + { + return; + } + + // allocated on invocation + extern __shared__ int doubleBuffer[]; + + int tx = threadIdx.x; + + int pout = 0, pin = 1; + + // Load input into shared memory. + doubleBuffer[pout * blockSize + tx] = idata[g_index]; + doubleBuffer[pin * blockSize + tx] = doubleBuffer[pout * blockSize + tx]; + __syncthreads(); + + for (int offset = 1; offset < blockSize; offset *= 2) + { + // swap double buffer indices + pout = 1 - pout; + pin = 1 - pout; + + if (tx >= offset) + { + doubleBuffer[pout * blockSize + tx] = doubleBuffer[pin * blockSize + tx - offset] + doubleBuffer[pin * blockSize + tx]; + } + else + { + doubleBuffer[pout * blockSize + tx] = doubleBuffer[pin * blockSize + tx]; + } + __syncthreads(); + } + + // write output + odata[g_index] = doubleBuffer[pout * blockSize + tx]; + } + + __global__ void kernelNaiveExclusivePrefixSumByBlock(const int n, const int* idata, int* odata) + { + int g_index = blockIdx.x * blockDim.x + threadIdx.x; + + if (g_index >= n) + { + return; + } + + // allocated on invocation + extern __shared__ int doubleBuffer[]; + + int tx = threadIdx.x; + + int pout = 0, pin = 1; + + // Load input into shared memory. + // This is exclusive scan, so shift right by one + // and set first element to 0 + doubleBuffer[pout * blockSize + tx] = (tx > 0) ? idata[g_index - 1] : 0; + doubleBuffer[pin * blockSize + tx] = doubleBuffer[pout * blockSize + tx]; + __syncthreads(); + + for (int offset = 1; offset < blockSize; offset *= 2) + { + // swap double buffer indices + pout = 1 - pout; + pin = 1 - pout; + + if (tx >= offset) + { + doubleBuffer[pout * blockSize + tx] = doubleBuffer[pin * blockSize + tx - offset] + doubleBuffer[pin * blockSize + tx]; + } + else + { + doubleBuffer[pout * blockSize + tx] = doubleBuffer[pin * blockSize + tx]; + } + __syncthreads(); + } + + // write output + odata[g_index] = doubleBuffer[pout * blockSize + tx]; + } + + __global__ void kernelExtractBlockSums(const int n, const int numBlocks, const int* idata, int* odata) + { + int g_index = blockIdx.x * blockDim.x + threadIdx.x; + + if (g_index >= numBlocks) + { + return; + } + + odata[g_index] = g_index == numBlocks - 1 ? idata[n - 1] : idata[(g_index * blockSize) + blockSize - 1]; + } + + __global__ void kernelAddBlockIncrements(const int n, const int* idataBlockSums, const int* idata, int* odata) + { + int g_index = blockIdx.x * blockDim.x + threadIdx.x; + + if (g_index >= n) + { + return; + } + + odata[g_index] = idata[g_index] + idataBlockSums[blockIdx.x]; + } /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ - void scan(int n, int *odata, const int *idata) { + void scan(int n, int* odata, const int* idata, bool useSharedMemory) + { + if (useSharedMemory) + { + naiveExclusivePrefixSumSharedMemory(n, idata, odata); + } + else + { + naiveExclusivePrefixSum(n, idata, odata); + } + } + + void naiveExclusivePrefixSum(const int n, const int* idata, int* odata) + { + int* dev_bufferA; + int* dev_bufferB; + + cudaMalloc((void**)&dev_bufferA, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_bufferA failed!"); + + cudaMalloc((void**)&dev_bufferB, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_bufferB failed!"); + + cudaMemcpy(dev_bufferA, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + checkCUDAError("memcpy idata to dev_bufferA failed!"); + + dim3 blocksPerGrid((n + blockSize - 1) / blockSize); + timer().startGpuTimer(); - // TODO + + + for (int offset = 1; offset < n; offset *= 2) + { + kernelNaiveInclusivePrefixSumPass<<>>(n, offset, dev_bufferA, dev_bufferB); + checkCUDAError("kernelNaiveInclusivePrefixSumPass failed!"); + + // set the input of the next iteration to the output of this iteration + std::swap(dev_bufferA, dev_bufferB); + } + + kernelInclusiveToExclusivePrefixSum<<>>(n, dev_bufferA, dev_bufferB); + checkCUDAError("kernelInclusiveToExclusivePrefixSum failed!"); + + cudaMemcpy(odata, dev_bufferB, sizeof(int) * n, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_bufferB to odata failed!"); + + timer().endGpuTimer(); + + cudaFree(dev_bufferA); + cudaFree(dev_bufferB); + } + + void naiveExclusivePrefixSumSharedMemory(const int n, const int* idata, int* odata) + { + int* dev_bufferA; + int* dev_bufferB; + + cudaMalloc((void**)&dev_bufferA, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_bufferA failed!"); + + cudaMalloc((void**)&dev_bufferB, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_bufferB failed!"); + + cudaMemcpy(dev_bufferA, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + checkCUDAError("memcpy idata to dev_bufferA failed!"); + + const int sharedMemoryBytes = 2 * blockSize * sizeof(int); + + int numBlocks = (n + blockSize - 1) / blockSize; + + timer().startGpuTimer(); + + + naiveInclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, n, numBlocks, dev_bufferA, dev_bufferB); + + kernelInclusiveToExclusivePrefixSum<<<(n + blockSize - 1) / blockSize, blockSize>>>(n, dev_bufferB, dev_bufferA); + checkCUDAError("kernelInclusiveToExclusivePrefixSum failed!"); + + cudaMemcpy(odata, dev_bufferA, sizeof(int) * n, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_bufferA to odata failed!"); + + + timer().endGpuTimer(); + + cudaFree(dev_bufferA); + cudaFree(dev_bufferB); + } + + // iterative approach is possible if the blockSums buffers are allocated carefully ahead of time, combined with clever indexing of them at each iteration + // for the sake of submitting this assignement on time, this will have to be explored at a later time + void naiveInclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int numBlocks, int* idata, int* odata) + { + kernelNaiveInclusivePrefixSumByBlock<<>>(n, idata, odata); + checkCUDAError("kernelNaiveInclusivePrefixSumByBlock failed!"); + + if (numBlocks > 1) + { + int* dev_bufferBlockSumsA; + int* dev_bufferBlockSumsB; + + cudaMalloc((void**)&dev_bufferBlockSumsA, sizeof(int) * numBlocks); + checkCUDAError("cudaMalloc dev_bufferBlockSumsA failed!"); + + cudaMalloc((void**)&dev_bufferBlockSumsB, sizeof(int) * numBlocks); + checkCUDAError("cudaMalloc dev_bufferBlockSumsB failed!"); + + int numBlocksForBlockSums = (numBlocks + blockSize - 1) / blockSize; + kernelExtractBlockSums<<>>(n, numBlocks, odata, dev_bufferBlockSumsA); + checkCUDAError("kernelExtractBlockSums failed!"); + + if (numBlocksForBlockSums > 1) + { + naiveInclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, numBlocks, numBlocksForBlockSums, dev_bufferBlockSumsA, dev_bufferBlockSumsB); + + kernelInclusiveToExclusivePrefixSum<<>>(numBlocks, dev_bufferBlockSumsB, dev_bufferBlockSumsA); + checkCUDAError("kernelInclusiveToExclusivePrefixSum failed!"); + + kernelAddBlockIncrements<<>>(n, dev_bufferBlockSumsA, odata, idata); + checkCUDAError("kernelAddBlockIncrements failed!"); + } + else + { + kernelNaiveExclusivePrefixSumByBlock<<>>(numBlocks, dev_bufferBlockSumsA, dev_bufferBlockSumsB); + checkCUDAError("kernelNaiveExclusivePrefixSumByBlock failed!"); + + kernelAddBlockIncrements<<>>(n, dev_bufferBlockSumsB, odata, idata); + checkCUDAError("kernelAddBlockIncrements failed!"); + } + + cudaMemcpy(odata, idata, sizeof(int) * n, cudaMemcpyDeviceToDevice); + checkCUDAError("memcpy idata to odata failed!"); + + cudaFree(dev_bufferBlockSumsA); + cudaFree(dev_bufferBlockSumsB); + } } } -} +} \ No newline at end of file diff --git a/stream_compaction/naive.h b/stream_compaction/naive.h index 37dcb06..41417f5 100644 --- a/stream_compaction/naive.h +++ b/stream_compaction/naive.h @@ -6,6 +6,24 @@ namespace StreamCompaction { namespace Naive { StreamCompaction::Common::PerformanceTimer& timer(); - void scan(int n, int *odata, const int *idata); + __global__ void kernelInclusiveToExclusivePrefixSum(const int n, const int* idata, int* odata); + + __global__ void kernelNaiveInclusivePrefixSumPass(const int n, const int offset, const int* idata, int* odata); + + __global__ void kernelNaiveInclusivePrefixSumByBlock(const int n, const int* idata, int* odata); + + __global__ void kernelNaiveExclusivePrefixSumByBlock(const int n, const int* idata, int* odata); + + __global__ void kernelExtractBlockSums(const int n, const int numBlocks, const int* idata, int* odata); + + __global__ void kernelAddBlockIncrements(const int n, const int* idataBlockSums, const int* idata, int* odata); + + void scan(int n, int* odata, const int* idata, bool useSharedMemory); + + void naiveExclusivePrefixSum(const int n, const int* idata, int* odata); + + void naiveExclusivePrefixSumSharedMemory(const int n, const int* idata, int* odata); + + void naiveInclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int numBlocks, int* idata, int* odata); } } From 0a47206a21c88cf6f888d21fd31f78c9bc37fce4 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Sat, 14 Sep 2024 18:13:57 -0400 Subject: [PATCH 04/16] Part 1 rework - cpu compact with scan can now handle if the last element of the stream is a "hit" --- src/main.cpp | 11 +++++++++-- stream_compaction/cpu.cu | 7 +++++++ 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index fede9e2..0502d25 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -141,12 +141,19 @@ int main(int argc, char* argv[]) { printCmpLenResult(count, expectedNPOT, b, c); zeroArray(SIZE, c); - printDesc("cpu compact with scan"); + printDesc("cpu compact with scan, power-of-two"); count = StreamCompaction::CPU::compactWithScan(SIZE, c, a); printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(count, c, true); + //printArray(count, c, true); printCmpLenResult(count, expectedCount, b, c); + zeroArray(SIZE, c); + printDesc("cpu compact with scan, non-power-of-two"); + count = StreamCompaction::CPU::compactWithScan(NPOT, c, a); + printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); + //printArray(count, c, true); + printCmpLenResult(count, expectedNPOT, b, c); + zeroArray(SIZE, c); printDesc("work-efficient compact, power-of-two"); count = StreamCompaction::Efficient::compact(SIZE, c, a); diff --git a/stream_compaction/cpu.cu b/stream_compaction/cpu.cu index 025b9fe..cdd6156 100644 --- a/stream_compaction/cpu.cu +++ b/stream_compaction/cpu.cu @@ -97,6 +97,13 @@ namespace StreamCompaction { } } + // handle case where last element of binary map is a "hit" AKA == 1 + // the exclusive prefixSumResult doesnt capture this edge case + if (binaryMap[n - 1] == 1) + { + odata[compactedCount++] = idata[n - 1]; + } + timer().endCpuTimer(); delete[] binaryMap; From 65c2637a0924a156b972bb55d036d5a5e07c50e2 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Sun, 15 Sep 2024 23:03:03 -0400 Subject: [PATCH 05/16] Complete Part 3: Work-Efficient GPU Scan & Stream Compaction - non-shared mem and shared mem. Part 1 and 2 slightly modified (#3) --- src/main.cpp | 44 +++++- stream_compaction/common.cu | 20 ++- stream_compaction/cpu.cu | 14 +- stream_compaction/efficient.cu | 276 ++++++++++++++++++++++++++++++++- stream_compaction/efficient.h | 18 ++- stream_compaction/naive.cu | 97 ++++++------ stream_compaction/naive.h | 2 +- 7 files changed, 391 insertions(+), 80 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index 0502d25..cee7ca4 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -82,15 +82,29 @@ int main(int argc, char* argv[]) { printCmpResult(NPOT, b, c); zeroArray(SIZE, c); - printDesc("work-efficient scan, power-of-two"); - StreamCompaction::Efficient::scan(SIZE, c, a); + printDesc("work-efficient scan, power-of-two, no shared memory"); + StreamCompaction::Efficient::scan(SIZE, c, a, false); printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); //printArray(SIZE, c, true); printCmpResult(SIZE, b, c); zeroArray(SIZE, c); - printDesc("work-efficient scan, non-power-of-two"); - StreamCompaction::Efficient::scan(NPOT, c, a); + printDesc("work-efficient scan, non-power-of-two, no shared memory"); + StreamCompaction::Efficient::scan(NPOT, c, a, false); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + //printArray(NPOT, c, true); + printCmpResult(NPOT, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient scan, power-of-two, shared memory"); + StreamCompaction::Efficient::scan(SIZE, c, a, true); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + //printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient scan, non-power-of-two, shared memory"); + StreamCompaction::Efficient::scan(NPOT, c, a, true); printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); //printArray(NPOT, c, true); printCmpResult(NPOT, b, c); @@ -155,15 +169,29 @@ int main(int argc, char* argv[]) { printCmpLenResult(count, expectedNPOT, b, c); zeroArray(SIZE, c); - printDesc("work-efficient compact, power-of-two"); - count = StreamCompaction::Efficient::compact(SIZE, c, a); + printDesc("work-efficient compact, power-of-two, no shared memory"); + count = StreamCompaction::Efficient::compact(SIZE, c, a, false); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + //printArray(count, c, true); + printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient compact, non-power-of-two, no shared memory"); + count = StreamCompaction::Efficient::compact(NPOT, c, a, false); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + //printArray(count, c, true); + printCmpLenResult(count, expectedNPOT, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient compact, power-of-two, shared memory"); + count = StreamCompaction::Efficient::compact(SIZE, c, a, true); printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); //printArray(count, c, true); printCmpLenResult(count, expectedCount, b, c); zeroArray(SIZE, c); - printDesc("work-efficient compact, non-power-of-two"); - count = StreamCompaction::Efficient::compact(NPOT, c, a); + printDesc("work-efficient compact, non-power-of-two, shared memory"); + count = StreamCompaction::Efficient::compact(NPOT, c, a, true); printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); //printArray(count, c, true); printCmpLenResult(count, expectedNPOT, b, c); diff --git a/stream_compaction/common.cu b/stream_compaction/common.cu index 2ed6d63..c2aa1b6 100644 --- a/stream_compaction/common.cu +++ b/stream_compaction/common.cu @@ -23,7 +23,14 @@ namespace StreamCompaction { * which map to 0 will be removed, and elements which map to 1 will be kept. */ __global__ void kernMapToBoolean(int n, int *bools, const int *idata) { - // TODO + + int g_index = (blockIdx.x * blockDim.x) + threadIdx.x; + if (g_index >= n) + { + return; + } + + bools[g_index] = idata[g_index] == 0 ? 0 : 1; } /** @@ -32,7 +39,16 @@ namespace StreamCompaction { */ __global__ void kernScatter(int n, int *odata, const int *idata, const int *bools, const int *indices) { - // TODO + + int g_index = (blockIdx.x * blockDim.x) + threadIdx.x; + + // index out of bounds or not a "hit" + if ((g_index >= n) || (bools[g_index] == 0)) + { + return; + } + + odata[indices[g_index]] = idata[g_index]; } } diff --git a/stream_compaction/cpu.cu b/stream_compaction/cpu.cu index cdd6156..7b43002 100644 --- a/stream_compaction/cpu.cu +++ b/stream_compaction/cpu.cu @@ -89,21 +89,15 @@ namespace StreamCompaction { // scatter int compactedCount = 0; - for (int i = 0; i < n - 1; ++i) + for (int i = 0; i < n; ++i) { - if (exclusivePrefixSumResult[i] != exclusivePrefixSumResult[i + 1]) + if (binaryMap[i] == 1) { - odata[compactedCount++] = idata[i]; + odata[exclusivePrefixSumResult[i]] = idata[i]; + ++compactedCount; } } - // handle case where last element of binary map is a "hit" AKA == 1 - // the exclusive prefixSumResult doesnt capture this edge case - if (binaryMap[n - 1] == 1) - { - odata[compactedCount++] = idata[n - 1]; - } - timer().endCpuTimer(); delete[] binaryMap; diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 2db346e..8082d6e 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -12,13 +12,219 @@ namespace StreamCompaction { return timer; } + __global__ void kernelReductionPass(const int bufferLength, const int offset, int* data) + { + int g_index = (blockIdx.x * blockDim.x) + threadIdx.x; + + // index out of bounds or not a rhs child node + if ((g_index >= bufferLength) || ((g_index + 1) % (2 * offset) != 0)) + { + return; + } + + data[g_index] += data[g_index - offset]; + } + + __global__ void kernelDownSweepPass(const int bufferLength, const int offset, int* data) + { + int g_index = (blockIdx.x * blockDim.x) + threadIdx.x; + + // index out of bounds or not a rhs child node + if ((g_index >= bufferLength) || ((g_index + 1) % (2 * offset) != 0)) + { + return; + } + + int tmp = data[g_index]; + data[g_index] += data[g_index - offset]; + data[g_index - offset] = tmp; + } + + __global__ void kernelEfficientExclusivePrefixSumByBlock(const int n, int* data, int* sums) + { + extern __shared__ int shared[]; + + int tx = threadIdx.x; + int g_index = blockIdx.x * blockDim.x + tx; + + // Load input into shared memory + if (g_index < n) + { + shared[tx] = data[g_index]; + } + else + { + shared[tx] = 0; + } + __syncthreads(); + + // Upsweep (Reduction) phase + for (int offset = 1; offset < blockSize; offset *= 2) + { + if ((tx + 1) % (2 * offset) == 0) + { + shared[tx] += shared[tx - offset]; + } + __syncthreads(); + } + + // Clear the last element + if (tx == blockSize - 1) + { + sums[blockIdx.x] = shared[tx]; + shared[tx] = 0; + } + __syncthreads(); + + // Downsweep phase + for (int offset = blockSize / 2; offset > 0; offset /= 2) + { + if ((tx + 1) % (2 * offset) == 0) + { + int temp = shared[tx]; + shared[tx] += shared[tx - offset]; + shared[tx - offset] = temp; + } + __syncthreads(); + } + + // Write the results back to global memory + if (g_index < n) + { + data[g_index] = shared[tx]; + } + } + + __global__ void kernelAddBlockSumsToBlockData(const int n, const int* idataBlockSums, int* data) + { + int g_index = blockIdx.x * blockDim.x + threadIdx.x; + if (g_index >= n) + { + return; + } + + data[g_index] += idataBlockSums[blockIdx.x]; + } + /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ - void scan(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO - timer().endGpuTimer(); + void scan(int n, int* odata, const int* idata, bool useSharedMemory) + { + if (useSharedMemory) + { + efficientExclusivePrefixSumSharedMemory(true, n, idata, odata); + } + else + { + efficientExclusivePrefixSum(true, n, idata, odata); + } + } + + void efficientExclusivePrefixSum(const bool useGpuTimer, const int n, const int* idata, int* odata) + { + int totalPasses = ilog2ceil(n); + int bufferLength = 1 << totalPasses; + + int* dev_data; + + cudaMalloc((void**)&dev_data, sizeof(int) * bufferLength); + checkCUDAError("cudaMalloc dev_data failed!"); + + cudaMemcpy(dev_data, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + checkCUDAError("memcpy idata to dev_data failed!"); + + if (bufferLength > n) + { + cudaMemset(dev_data + n, 0, sizeof(int) * (bufferLength - n)); + checkCUDAError("cudaMemset elements n to bufferLength - 1 in dev_data to 0 failed!"); + } + + int blocksPerGrid = (bufferLength + blockSize - 1) / blockSize; + + if (useGpuTimer) timer().startGpuTimer(); + + + for (int offset = 1; offset < bufferLength; offset *= 2) + { + kernelReductionPass<<>>(bufferLength, offset, dev_data); + checkCUDAError("kernelReductionPass failed!"); + } + + cudaMemset(dev_data + bufferLength - 1, 0, sizeof(int)); + checkCUDAError("cudaMemset last element in dev_data to 0 failed!"); + + for (int offset = bufferLength / 2; offset > 0; offset /= 2) + { + kernelDownSweepPass<<>>(bufferLength, offset, dev_data); + checkCUDAError("kernelDownSweepPass failed!"); + } + + + if (useGpuTimer) timer().endGpuTimer(); + + cudaMemcpy(odata, dev_data, sizeof(int) * n, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_data to odata failed!"); + + cudaFree(dev_data); + } + + void efficientExclusivePrefixSumSharedMemory(const bool useGpuTimer, const int n, const int* idata, int* odata) + { + int blocksPerGrid = (n + blockSize - 1) / blockSize; + + int* dev_data; + int* dev_sums; + + cudaMalloc((void**)&dev_data, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_data failed!"); + + cudaMalloc((void**)&dev_sums, sizeof(int) * blocksPerGrid); + checkCUDAError("cudaMalloc dev_sums failed!"); + + cudaMemcpy(dev_data, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + checkCUDAError("memcpy idata to dev_data failed!"); + + int sharedMemoryBytes = blockSize * sizeof(int); + + if (useGpuTimer) timer().startGpuTimer(); + + + efficientExclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, n, blocksPerGrid, dev_data, dev_sums); + + + if (useGpuTimer) timer().endGpuTimer(); + + cudaMemcpy(odata, dev_data, sizeof(int) * n, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_data to odata failed!"); + + cudaFree(dev_data); + cudaFree(dev_sums); + } + + // iterative approach is possible + // for the sake of submitting this assignement on time, this will have to be explored at a later time + void efficientExclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int numBlocks, int* data, int* sums) + { + kernelEfficientExclusivePrefixSumByBlock<<>>(n, data, sums); + checkCUDAError("kernelEfficientExclusivePrefixSumByBlock failed!"); + + if (numBlocks > 1) + { + int numBlocksForBlockSums = (numBlocks + blockSize - 1) / blockSize; + + int* dev_sums; + + cudaMalloc((void**)&dev_sums, sizeof(int) * numBlocksForBlockSums); + checkCUDAError("cudaMalloc dev_sums failed!"); + + efficientExclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, numBlocks, numBlocksForBlockSums, sums, dev_sums); + + kernelAddBlockSumsToBlockData<<>>(n, sums, data); + checkCUDAError("kernelAddBlockSumsToBlockData failed!"); + + cudaFree(dev_sums); + } } /** @@ -30,11 +236,67 @@ namespace StreamCompaction { * @param idata The array of elements to compact. * @returns The number of elements remaining after compaction. */ - int compact(int n, int *odata, const int *idata) { + int compact(int n, int* odata, const int* idata, bool useSharedMemory) + { + int* dev_idata; + int* dev_odata; + int* dev_binaryMap; + int* dev_exclusivePrefixSumResult; + + cudaMalloc((void**)&dev_idata, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_idata failed!"); + + cudaMalloc((void**)&dev_odata, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_odata failed!"); + + cudaMalloc((void**)&dev_binaryMap, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_binaryMap failed!"); + + cudaMalloc((void**)&dev_exclusivePrefixSumResult, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_exclusivePrefixSumResult failed!"); + + cudaMemcpy(dev_idata, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + checkCUDAError("memcpy idata to dev_idata failed!"); + + int blocksPerGrid = (n + blockSize - 1) / blockSize; + timer().startGpuTimer(); - // TODO + + + Common::kernMapToBoolean<<>>(n, dev_binaryMap, dev_idata); + + if (useSharedMemory) + { + efficientExclusivePrefixSumSharedMemory(false, n, dev_binaryMap, dev_exclusivePrefixSumResult); + } + else + { + efficientExclusivePrefixSum(false, n, dev_binaryMap, dev_exclusivePrefixSumResult); + } + + Common::kernScatter<<>>(n, dev_odata, dev_idata, dev_binaryMap, dev_exclusivePrefixSumResult); + + timer().endGpuTimer(); - return -1; + + cudaMemcpy(odata, dev_odata, sizeof(int) * n, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_bufferB to odata failed!"); + + int compactedCount; + cudaMemcpy(&compactedCount, dev_exclusivePrefixSumResult + (n - 1), sizeof(int), cudaMemcpyDeviceToHost); + checkCUDAError("cudaMemcpy dev_exclusivePrefixSumResult to compactedCount failed!"); + + if (idata[n - 1] != 0) + { + ++compactedCount; + } + + cudaFree(dev_idata); + cudaFree(dev_odata); + cudaFree(dev_binaryMap); + cudaFree(dev_exclusivePrefixSumResult); + + return compactedCount; } } } diff --git a/stream_compaction/efficient.h b/stream_compaction/efficient.h index 803cb4f..089c5e0 100644 --- a/stream_compaction/efficient.h +++ b/stream_compaction/efficient.h @@ -6,8 +6,22 @@ namespace StreamCompaction { namespace Efficient { StreamCompaction::Common::PerformanceTimer& timer(); - void scan(int n, int *odata, const int *idata); + __global__ void kernelReductionPass(const int bufferLength, const int offset, int* data); - int compact(int n, int *odata, const int *idata); + __global__ void kernelDownSweepPass(const int bufferLength, const int offset, int* data); + + __global__ void kernelEfficientExclusivePrefixSumByBlock(const int n, int* data, int* sums); + + __global__ void kernelAddBlockSumsToBlockData(const int n, const int* idataBlockSums, int* data); + + void scan(int n, int* odata, const int* idata, bool useSharedMemory); + + void efficientExclusivePrefixSum(const bool useGpuTimer, const int n, const int* idata, int* odata); + + void efficientExclusivePrefixSumSharedMemory(const bool useGpuTimer, const int n, const int* idata, int* odata); + + void efficientExclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int numBlocks, int* data, int* sums); + + int compact(int n, int* odata, const int* idata, bool useSharedMemory); } } diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 94e3909..b75ae7e 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -50,94 +50,94 @@ namespace StreamCompaction { __global__ void kernelNaiveInclusivePrefixSumByBlock(const int n, const int* idata, int* odata) { - int g_index = blockIdx.x * blockDim.x + threadIdx.x; + // allocated on invocation + extern __shared__ int doubleBuffer[]; + int g_index = blockIdx.x * blockDim.x + threadIdx.x; if (g_index >= n) { return; } - // allocated on invocation - extern __shared__ int doubleBuffer[]; - int tx = threadIdx.x; - int pout = 0, pin = 1; + // identify which half of double buffer is read-half and write-half + int writeBuffer = 0; + int readBuffer = 1; - // Load input into shared memory. - doubleBuffer[pout * blockSize + tx] = idata[g_index]; - doubleBuffer[pin * blockSize + tx] = doubleBuffer[pout * blockSize + tx]; + // Load input into shared memory + // Only need to write to the first half since our first write will be to the second half + doubleBuffer[tx] = idata[g_index]; __syncthreads(); for (int offset = 1; offset < blockSize; offset *= 2) { // swap double buffer indices - pout = 1 - pout; - pin = 1 - pout; + writeBuffer = 1 - writeBuffer; + readBuffer = 1 - writeBuffer; if (tx >= offset) { - doubleBuffer[pout * blockSize + tx] = doubleBuffer[pin * blockSize + tx - offset] + doubleBuffer[pin * blockSize + tx]; + doubleBuffer[writeBuffer * blockSize + tx] = doubleBuffer[readBuffer * blockSize + tx - offset] + doubleBuffer[readBuffer * blockSize + tx]; } else { - doubleBuffer[pout * blockSize + tx] = doubleBuffer[pin * blockSize + tx]; + doubleBuffer[writeBuffer * blockSize + tx] = doubleBuffer[readBuffer * blockSize + tx]; } __syncthreads(); } // write output - odata[g_index] = doubleBuffer[pout * blockSize + tx]; + odata[g_index] = doubleBuffer[writeBuffer * blockSize + tx]; } __global__ void kernelNaiveExclusivePrefixSumByBlock(const int n, const int* idata, int* odata) { - int g_index = blockIdx.x * blockDim.x + threadIdx.x; + // allocated on invocation + extern __shared__ int doubleBuffer[]; + int g_index = blockIdx.x * blockDim.x + threadIdx.x; if (g_index >= n) { return; } - // allocated on invocation - extern __shared__ int doubleBuffer[]; - int tx = threadIdx.x; - int pout = 0, pin = 1; + // identify which half of double buffer is read-half and write-half + int writeBuffer = 0; + int readBuffer = 1; - // Load input into shared memory. - // This is exclusive scan, so shift right by one - // and set first element to 0 - doubleBuffer[pout * blockSize + tx] = (tx > 0) ? idata[g_index - 1] : 0; - doubleBuffer[pin * blockSize + tx] = doubleBuffer[pout * blockSize + tx]; + // Load input into shared memory + // Exclusive scan - shift all elements right by one and set first element to 0 + // Only need to write to the first half since our first write will be to the second half + doubleBuffer[tx] = (tx > 0) ? idata[g_index - 1] : 0; __syncthreads(); for (int offset = 1; offset < blockSize; offset *= 2) { // swap double buffer indices - pout = 1 - pout; - pin = 1 - pout; + writeBuffer = 1 - writeBuffer; + readBuffer = 1 - writeBuffer; if (tx >= offset) { - doubleBuffer[pout * blockSize + tx] = doubleBuffer[pin * blockSize + tx - offset] + doubleBuffer[pin * blockSize + tx]; + doubleBuffer[writeBuffer * blockSize + tx] = doubleBuffer[readBuffer * blockSize + tx - offset] + doubleBuffer[readBuffer * blockSize + tx]; } else { - doubleBuffer[pout * blockSize + tx] = doubleBuffer[pin * blockSize + tx]; + doubleBuffer[writeBuffer * blockSize + tx] = doubleBuffer[readBuffer * blockSize + tx]; } __syncthreads(); } // write output - odata[g_index] = doubleBuffer[pout * blockSize + tx]; + odata[g_index] = doubleBuffer[writeBuffer * blockSize + tx]; } __global__ void kernelExtractBlockSums(const int n, const int numBlocks, const int* idata, int* odata) { int g_index = blockIdx.x * blockDim.x + threadIdx.x; - if (g_index >= numBlocks) { return; @@ -146,16 +146,15 @@ namespace StreamCompaction { odata[g_index] = g_index == numBlocks - 1 ? idata[n - 1] : idata[(g_index * blockSize) + blockSize - 1]; } - __global__ void kernelAddBlockIncrements(const int n, const int* idataBlockSums, const int* idata, int* odata) + __global__ void kernelAddBlockSumsToBlockData(const int n, const int* idataBlockSums, int* data) { int g_index = blockIdx.x * blockDim.x + threadIdx.x; - if (g_index >= n) { return; } - odata[g_index] = idata[g_index] + idataBlockSums[blockIdx.x]; + data[g_index] += idataBlockSums[blockIdx.x]; } /** @@ -187,7 +186,7 @@ namespace StreamCompaction { cudaMemcpy(dev_bufferA, idata, sizeof(int) * n, cudaMemcpyHostToDevice); checkCUDAError("memcpy idata to dev_bufferA failed!"); - dim3 blocksPerGrid((n + blockSize - 1) / blockSize); + int blocksPerGrid = (n + blockSize - 1) / blockSize; timer().startGpuTimer(); @@ -204,12 +203,12 @@ namespace StreamCompaction { kernelInclusiveToExclusivePrefixSum<<>>(n, dev_bufferA, dev_bufferB); checkCUDAError("kernelInclusiveToExclusivePrefixSum failed!"); - cudaMemcpy(odata, dev_bufferB, sizeof(int) * n, cudaMemcpyDeviceToHost); - checkCUDAError("memcpy dev_bufferB to odata failed!"); - timer().endGpuTimer(); + cudaMemcpy(odata, dev_bufferB, sizeof(int) * n, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_bufferB to odata failed!"); + cudaFree(dev_bufferA); cudaFree(dev_bufferB); } @@ -228,24 +227,24 @@ namespace StreamCompaction { cudaMemcpy(dev_bufferA, idata, sizeof(int) * n, cudaMemcpyHostToDevice); checkCUDAError("memcpy idata to dev_bufferA failed!"); - const int sharedMemoryBytes = 2 * blockSize * sizeof(int); + int blocksPerGrid = (n + blockSize - 1) / blockSize; - int numBlocks = (n + blockSize - 1) / blockSize; + int sharedMemoryBytes = 2 * blockSize * sizeof(int); timer().startGpuTimer(); - naiveInclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, n, numBlocks, dev_bufferA, dev_bufferB); + naiveInclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, n, blocksPerGrid, dev_bufferA, dev_bufferB); - kernelInclusiveToExclusivePrefixSum<<<(n + blockSize - 1) / blockSize, blockSize>>>(n, dev_bufferB, dev_bufferA); + kernelInclusiveToExclusivePrefixSum<<>>(n, dev_bufferB, dev_bufferA); checkCUDAError("kernelInclusiveToExclusivePrefixSum failed!"); - cudaMemcpy(odata, dev_bufferA, sizeof(int) * n, cudaMemcpyDeviceToHost); - checkCUDAError("memcpy dev_bufferA to odata failed!"); - timer().endGpuTimer(); + cudaMemcpy(odata, dev_bufferA, sizeof(int) * n, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_bufferA to odata failed!"); + cudaFree(dev_bufferA); cudaFree(dev_bufferB); } @@ -269,6 +268,7 @@ namespace StreamCompaction { checkCUDAError("cudaMalloc dev_bufferBlockSumsB failed!"); int numBlocksForBlockSums = (numBlocks + blockSize - 1) / blockSize; + kernelExtractBlockSums<<>>(n, numBlocks, odata, dev_bufferBlockSumsA); checkCUDAError("kernelExtractBlockSums failed!"); @@ -279,21 +279,18 @@ namespace StreamCompaction { kernelInclusiveToExclusivePrefixSum<<>>(numBlocks, dev_bufferBlockSumsB, dev_bufferBlockSumsA); checkCUDAError("kernelInclusiveToExclusivePrefixSum failed!"); - kernelAddBlockIncrements<<>>(n, dev_bufferBlockSumsA, odata, idata); - checkCUDAError("kernelAddBlockIncrements failed!"); + kernelAddBlockSumsToBlockData<<>>(n, dev_bufferBlockSumsA, odata); + checkCUDAError("kernelAddBlockSumsToBlockData failed!"); } else { kernelNaiveExclusivePrefixSumByBlock<<>>(numBlocks, dev_bufferBlockSumsA, dev_bufferBlockSumsB); checkCUDAError("kernelNaiveExclusivePrefixSumByBlock failed!"); - kernelAddBlockIncrements<<>>(n, dev_bufferBlockSumsB, odata, idata); - checkCUDAError("kernelAddBlockIncrements failed!"); + kernelAddBlockSumsToBlockData<<>>(n, dev_bufferBlockSumsB, odata); + checkCUDAError("kernelAddBlockSumsToBlockData failed!"); } - cudaMemcpy(odata, idata, sizeof(int) * n, cudaMemcpyDeviceToDevice); - checkCUDAError("memcpy idata to odata failed!"); - cudaFree(dev_bufferBlockSumsA); cudaFree(dev_bufferBlockSumsB); } diff --git a/stream_compaction/naive.h b/stream_compaction/naive.h index 41417f5..84fe093 100644 --- a/stream_compaction/naive.h +++ b/stream_compaction/naive.h @@ -16,7 +16,7 @@ namespace StreamCompaction { __global__ void kernelExtractBlockSums(const int n, const int numBlocks, const int* idata, int* odata); - __global__ void kernelAddBlockIncrements(const int n, const int* idataBlockSums, const int* idata, int* odata); + __global__ void kernelAddBlockSumsToBlockData(const int n, const int* idataBlockSums, int* data); void scan(int n, int* odata, const int* idata, bool useSharedMemory); From 3dc17723214b1446032c37c1bfbcd135ec3f70cb Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Sun, 15 Sep 2024 23:55:05 -0400 Subject: [PATCH 06/16] Complete Part 4: Using Thrust's Implementation --- stream_compaction/efficient.cu | 4 ++++ stream_compaction/naive.cu | 3 +++ stream_compaction/thrust.cu | 31 ++++++++++++++++++++++++++++--- 3 files changed, 35 insertions(+), 3 deletions(-) diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 8082d6e..bc3b0b0 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -167,6 +167,7 @@ namespace StreamCompaction { checkCUDAError("memcpy dev_data to odata failed!"); cudaFree(dev_data); + checkCUDAError("cudaFree failed!"); } void efficientExclusivePrefixSumSharedMemory(const bool useGpuTimer, const int n, const int* idata, int* odata) @@ -200,6 +201,7 @@ namespace StreamCompaction { cudaFree(dev_data); cudaFree(dev_sums); + checkCUDAError("cudaFree failed!"); } // iterative approach is possible @@ -224,6 +226,7 @@ namespace StreamCompaction { checkCUDAError("kernelAddBlockSumsToBlockData failed!"); cudaFree(dev_sums); + checkCUDAError("cudaFree failed!"); } } @@ -295,6 +298,7 @@ namespace StreamCompaction { cudaFree(dev_odata); cudaFree(dev_binaryMap); cudaFree(dev_exclusivePrefixSumResult); + checkCUDAError("cudaFree failed!"); return compactedCount; } diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index b75ae7e..1bb348b 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -211,6 +211,7 @@ namespace StreamCompaction { cudaFree(dev_bufferA); cudaFree(dev_bufferB); + checkCUDAError("cudaFree failed!"); } void naiveExclusivePrefixSumSharedMemory(const int n, const int* idata, int* odata) @@ -247,6 +248,7 @@ namespace StreamCompaction { cudaFree(dev_bufferA); cudaFree(dev_bufferB); + checkCUDAError("cudaFree failed!"); } // iterative approach is possible if the blockSums buffers are allocated carefully ahead of time, combined with clever indexing of them at each iteration @@ -293,6 +295,7 @@ namespace StreamCompaction { cudaFree(dev_bufferBlockSumsA); cudaFree(dev_bufferBlockSumsB); + checkCUDAError("cudaFree failed!"); } } } diff --git a/stream_compaction/thrust.cu b/stream_compaction/thrust.cu index 1def45e..5bc857a 100644 --- a/stream_compaction/thrust.cu +++ b/stream_compaction/thrust.cu @@ -18,11 +18,36 @@ namespace StreamCompaction { * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ void scan(int n, int *odata, const int *idata) { + + int* dev_idata; + int* dev_odata; + + cudaMalloc((void**)&dev_idata, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_idata failed!"); + + cudaMalloc((void**)&dev_odata, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_odata failed!"); + + cudaMemcpy(dev_idata, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + checkCUDAError("memcpy idata to dev_idata failed!"); + + thrust::device_ptr dev_thrust_idata(dev_idata); + thrust::device_ptr dev_thrust_odata(dev_odata); + timer().startGpuTimer(); - // TODO use `thrust::exclusive_scan` - // example: for device_vectors dv_in and dv_out: - // thrust::exclusive_scan(dv_in.begin(), dv_in.end(), dv_out.begin()); + + + thrust::exclusive_scan(dev_thrust_idata, dev_thrust_idata + n, dev_thrust_odata); + + timer().endGpuTimer(); + + cudaMemcpy(odata, dev_odata, sizeof(int) * n, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_odata to odata failed!"); + + cudaFree(dev_idata); + cudaFree(dev_odata); + checkCUDAError("cudaFree failed!"); } } } From 57da4c02480bbfe7a36b6b45e8d217fcce16f06f Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Mon, 16 Sep 2024 02:05:07 -0400 Subject: [PATCH 07/16] thrust stream compaction using remove_if --- src/main.cpp | 14 ++++++++++++ stream_compaction/efficient.cu | 12 +++++----- stream_compaction/naive.cu | 22 +++++++++--------- stream_compaction/thrust.cu | 41 ++++++++++++++++++++++++++++++++++ stream_compaction/thrust.h | 2 ++ 5 files changed, 76 insertions(+), 15 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index cee7ca4..7692a43 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -196,6 +196,20 @@ int main(int argc, char* argv[]) { //printArray(count, c, true); printCmpLenResult(count, expectedNPOT, b, c); + zeroArray(SIZE, c); + printDesc("thrust compact, power-of-two"); + count = StreamCompaction::Thrust::compact(SIZE, c, a); + printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + //printArray(count, c, true); + printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + printDesc("thrust compact, non-power-of-two"); + count = StreamCompaction::Thrust::compact(NPOT, c, a); + printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + //printArray(count, c, true); + printCmpLenResult(count, expectedNPOT, b, c); + system("pause"); // stop Win32 console from closing on exit delete[] a; delete[] b; diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index bc3b0b0..96f7c65 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -68,7 +68,7 @@ namespace StreamCompaction { __syncthreads(); } - // Clear the last element + // Write the block sum and then clear it for the Downsweep if (tx == blockSize - 1) { sums[blockIdx.x] = shared[tx]; @@ -267,6 +267,7 @@ namespace StreamCompaction { Common::kernMapToBoolean<<>>(n, dev_binaryMap, dev_idata); + checkCUDAError("kernMapToBoolean failed!"); if (useSharedMemory) { @@ -278,22 +279,23 @@ namespace StreamCompaction { } Common::kernScatter<<>>(n, dev_odata, dev_idata, dev_binaryMap, dev_exclusivePrefixSumResult); + checkCUDAError("kernScatter failed!"); timer().endGpuTimer(); - cudaMemcpy(odata, dev_odata, sizeof(int) * n, cudaMemcpyDeviceToHost); - checkCUDAError("memcpy dev_bufferB to odata failed!"); - int compactedCount; cudaMemcpy(&compactedCount, dev_exclusivePrefixSumResult + (n - 1), sizeof(int), cudaMemcpyDeviceToHost); - checkCUDAError("cudaMemcpy dev_exclusivePrefixSumResult to compactedCount failed!"); + checkCUDAError("cudaMemcpy dev_exclusivePrefixSumResult last element to compactedCount failed!"); if (idata[n - 1] != 0) { ++compactedCount; } + cudaMemcpy(odata, dev_odata, sizeof(int) * compactedCount, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_odata to odata failed!"); + cudaFree(dev_idata); cudaFree(dev_odata); cudaFree(dev_binaryMap); diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 1bb348b..20e240c 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -51,7 +51,8 @@ namespace StreamCompaction { __global__ void kernelNaiveInclusivePrefixSumByBlock(const int n, const int* idata, int* odata) { // allocated on invocation - extern __shared__ int doubleBuffer[]; + // double buffer for ping-ponging + extern __shared__ int shared[]; int g_index = blockIdx.x * blockDim.x + threadIdx.x; if (g_index >= n) @@ -67,7 +68,7 @@ namespace StreamCompaction { // Load input into shared memory // Only need to write to the first half since our first write will be to the second half - doubleBuffer[tx] = idata[g_index]; + shared[tx] = idata[g_index]; __syncthreads(); for (int offset = 1; offset < blockSize; offset *= 2) @@ -78,23 +79,24 @@ namespace StreamCompaction { if (tx >= offset) { - doubleBuffer[writeBuffer * blockSize + tx] = doubleBuffer[readBuffer * blockSize + tx - offset] + doubleBuffer[readBuffer * blockSize + tx]; + shared[writeBuffer * blockSize + tx] = shared[readBuffer * blockSize + tx - offset] + shared[readBuffer * blockSize + tx]; } else { - doubleBuffer[writeBuffer * blockSize + tx] = doubleBuffer[readBuffer * blockSize + tx]; + shared[writeBuffer * blockSize + tx] = shared[readBuffer * blockSize + tx]; } __syncthreads(); } // write output - odata[g_index] = doubleBuffer[writeBuffer * blockSize + tx]; + odata[g_index] = shared[writeBuffer * blockSize + tx]; } __global__ void kernelNaiveExclusivePrefixSumByBlock(const int n, const int* idata, int* odata) { // allocated on invocation - extern __shared__ int doubleBuffer[]; + // double buffer for ping-ponging + extern __shared__ int shared[]; int g_index = blockIdx.x * blockDim.x + threadIdx.x; if (g_index >= n) @@ -111,7 +113,7 @@ namespace StreamCompaction { // Load input into shared memory // Exclusive scan - shift all elements right by one and set first element to 0 // Only need to write to the first half since our first write will be to the second half - doubleBuffer[tx] = (tx > 0) ? idata[g_index - 1] : 0; + shared[tx] = (tx > 0) ? idata[g_index - 1] : 0; __syncthreads(); for (int offset = 1; offset < blockSize; offset *= 2) @@ -122,17 +124,17 @@ namespace StreamCompaction { if (tx >= offset) { - doubleBuffer[writeBuffer * blockSize + tx] = doubleBuffer[readBuffer * blockSize + tx - offset] + doubleBuffer[readBuffer * blockSize + tx]; + shared[writeBuffer * blockSize + tx] = shared[readBuffer * blockSize + tx - offset] + shared[readBuffer * blockSize + tx]; } else { - doubleBuffer[writeBuffer * blockSize + tx] = doubleBuffer[readBuffer * blockSize + tx]; + shared[writeBuffer * blockSize + tx] = shared[readBuffer * blockSize + tx]; } __syncthreads(); } // write output - odata[g_index] = doubleBuffer[writeBuffer * blockSize + tx]; + odata[g_index] = shared[writeBuffer * blockSize + tx]; } __global__ void kernelExtractBlockSums(const int n, const int numBlocks, const int* idata, int* odata) diff --git a/stream_compaction/thrust.cu b/stream_compaction/thrust.cu index 5bc857a..f500fc0 100644 --- a/stream_compaction/thrust.cu +++ b/stream_compaction/thrust.cu @@ -3,9 +3,17 @@ #include #include #include +#include #include "common.h" #include "thrust.h" +struct is_zero +{ + __device__ bool operator()(const int& x) const { + return x == 0; + } +}; + namespace StreamCompaction { namespace Thrust { using StreamCompaction::Common::PerformanceTimer; @@ -49,5 +57,38 @@ namespace StreamCompaction { cudaFree(dev_odata); checkCUDAError("cudaFree failed!"); } + + int compact(int n, int* odata, const int* idata) + { + int* dev_data; + + cudaMalloc((void**)&dev_data, sizeof(int) * n); + checkCUDAError("cudaMalloc dev_data failed!"); + + cudaMemcpy(dev_data, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + checkCUDAError("memcpy idata to dev_data failed!"); + + thrust::device_ptr dev_thrust_data(dev_data); + + timer().startGpuTimer(); + + + thrust::device_ptr new_end = thrust::remove_if(dev_thrust_data, dev_thrust_data + n, is_zero()); + + + timer().endGpuTimer(); + + // Calculate the number of elements remaining after removal + int compactedCount = new_end - dev_thrust_data; + + cudaMemcpy(odata, dev_data, sizeof(int) * compactedCount, cudaMemcpyDeviceToHost); + checkCUDAError("memcpy dev_data to odata failed!"); + + // Free device memory + cudaFree(dev_data); + checkCUDAError("cudaFree failed!"); + + return compactedCount; + } } } diff --git a/stream_compaction/thrust.h b/stream_compaction/thrust.h index fe98206..d5c82ec 100644 --- a/stream_compaction/thrust.h +++ b/stream_compaction/thrust.h @@ -7,5 +7,7 @@ namespace StreamCompaction { StreamCompaction::Common::PerformanceTimer& timer(); void scan(int n, int *odata, const int *idata); + + int compact(int n, int* odata, const int* idata); } } From 30991c72893e1a1caad519bda6edaa603b7d6d35 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Mon, 16 Sep 2024 02:22:24 -0400 Subject: [PATCH 08/16] replacing dividing by 2 with bitwise shifts --- stream_compaction/efficient.cu | 4 ++-- stream_compaction/naive.cu | 16 ++++++++-------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 96f7c65..26fa16c 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -77,7 +77,7 @@ namespace StreamCompaction { __syncthreads(); // Downsweep phase - for (int offset = blockSize / 2; offset > 0; offset /= 2) + for (int offset = blockSize >> 1; offset > 0; offset >>= 1) { if ((tx + 1) % (2 * offset) == 0) { @@ -154,7 +154,7 @@ namespace StreamCompaction { cudaMemset(dev_data + bufferLength - 1, 0, sizeof(int)); checkCUDAError("cudaMemset last element in dev_data to 0 failed!"); - for (int offset = bufferLength / 2; offset > 0; offset /= 2) + for (int offset = bufferLength >> 1; offset > 0; offset >>= 1) { kernelDownSweepPass<<>>(bufferLength, offset, dev_data); checkCUDAError("kernelDownSweepPass failed!"); diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 20e240c..3c4a5ac 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -62,15 +62,15 @@ namespace StreamCompaction { int tx = threadIdx.x; - // identify which half of double buffer is read-half and write-half - int writeBuffer = 0; - int readBuffer = 1; - // Load input into shared memory // Only need to write to the first half since our first write will be to the second half shared[tx] = idata[g_index]; __syncthreads(); + // identify which half of double buffer is read-half and write-half + int writeBuffer = 0; + int readBuffer = 1; + for (int offset = 1; offset < blockSize; offset *= 2) { // swap double buffer indices @@ -106,16 +106,16 @@ namespace StreamCompaction { int tx = threadIdx.x; - // identify which half of double buffer is read-half and write-half - int writeBuffer = 0; - int readBuffer = 1; - // Load input into shared memory // Exclusive scan - shift all elements right by one and set first element to 0 // Only need to write to the first half since our first write will be to the second half shared[tx] = (tx > 0) ? idata[g_index - 1] : 0; __syncthreads(); + // identify which half of double buffer is read-half and write-half + int writeBuffer = 0; + int readBuffer = 1; + for (int offset = 1; offset < blockSize; offset *= 2) { // swap double buffer indices From 774513891ae8fab137a689da29e4fe59302e9843 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Tue, 17 Sep 2024 22:53:31 -0400 Subject: [PATCH 09/16] optimizations for work-efficient scan, no shared memory and shared memory --- stream_compaction/efficient.cu | 182 ++++++++++++++++++++++----------- stream_compaction/efficient.h | 10 +- stream_compaction/naive.cu | 44 ++++---- stream_compaction/naive.h | 6 +- 4 files changed, 151 insertions(+), 91 deletions(-) diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 26fa16c..01907c0 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -12,98 +12,117 @@ namespace StreamCompaction { return timer; } - __global__ void kernelReductionPass(const int bufferLength, const int offset, int* data) + __global__ void kernelReductionPass(const int reqThdsForPass, const int offset, int* data) { int g_index = (blockIdx.x * blockDim.x) + threadIdx.x; - // index out of bounds or not a rhs child node - if ((g_index >= bufferLength) || ((g_index + 1) % (2 * offset) != 0)) + if (g_index >= reqThdsForPass) { return; } - data[g_index] += data[g_index - offset]; + int left_node_g_index = offset * (2 * g_index + 1) - 1; + int right_node_g_index = offset * (2 * g_index + 2) - 1; + + data[right_node_g_index] += data[left_node_g_index]; } - __global__ void kernelDownSweepPass(const int bufferLength, const int offset, int* data) + __global__ void kernelDownSweepPass(const int reqThdsForPass, const int offset, int* data) { int g_index = (blockIdx.x * blockDim.x) + threadIdx.x; - // index out of bounds or not a rhs child node - if ((g_index >= bufferLength) || ((g_index + 1) % (2 * offset) != 0)) + if (g_index >= reqThdsForPass) { return; } - int tmp = data[g_index]; - data[g_index] += data[g_index - offset]; - data[g_index - offset] = tmp; + int left_node_g_index = offset * (2 * g_index + 1) - 1; + int right_node_g_index = offset * (2 * g_index + 2) - 1; + + int temp = data[left_node_g_index]; + data[left_node_g_index] = data[right_node_g_index]; + data[right_node_g_index] += temp; } - __global__ void kernelEfficientExclusivePrefixSumByBlock(const int n, int* data, int* sums) + __global__ void kernelEfficientExclusivePrefixSumByBlock(const int reqThdsPerBlock, int* data, int* blockSums) { + // allocated on invocation + // 2x block size extern __shared__ int shared[]; int tx = threadIdx.x; - int g_index = blockIdx.x * blockDim.x + tx; - // Load input into shared memory - if (g_index < n) + if (tx >= reqThdsPerBlock) { - shared[tx] = data[g_index]; - } - else - { - shared[tx] = 0; + return; } - __syncthreads(); + + int g_index = 2 * ((blockIdx.x * blockDim.x) + tx); + + // Load input into shared memory + shared[2 * tx] = data[g_index]; + shared[2 * tx + 1] = data[g_index + 1]; + + int offset = 1; // Upsweep (Reduction) phase - for (int offset = 1; offset < blockSize; offset *= 2) + for (int reqThdsForPass = reqThdsPerBlock; reqThdsForPass > 0; reqThdsForPass >>= 1) { - if ((tx + 1) % (2 * offset) == 0) + __syncthreads(); + + if (tx < reqThdsForPass) { - shared[tx] += shared[tx - offset]; + int left_node_shrd_index = offset * (2 * tx + 1) - 1; + int right_node_shrd_index = offset * (2 * tx + 2) - 1; + + shared[right_node_shrd_index] += shared[left_node_shrd_index]; } - __syncthreads(); + + offset *= 2; } // Write the block sum and then clear it for the Downsweep - if (tx == blockSize - 1) + if (tx == 0) { - sums[blockIdx.x] = shared[tx]; - shared[tx] = 0; + blockSums[blockIdx.x] = shared[2 * reqThdsPerBlock - 1]; + shared[2 * reqThdsPerBlock - 1] = 0; } - __syncthreads(); // Downsweep phase - for (int offset = blockSize >> 1; offset > 0; offset >>= 1) + for (int reqThdsForPass = 1; reqThdsForPass <= reqThdsPerBlock; reqThdsForPass *= 2) { - if ((tx + 1) % (2 * offset) == 0) + offset >>= 1; + __syncthreads(); + + if (tx < reqThdsForPass) { - int temp = shared[tx]; - shared[tx] += shared[tx - offset]; - shared[tx - offset] = temp; + int left_node_shrd_index = offset * (2 * tx + 1) - 1; + int right_node_shrd_index = offset * (2 * tx + 2) - 1; + + int temp = shared[left_node_shrd_index]; + shared[left_node_shrd_index] = shared[right_node_shrd_index]; + shared[right_node_shrd_index] += temp; } - __syncthreads(); } + __syncthreads(); + // Write the results back to global memory - if (g_index < n) - { - data[g_index] = shared[tx]; - } + data[g_index] = shared[2 * tx]; + data[g_index + 1] = shared[2 * tx + 1]; } - __global__ void kernelAddBlockSumsToBlockData(const int n, const int* idataBlockSums, int* data) + __global__ void kernelAddBlockSumsToBlockData(const int* blockSums, int* data) { - int g_index = blockIdx.x * blockDim.x + threadIdx.x; - if (g_index >= n) - { - return; - } + int g_index = 2 * ((blockIdx.x * blockDim.x) + threadIdx.x); - data[g_index] += idataBlockSums[blockIdx.x]; + // ***** blockSize must be a power of 2 ***** // + // no reason to check bounds because this kernel is only called if we recursively scan, + // meaning the buffer length (of int* data) is going to be (2 * (blockSize)) * (2 to the power of n), + // where n is the depth of recursion. To reiterate the first line, n is always >= 1 + + data[g_index] += blockSums[blockIdx.x]; + data[g_index + 1] += blockSums[blockIdx.x]; } /** @@ -140,23 +159,30 @@ namespace StreamCompaction { checkCUDAError("cudaMemset elements n to bufferLength - 1 in dev_data to 0 failed!"); } - int blocksPerGrid = (bufferLength + blockSize - 1) / blockSize; + int blocksPerGrid = 0; + int offset = 1; if (useGpuTimer) timer().startGpuTimer(); - for (int offset = 1; offset < bufferLength; offset *= 2) + for (int reqThdsForPass = bufferLength >> 1; reqThdsForPass > 0; reqThdsForPass >>= 1) { - kernelReductionPass<<>>(bufferLength, offset, dev_data); + blocksPerGrid = (reqThdsForPass + blockSize - 1) / blockSize; + kernelReductionPass<<>>(reqThdsForPass, offset, dev_data); checkCUDAError("kernelReductionPass failed!"); + + offset *= 2; } cudaMemset(dev_data + bufferLength - 1, 0, sizeof(int)); checkCUDAError("cudaMemset last element in dev_data to 0 failed!"); - for (int offset = bufferLength >> 1; offset > 0; offset >>= 1) + for (int reqThdsForPass = 1; reqThdsForPass < bufferLength; reqThdsForPass *= 2) { - kernelDownSweepPass<<>>(bufferLength, offset, dev_data); + offset >>= 1; + + blocksPerGrid = (reqThdsForPass + blockSize - 1) / blockSize; + kernelDownSweepPass<<>>(reqThdsForPass, offset, dev_data); checkCUDAError("kernelDownSweepPass failed!"); } @@ -172,12 +198,15 @@ namespace StreamCompaction { void efficientExclusivePrefixSumSharedMemory(const bool useGpuTimer, const int n, const int* idata, int* odata) { - int blocksPerGrid = (n + blockSize - 1) / blockSize; + int totalPasses = ilog2ceil(n); + int bufferLength = 1 << totalPasses; + int reqThdsTotal = bufferLength >> 1; + int blocksPerGrid = (reqThdsTotal + blockSize - 1) / blockSize; int* dev_data; int* dev_sums; - cudaMalloc((void**)&dev_data, sizeof(int) * n); + cudaMalloc((void**)&dev_data, sizeof(int) * bufferLength); checkCUDAError("cudaMalloc dev_data failed!"); cudaMalloc((void**)&dev_sums, sizeof(int) * blocksPerGrid); @@ -186,12 +215,19 @@ namespace StreamCompaction { cudaMemcpy(dev_data, idata, sizeof(int) * n, cudaMemcpyHostToDevice); checkCUDAError("memcpy idata to dev_data failed!"); - int sharedMemoryBytes = blockSize * sizeof(int); + if (bufferLength > n) + { + cudaMemset(dev_data + n, 0, sizeof(int) * (bufferLength - n)); + checkCUDAError("cudaMemset elements n to bufferLength - 1 in dev_data to 0 failed!"); + } - if (useGpuTimer) timer().startGpuTimer(); + int reqThdsPerBlock = std::min(reqThdsTotal, blockSize); + int sharedMemoryBytes = 2 * reqThdsPerBlock * sizeof(int); + if (useGpuTimer) timer().startGpuTimer(); - efficientExclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, n, blocksPerGrid, dev_data, dev_sums); + + efficientExclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, reqThdsPerBlock, blocksPerGrid, dev_data, dev_sums); if (useGpuTimer) timer().endGpuTimer(); @@ -206,26 +242,48 @@ namespace StreamCompaction { // iterative approach is possible // for the sake of submitting this assignement on time, this will have to be explored at a later time - void efficientExclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int numBlocks, int* data, int* sums) + void efficientExclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int reqThdsPerBlock, const int blocksPerGrid, int* data, int* blockSums) { - kernelEfficientExclusivePrefixSumByBlock<<>>(n, data, sums); + kernelEfficientExclusivePrefixSumByBlock<<>>(reqThdsPerBlock, data, blockSums); checkCUDAError("kernelEfficientExclusivePrefixSumByBlock failed!"); - if (numBlocks > 1) + if (blocksPerGrid > 1) { - int numBlocksForBlockSums = (numBlocks + blockSize - 1) / blockSize; + int n_blockSums = blocksPerGrid; + + int totalPasses_blockSums = ilog2ceil(n_blockSums); + int bufferLength_blockSums = 1 << totalPasses_blockSums; + int reqThdsTotal_blockSums = bufferLength_blockSums >> 1; + int blocksPerGrid_blockSums = (reqThdsTotal_blockSums + blockSize - 1) / blockSize; int* dev_sums; + int* dev_new_sums; - cudaMalloc((void**)&dev_sums, sizeof(int) * numBlocksForBlockSums); + cudaMalloc((void**)&dev_sums, sizeof(int) * bufferLength_blockSums); checkCUDAError("cudaMalloc dev_sums failed!"); - efficientExclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, numBlocks, numBlocksForBlockSums, sums, dev_sums); + cudaMalloc((void**)&dev_new_sums, sizeof(int) * blocksPerGrid_blockSums); + checkCUDAError("cudaMalloc dev_new_sums failed!"); + + cudaMemcpy(dev_sums, blockSums, sizeof(int) * n_blockSums, cudaMemcpyDeviceToDevice); + checkCUDAError("memcpy blockSums to dev_sums failed!"); + + if (bufferLength_blockSums > n_blockSums) + { + cudaMemset(dev_sums + n_blockSums, 0, sizeof(int) * (bufferLength_blockSums - n_blockSums)); + checkCUDAError("cudaMemset elements n_blockSums to bufferLength_blockSums - 1 in dev_sums to 0 failed!"); + } + + int reqThdsPerBlock_blockSums = std::min(reqThdsTotal_blockSums, blockSize); + int sharedMemoryBytes_blockSums = 2 * reqThdsPerBlock_blockSums * sizeof(int); + + efficientExclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes_blockSums, reqThdsPerBlock_blockSums, blocksPerGrid_blockSums, dev_sums, dev_new_sums); - kernelAddBlockSumsToBlockData<<>>(n, sums, data); + kernelAddBlockSumsToBlockData<<>>(dev_sums, data); checkCUDAError("kernelAddBlockSumsToBlockData failed!"); cudaFree(dev_sums); + cudaFree(dev_new_sums); checkCUDAError("cudaFree failed!"); } } diff --git a/stream_compaction/efficient.h b/stream_compaction/efficient.h index 089c5e0..0de9d06 100644 --- a/stream_compaction/efficient.h +++ b/stream_compaction/efficient.h @@ -6,13 +6,13 @@ namespace StreamCompaction { namespace Efficient { StreamCompaction::Common::PerformanceTimer& timer(); - __global__ void kernelReductionPass(const int bufferLength, const int offset, int* data); + __global__ void kernelReductionPass(const int reqThdsForPass, const int offset, int* data); - __global__ void kernelDownSweepPass(const int bufferLength, const int offset, int* data); + __global__ void kernelDownSweepPass(const int reqThdsForPass, const int offset, int* data); - __global__ void kernelEfficientExclusivePrefixSumByBlock(const int n, int* data, int* sums); + __global__ void kernelEfficientExclusivePrefixSumByBlock(const int reqThdsPerBlock, int* data, int* blockSums); - __global__ void kernelAddBlockSumsToBlockData(const int n, const int* idataBlockSums, int* data); + __global__ void kernelAddBlockSumsToBlockData(const int* blockSums, int* data); void scan(int n, int* odata, const int* idata, bool useSharedMemory); @@ -20,7 +20,7 @@ namespace StreamCompaction { void efficientExclusivePrefixSumSharedMemory(const bool useGpuTimer, const int n, const int* idata, int* odata); - void efficientExclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int numBlocks, int* data, int* sums); + void efficientExclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int reqThdsPerBlock, const int blocksPerGrid, int* data, int* blockSums); int compact(int n, int* odata, const int* idata, bool useSharedMemory); } diff --git a/stream_compaction/naive.cu b/stream_compaction/naive.cu index 3c4a5ac..2331673 100644 --- a/stream_compaction/naive.cu +++ b/stream_compaction/naive.cu @@ -40,7 +40,7 @@ namespace StreamCompaction { if (g_index >= offset) { - odata[g_index] = idata[g_index - offset] + idata[g_index]; + odata[g_index] = idata[g_index] + idata[g_index - offset]; } else { @@ -79,7 +79,7 @@ namespace StreamCompaction { if (tx >= offset) { - shared[writeBuffer * blockSize + tx] = shared[readBuffer * blockSize + tx - offset] + shared[readBuffer * blockSize + tx]; + shared[writeBuffer * blockSize + tx] = shared[readBuffer * blockSize + tx] + shared[readBuffer * blockSize + tx - offset]; } else { @@ -124,7 +124,7 @@ namespace StreamCompaction { if (tx >= offset) { - shared[writeBuffer * blockSize + tx] = shared[readBuffer * blockSize + tx - offset] + shared[readBuffer * blockSize + tx]; + shared[writeBuffer * blockSize + tx] = shared[readBuffer * blockSize + tx] + shared[readBuffer * blockSize + tx - offset]; } else { @@ -137,18 +137,18 @@ namespace StreamCompaction { odata[g_index] = shared[writeBuffer * blockSize + tx]; } - __global__ void kernelExtractBlockSums(const int n, const int numBlocks, const int* idata, int* odata) + __global__ void kernelExtractBlockSums(const int n, const int n_blockSums, const int* idata, int* odata) { int g_index = blockIdx.x * blockDim.x + threadIdx.x; - if (g_index >= numBlocks) + if (g_index >= n_blockSums) { return; } - odata[g_index] = g_index == numBlocks - 1 ? idata[n - 1] : idata[(g_index * blockSize) + blockSize - 1]; + odata[g_index] = g_index == n_blockSums - 1 ? idata[n - 1] : idata[(g_index * blockSize) + blockSize - 1]; } - __global__ void kernelAddBlockSumsToBlockData(const int n, const int* idataBlockSums, int* data) + __global__ void kernelAddBlockSumsToBlockData(const int n, const int* blockSums, int* data) { int g_index = blockIdx.x * blockDim.x + threadIdx.x; if (g_index >= n) @@ -156,7 +156,7 @@ namespace StreamCompaction { return; } - data[g_index] += idataBlockSums[blockIdx.x]; + data[g_index] += blockSums[blockIdx.x]; } /** @@ -255,43 +255,45 @@ namespace StreamCompaction { // iterative approach is possible if the blockSums buffers are allocated carefully ahead of time, combined with clever indexing of them at each iteration // for the sake of submitting this assignement on time, this will have to be explored at a later time - void naiveInclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int numBlocks, int* idata, int* odata) + void naiveInclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int blocksPerGrid, int* idata, int* odata) { - kernelNaiveInclusivePrefixSumByBlock<<>>(n, idata, odata); + kernelNaiveInclusivePrefixSumByBlock<<>>(n, idata, odata); checkCUDAError("kernelNaiveInclusivePrefixSumByBlock failed!"); - if (numBlocks > 1) + if (blocksPerGrid > 1) { + int n_blockSums = blocksPerGrid; + int* dev_bufferBlockSumsA; int* dev_bufferBlockSumsB; - cudaMalloc((void**)&dev_bufferBlockSumsA, sizeof(int) * numBlocks); + cudaMalloc((void**)&dev_bufferBlockSumsA, sizeof(int) * n_blockSums); checkCUDAError("cudaMalloc dev_bufferBlockSumsA failed!"); - cudaMalloc((void**)&dev_bufferBlockSumsB, sizeof(int) * numBlocks); + cudaMalloc((void**)&dev_bufferBlockSumsB, sizeof(int) * n_blockSums); checkCUDAError("cudaMalloc dev_bufferBlockSumsB failed!"); - int numBlocksForBlockSums = (numBlocks + blockSize - 1) / blockSize; + int blocksPerGrid_blockSums = (n_blockSums + blockSize - 1) / blockSize; - kernelExtractBlockSums<<>>(n, numBlocks, odata, dev_bufferBlockSumsA); + kernelExtractBlockSums<<>>(n, n_blockSums, odata, dev_bufferBlockSumsA); checkCUDAError("kernelExtractBlockSums failed!"); - if (numBlocksForBlockSums > 1) + if (blocksPerGrid_blockSums > 1) { - naiveInclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, numBlocks, numBlocksForBlockSums, dev_bufferBlockSumsA, dev_bufferBlockSumsB); + naiveInclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes, n_blockSums, blocksPerGrid_blockSums, dev_bufferBlockSumsA, dev_bufferBlockSumsB); - kernelInclusiveToExclusivePrefixSum<<>>(numBlocks, dev_bufferBlockSumsB, dev_bufferBlockSumsA); + kernelInclusiveToExclusivePrefixSum<<>>(n_blockSums, dev_bufferBlockSumsB, dev_bufferBlockSumsA); checkCUDAError("kernelInclusiveToExclusivePrefixSum failed!"); - kernelAddBlockSumsToBlockData<<>>(n, dev_bufferBlockSumsA, odata); + kernelAddBlockSumsToBlockData<<>>(n, dev_bufferBlockSumsA, odata); checkCUDAError("kernelAddBlockSumsToBlockData failed!"); } else { - kernelNaiveExclusivePrefixSumByBlock<<>>(numBlocks, dev_bufferBlockSumsA, dev_bufferBlockSumsB); + kernelNaiveExclusivePrefixSumByBlock<<>>(n_blockSums, dev_bufferBlockSumsA, dev_bufferBlockSumsB); checkCUDAError("kernelNaiveExclusivePrefixSumByBlock failed!"); - kernelAddBlockSumsToBlockData<<>>(n, dev_bufferBlockSumsB, odata); + kernelAddBlockSumsToBlockData<<>>(n, dev_bufferBlockSumsB, odata); checkCUDAError("kernelAddBlockSumsToBlockData failed!"); } diff --git a/stream_compaction/naive.h b/stream_compaction/naive.h index 84fe093..c447ce2 100644 --- a/stream_compaction/naive.h +++ b/stream_compaction/naive.h @@ -14,9 +14,9 @@ namespace StreamCompaction { __global__ void kernelNaiveExclusivePrefixSumByBlock(const int n, const int* idata, int* odata); - __global__ void kernelExtractBlockSums(const int n, const int numBlocks, const int* idata, int* odata); + __global__ void kernelExtractBlockSums(const int n, const int n_blockSums, const int* idata, int* odata); - __global__ void kernelAddBlockSumsToBlockData(const int n, const int* idataBlockSums, int* data); + __global__ void kernelAddBlockSumsToBlockData(const int n, const int* blockSums, int* data); void scan(int n, int* odata, const int* idata, bool useSharedMemory); @@ -24,6 +24,6 @@ namespace StreamCompaction { void naiveExclusivePrefixSumSharedMemory(const int n, const int* idata, int* odata); - void naiveInclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int numBlocks, int* idata, int* odata); + void naiveInclusivePrefixSumAnyNumberOfBlocks(const int sharedMemoryBytes, const int n, const int blocksPerGrid, int* idata, int* odata); } } From 511b81d836e86878df8b7b83e4675a7a5b774baf Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Tue, 17 Sep 2024 23:46:12 -0400 Subject: [PATCH 10/16] eliminate bank conflicts from work-efficient scan shared mem impl --- stream_compaction/efficient.cu | 44 +++++++++++++++++++++++++++------- 1 file changed, 35 insertions(+), 9 deletions(-) diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 01907c0..2e0808e 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -3,6 +3,15 @@ #include "common.h" #include "efficient.h" +#define NUM_BANKS 32 +#define LOG_NUM_BANKS 5 +#ifdef ZERO_BANK_CONFLICTS +#define CONFLICT_FREE_OFFSET(n) \ + ((n) >> (LOG_NUM_BANKS) + (n) >> (2 * LOG_NUM_BANKS)) +#else +#define CONFLICT_FREE_OFFSET(n) ((n) >> LOG_NUM_BANKS) +#endif + namespace StreamCompaction { namespace Efficient { using StreamCompaction::Common::PerformanceTimer; @@ -57,11 +66,19 @@ namespace StreamCompaction { return; } - int g_index = 2 * ((blockIdx.x * blockDim.x) + tx); + int g_index = 2 * (blockIdx.x * blockDim.x) + tx; + + int ai = tx; + int bi = tx + reqThdsPerBlock; + int g_ai = g_index; + int g_bi = g_index + reqThdsPerBlock; + + int bankOffsetA = CONFLICT_FREE_OFFSET(ai); + int bankOffsetB = CONFLICT_FREE_OFFSET(bi); // Load input into shared memory - shared[2 * tx] = data[g_index]; - shared[2 * tx + 1] = data[g_index + 1]; + shared[ai + bankOffsetA] = data[g_ai]; + shared[bi + bankOffsetB] = data[g_bi]; int offset = 1; @@ -74,6 +91,8 @@ namespace StreamCompaction { { int left_node_shrd_index = offset * (2 * tx + 1) - 1; int right_node_shrd_index = offset * (2 * tx + 2) - 1; + left_node_shrd_index += CONFLICT_FREE_OFFSET(left_node_shrd_index); + right_node_shrd_index += CONFLICT_FREE_OFFSET(right_node_shrd_index); shared[right_node_shrd_index] += shared[left_node_shrd_index]; } @@ -84,8 +103,11 @@ namespace StreamCompaction { // Write the block sum and then clear it for the Downsweep if (tx == 0) { - blockSums[blockIdx.x] = shared[2 * reqThdsPerBlock - 1]; - shared[2 * reqThdsPerBlock - 1] = 0; + int n_minus_1 = 2 * reqThdsPerBlock - 1; + n_minus_1 += CONFLICT_FREE_OFFSET(n_minus_1); + + blockSums[blockIdx.x] = shared[n_minus_1]; + shared[n_minus_1] = 0; } // Downsweep phase @@ -98,6 +120,8 @@ namespace StreamCompaction { { int left_node_shrd_index = offset * (2 * tx + 1) - 1; int right_node_shrd_index = offset * (2 * tx + 2) - 1; + left_node_shrd_index += CONFLICT_FREE_OFFSET(left_node_shrd_index); + right_node_shrd_index += CONFLICT_FREE_OFFSET(right_node_shrd_index); int temp = shared[left_node_shrd_index]; shared[left_node_shrd_index] = shared[right_node_shrd_index]; @@ -108,8 +132,8 @@ namespace StreamCompaction { __syncthreads(); // Write the results back to global memory - data[g_index] = shared[2 * tx]; - data[g_index + 1] = shared[2 * tx + 1]; + data[g_ai] = shared[ai + bankOffsetA]; + data[g_bi] = shared[bi + bankOffsetB]; } __global__ void kernelAddBlockSumsToBlockData(const int* blockSums, int* data) @@ -222,7 +246,8 @@ namespace StreamCompaction { } int reqThdsPerBlock = std::min(reqThdsTotal, blockSize); - int sharedMemoryBytes = 2 * reqThdsPerBlock * sizeof(int); + int procElemsPerBlock = 2 * reqThdsPerBlock; + int sharedMemoryBytes = sizeof(int) * (procElemsPerBlock + (procElemsPerBlock / NUM_BANKS) + (procElemsPerBlock / (NUM_BANKS * NUM_BANKS))); if (useGpuTimer) timer().startGpuTimer(); @@ -275,7 +300,8 @@ namespace StreamCompaction { } int reqThdsPerBlock_blockSums = std::min(reqThdsTotal_blockSums, blockSize); - int sharedMemoryBytes_blockSums = 2 * reqThdsPerBlock_blockSums * sizeof(int); + int procElemsPerBlock_blockSums = 2 * reqThdsPerBlock_blockSums; + int sharedMemoryBytes_blockSums = sizeof(int) * (procElemsPerBlock_blockSums + (procElemsPerBlock_blockSums / NUM_BANKS) + (procElemsPerBlock_blockSums / (NUM_BANKS * NUM_BANKS))); efficientExclusivePrefixSumAnyNumberOfBlocks(sharedMemoryBytes_blockSums, reqThdsPerBlock_blockSums, blocksPerGrid_blockSums, dev_sums, dev_new_sums); From e67b65ae81a93011d2affe36e077e6872058c002 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Wed, 18 Sep 2024 03:20:18 -0400 Subject: [PATCH 11/16] removing an outdated comment --- stream_compaction/efficient.cu | 1 - 1 file changed, 1 deletion(-) diff --git a/stream_compaction/efficient.cu b/stream_compaction/efficient.cu index 2e0808e..ccb33c5 100644 --- a/stream_compaction/efficient.cu +++ b/stream_compaction/efficient.cu @@ -56,7 +56,6 @@ namespace StreamCompaction { __global__ void kernelEfficientExclusivePrefixSumByBlock(const int reqThdsPerBlock, int* data, int* blockSums) { // allocated on invocation - // 2x block size extern __shared__ int shared[]; int tx = threadIdx.x; From 422ea510e18945d99fc83a12f4067cb96f7c7006 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Wed, 18 Sep 2024 19:40:29 -0400 Subject: [PATCH 12/16] modified test code - for each test, run it N times, store the N runtime results, and display the average of the runtimes --- src/main.cpp | 376 +++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 289 insertions(+), 87 deletions(-) diff --git a/src/main.cpp b/src/main.cpp index 7692a43..ec5a484 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -13,12 +13,33 @@ #include #include "testing_helpers.hpp" +const bool runXNumTestsAndGetAverage = false; +const int numTests = 10; +int testCount = 0; +float testResults[numTests]; + const int SIZE = 1 << 8; // feel free to change the size of array const int NPOT = SIZE - 3; // Non-Power-Of-Two int *a = new int[SIZE]; int *b = new int[SIZE]; int *c = new int[SIZE]; +void printAverageOfTestsAndResetTestCount(bool ranOnGPU) +{ + float average = 0.f; + for (int i = 0; i < numTests; ++i) + { + average += testResults[i]; + } + average /= numTests; + + std::cout << "Average Runtime of " << numTests << " runs: "; + printElapsedTime(average, ranOnGPU ? "(CUDA Measured)" : "(std::chrono Measured)"); + std::cout << std::endl; + + testCount = 0; +} + int main(int argc, char* argv[]) { // Scan tests @@ -34,94 +55,275 @@ int main(int argc, char* argv[]) { // initialize b using StreamCompaction::CPU::scan you implement // We use b for further comparison. Make sure your StreamCompaction::CPU::scan is correct. // At first all cases passed because b && c are all zeroes. - zeroArray(SIZE, b); - printDesc("cpu scan, power-of-two"); - StreamCompaction::CPU::scan(SIZE, b, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(SIZE, b, true); - - zeroArray(SIZE, c); - printDesc("cpu scan, non-power-of-two"); - StreamCompaction::CPU::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(NPOT, b, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("naive scan, power-of-two, no shared memory"); - StreamCompaction::Naive::scan(SIZE, c, a, false); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - /* For bug-finding only: Array of 1s to help find bugs in stream compaction or scan - onesArray(SIZE, c); - printDesc("1s array for finding bugs"); - StreamCompaction::Naive::scan(SIZE, c, a); - printArray(SIZE, c, true); */ - - zeroArray(SIZE, c); - printDesc("naive scan, non-power-of-two, no shared memory"); - StreamCompaction::Naive::scan(NPOT, c, a, false); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("naive scan, power-of-two, shared memory"); - StreamCompaction::Naive::scan(SIZE, c, a, true); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("naive scan, non-power-of-two, shared memory"); - StreamCompaction::Naive::scan(NPOT, c, a, true); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(NPOT, b, c); - zeroArray(SIZE, c); - printDesc("work-efficient scan, power-of-two, no shared memory"); - StreamCompaction::Efficient::scan(SIZE, c, a, false); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient scan, non-power-of-two, no shared memory"); - StreamCompaction::Efficient::scan(NPOT, c, a, false); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(NPOT, c, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient scan, power-of-two, shared memory"); - StreamCompaction::Efficient::scan(SIZE, c, a, true); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient scan, non-power-of-two, shared memory"); - StreamCompaction::Efficient::scan(NPOT, c, a, true); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(NPOT, c, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("thrust scan, power-of-two"); - StreamCompaction::Thrust::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("thrust scan, non-power-of-two"); - StreamCompaction::Thrust::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(NPOT, c, true); - printCmpResult(NPOT, b, c); + do + { + zeroArray(SIZE, b); + if (testCount == 0) + { + printDesc("cpu scan, power-of-two"); + } + StreamCompaction::CPU::scan(SIZE, b, a); + testResults[testCount] = StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + printArray(SIZE, b, true); + } + printElapsedTime(testResults[testCount], "(std::chrono Measured)"); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(false); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("cpu scan, non-power-of-two"); + } + StreamCompaction::CPU::scan(NPOT, c, a); + testResults[testCount] = StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + printArray(NPOT, c, true); + } + printElapsedTime(testResults[testCount], "(std::chrono Measured)"); + printCmpResult(NPOT, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(false); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("naive scan, power-of-two, no shared memory"); + } + StreamCompaction::Naive::scan(SIZE, c, a, false); + testResults[testCount] = StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(SIZE, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(SIZE, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } + + /* For bug-finding only: Array of 1s to help find bugs in stream compaction or scan + onesArray(SIZE, c); + printDesc("1s array for finding bugs"); + StreamCompaction::Naive::scan(SIZE, c, a); + printArray(SIZE, c, true); */ + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("naive scan, non-power-of-two, no shared memory"); + } + StreamCompaction::Naive::scan(NPOT, c, a, false); + testResults[testCount] = StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(NPOT, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(NPOT, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("naive scan, power-of-two, shared memory"); + } + StreamCompaction::Naive::scan(SIZE, c, a, true); + testResults[testCount] = StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(SIZE, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(SIZE, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("naive scan, non-power-of-two, shared memory"); + } + StreamCompaction::Naive::scan(NPOT, c, a, true); + testResults[testCount] = StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(NPOT, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(NPOT, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("work-efficient scan, power-of-two, no shared memory"); + } + StreamCompaction::Efficient::scan(SIZE, c, a, false); + testResults[testCount] = StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(SIZE, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(SIZE, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("work-efficient scan, non-power-of-two, no shared memory"); + } + StreamCompaction::Efficient::scan(NPOT, c, a, false); + testResults[testCount] = StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(NPOT, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(NPOT, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("work-efficient scan, power-of-two, shared memory"); + } + StreamCompaction::Efficient::scan(SIZE, c, a, true); + testResults[testCount] = StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(SIZE, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(SIZE, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("work-efficient scan, non-power-of-two, shared memory"); + } + StreamCompaction::Efficient::scan(NPOT, c, a, true); + testResults[testCount] = StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(NPOT, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(NPOT, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("thrust scan, power-of-two"); + } + StreamCompaction::Thrust::scan(SIZE, c, a); + testResults[testCount] = StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(SIZE, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(SIZE, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } + + do + { + zeroArray(SIZE, c); + if (testCount == 0) + { + printDesc("thrust scan, non-power-of-two"); + } + StreamCompaction::Thrust::scan(NPOT, c, a); + testResults[testCount] = StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(); + if (testCount == 0) + { + //printArray(NPOT, c, true); + } + printElapsedTime(testResults[testCount], "(CUDA Measured)"); + printCmpResult(NPOT, b, c); + } while (runXNumTestsAndGetAverage && ++testCount < numTests); + + if (runXNumTestsAndGetAverage) + { + printAverageOfTestsAndResetTestCount(true); + } printf("\n"); printf("*****************************\n"); From 5d47a27cd5627d08e490b65cc488c857a38fb8a5 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Wed, 18 Sep 2024 23:58:48 -0400 Subject: [PATCH 13/16] updating README (#5) * Update README.md * Update README.md * Update README.md * uploading images * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * upload images * Update README.md * Update README.md * Update README.md * Update README.md * update images * Update README.md * update images * update image * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * upload image * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * readme charts * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * images * Update README.md * Update README.md * Update README.md --- README.md | 213 +++++++++++++++++++++- img/compaction_with_scan.PNG | Bin 0 -> 17865 bytes img/gpu_excluisve_efficient_downsweep.PNG | Bin 0 -> 18381 bytes img/gpu_exclusive_efficient_upsweep.PNG | Bin 0 -> 11643 bytes img/gpu_inclusive_naive.PNG | Bin 0 -> 15855 bytes img/runtime_vs_arraysize.png | Bin 0 -> 169990 bytes img/runtime_vs_blocksize.png | Bin 0 -> 130776 bytes img/scan_visual.PNG | Bin 0 -> 15037 bytes img/stream_compaction_visual.PNG | Bin 0 -> 8555 bytes 9 files changed, 207 insertions(+), 6 deletions(-) create mode 100644 img/compaction_with_scan.PNG create mode 100644 img/gpu_excluisve_efficient_downsweep.PNG create mode 100644 img/gpu_exclusive_efficient_upsweep.PNG create mode 100644 img/gpu_inclusive_naive.PNG create mode 100644 img/runtime_vs_arraysize.png create mode 100644 img/runtime_vs_blocksize.png create mode 100644 img/scan_visual.PNG create mode 100644 img/stream_compaction_visual.PNG diff --git a/README.md b/README.md index 0e38ddb..b031049 100644 --- a/README.md +++ b/README.md @@ -3,12 +3,213 @@ CUDA Stream Compaction **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2** -* (TODO) YOUR NAME HERE - * (TODO) [LinkedIn](), [personal website](), [twitter](), etc. -* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab) +* Michael Rabbitz + * [LinkedIn](https://www.linkedin.com/in/mike-rabbitz) +* Tested on: Windows 10, i7-9750H @ 2.60GHz 32GB, RTX 2060 6GB (Personal) -### (TODO: Your README) +## Part 1: Introduction -Include analysis, etc. (Remember, this is public, so don't put -anything here that you don't want to share with the world.) +This project focuses on implementing various Stream Compaction algorithms, including those utilizing the Scan algorithm, to emphasize the importance of designing GPU hardware-optimized algorithms that leverage parallel computation for superior performance compared to CPU implementations. +### Stream Compaction +Stream compaction involves filtering an input data set to produce a new collection that contains only the elements meeting a specified condition, while preserving their original order. This process reduces the size of the data set by removing unwanted elements, which is crucial for optimizing performance and memory usage in applications like path tracing, collision detection, etc. + +

+ +

+ +### Scan +The Scan algorithm, also known as the all-prefix-sums operation, computes prefix sums for an array of data. In an exclusive scan, each element in the result is the sum of all preceding elements in the input array. Conversely, an inclusive scan includes the current element in the sum. Scan algorithms are fundamental in parallel computing for tasks like sorting, stream compaction, and building data structures, as they transform inherently sequential computations into parallel ones. + +

+ +

+ +## Part 2: Implementation Details + +**n represents the number of elements in the array** + +**All implementations support arrays of arbitrary n - small, large, powers of two, not powers of two** + +### Scan +#### Implementations +1. **CPU:** O(n) addition operations - sequential loop over array elements, accumulating a sum at each iteration +2. **GPU Naive Algorithm:** O(n * log2(n)) addition operations - over log2(n) passes, for pass p starting at p = 1, compute the partial sums of n - 2p - 1 elements in parallel +3. **GPU Work-Efficient Algorithm:** O(n) operations - performs scan into two phases: parallel upsweep (reduction) with n - 1 adds (O(n)), and parallel downsweep with n - 1 adds (O(n)) and n - 1 swaps (O(n)) +4. **GPU Naive Algorithm with Hardware Efficiency:** shared memory - divide the array into evenly-sized blocks, each of which is scanned by a single thread block. Utilize shared memory within each thread block to perform the scan and write the total sum of each block to a separate array of block sums. Then, scan the array of block sums to create an array of block increments, which are added to all elements within their respective blocks. +5. **GPU Work-Efficient Algorithm with Hardware Efficiency:** shared memory - same process as above +6. **GPU using [Thrust CUDA library](https://nvidia.github.io/cccl/thrust):** wrapper function using thrust::exclusive_scan + +#### Inclusive Scan - GPU Naive Algorithm: +

+ +

+ +#### Exclusive Scan - GPU Work-Efficient Algorithm: +**Upsweep** +

+ +

+ +**Downsweep** +

+ +

+ +### Stream Compaction +**Compaction removes invalid elements (0s) from an array of randomized ints** + +#### Stream Compaction with Scan is described in 3 steps: +1. **Create Binary Map:** use the input array to create a binary map array indicating the validity of each input element +2. **Scan:** perform Scan on the binary array to generate a map of index values which correspond to the compacted array index of sequential valid input elements +3. **Scatter:** for the indices of the binary array that indicate valid input elements, use those indices to index the Scan output array to determine the compated array index of the valid element, then place valid element data into the compacted array + +

+ +

+ +#### Implementations +1. **CPU without Scan:** - sequential loop over input elements while placing valid input data into the compacted array +2. **CPU with Scan:** - perform Step 1 with sequential loop over n elements, perform Step 2 using CPU Scan, then perform Step 3 with sequential loop over n elements +3. **GPU with Work-Efficient Scan:** - perform Step 1 over n elements in one parallel pass, perform Step 2 using Work-Efficient Scan, then perform Step 3 over n elements in one parallel pass +4. **GPU with Work-Efficient and Hardware-Efficient Scan:** - same as above line except using Work-Efficient and Hardware-Efficient Scan +5. **GPU using [Thrust CUDA library](https://nvidia.github.io/cccl/thrust):** wrapper function using thrust::remove_if + +## Part 3: Performance Analysis +### Optimal Block Size +**For each GPU Scan implementation, find the block size that gives the minimal runtime on the GPU** + +**Each data point is the average of 100 runtime samples** +

+ +

+ +| Block Size | Naive (ms) | Naive & Hardware-Eff (ms) | Work-Eff | Work-Eff & Hardware-Eff | +| ---------- | ---------- | ------------------------- | -------- | ----------------------- | +|32 |191.8 |24 |47.6 |13.1 | +|64 |116.9 |14.4 |47.9 |9.7 | +|128 |116.8 |13.8 |47.4 |9.3 | +|256 |116.8 |14 |47.8 |9.6 | +|512 |116.8 |14.4 |48.3 |10.5 | +|1024 |118.1 |16.6 |50.4 |12.4 | + +Based on the results, it is clear that block size of 128 is optimal. + +### Runtime +**Includes the CPU Scan implementation, all GPU Scan implementations, and the Thrust Scan** + +**Each data point is the average of 100 runtime samples** +

+ +

+ +| Array Size | CPU (ms) | Naive (ms) | Naive & Hardware-Eff (ms) | Work-Eff | Work-Eff & Hardware-Eff | Thrust (ms) | +| ------------ | -------- |------------ | -------------------------- | --------- | ------------------------ | ------------ | +|210|0.0005 |0.0332 |0.0626 |0.0786 |0.0742 |0.0670 | +|212|0.0018 |0.0390 |0.0570 |0.1122 |0.0766 |0.1143 | +|214|0.0068 |0.0494 |0.0589 |0.1236 |0.0598 |0.0514 | +|216|0.0241 |0.0756 |0.1145 |0.1271 |0.0776 |0.0793 | +|218|0.1092 |0.1705 |0.2576 |0.1742 |0.1362 |0.1640 | +|220|0.5457 |0.8490 |0.3955 |0.4744 |0.2322 |0.2773 | +|222|2.8498 |2.9690 |0.7290 |1.4791 |0.4043 |0.3592 | +|224|13.7399 |12.8068 |1.9275 |5.9673 |1.2438 |0.6996 | + +Based on the results, we see that the CPU implementation is the fastest up to array size 218. +As array size continues to increase past this, all of the GPU algorithms besides Naive get relatively exponentially faster. +Work-Efficient, although much better than CPU and Naive, does itself start to have exponentially longer run times as we get to very large sizes. +The Naive & Hardware-Efficient Scan and Work-Efficient & Hardware-Efficient Scan both exemplify the signficance of using shared memory best practices (no bank conflicts) and warp partitioning. +Thrust clearly starts to pull away as we get to the largest array sizes, but I am proud of how my Work-Efficient & Hardware-Efficient Scan gives it a run for its money! + + +### Performance Bottlenecks + +The Work-Efficient Scan is clearly faster than the Naive Scan since there is less overall work to do as described in the implementation details in the [Scan](#Scan) section. +However, since they both have a vast amount of read and write operations from/to global memory, they both perform poorly relative to the Hardware-Efficient implementations that use shared memory. +Another inefficiency of the Naive Scan is the absense of warp partitioning best practices, which the rest of the GPU Scan implementations utilize. Warp paritioning is how threads from a block are divided into warps, and the goal is to partition based on consecutive increasing thread indices such that divergent branches are minimized and warps are retired early, freeing up resources for the GPU to perform any other available work with. +Lastly, since the Naive & Hardware-Efficient and Work-Efficient & Hardware-Efficient implementations are optimized with shared memory and warp paritioning best practices, another bottleneck comes into play called bakn conflicts. Shared memory is split up to 32 banks such that each bank can service on address per cycle. This was not a problem when implementing the Naive & Hardware-Efficient Scan, but was introduced in the initial implementations of the Work-Efficient & Hardware-Efficient Scan. Through adding a padding offset to after every 32 shared memory elements, the bank conflicts were alleviated and performance increased. + +### Sample Output +This output is used to test the correctness and timing of all Scan and Stream Compaction implementations. + +In this sample, Array Size of 220 is used. + +``` +**************** +** SCAN TESTS ** +**************** + [ 12 47 23 8 49 44 11 3 48 22 33 4 12 ... 31 0 ] +==== cpu scan, power-of-two ==== + [ 0 12 59 82 90 139 183 194 197 245 267 300 304 ... 25666918 25666949 ] + elapsed time: 0.6517ms (std::chrono Measured) +==== cpu scan, non-power-of-two ==== + [ 0 12 59 82 90 139 183 194 197 245 267 300 304 ... 25666812 25666855 ] + elapsed time: 0.5208ms (std::chrono Measured) + passed +==== naive scan, power-of-two, no shared memory ==== + elapsed time: 1.07728ms (CUDA Measured) + passed +==== naive scan, non-power-of-two, no shared memory ==== + elapsed time: 0.843456ms (CUDA Measured) + passed +==== naive scan, power-of-two, shared memory ==== + elapsed time: 0.49872ms (CUDA Measured) + passed +==== naive scan, non-power-of-two, shared memory ==== + elapsed time: 0.477088ms (CUDA Measured) + passed +==== work-efficient scan, power-of-two, no shared memory ==== + elapsed time: 0.566848ms (CUDA Measured) + passed +==== work-efficient scan, non-power-of-two, no shared memory ==== + elapsed time: 0.468896ms (CUDA Measured) + passed +==== work-efficient scan, power-of-two, shared memory ==== + elapsed time: 0.216576ms (CUDA Measured) + passed +==== work-efficient scan, non-power-of-two, shared memory ==== + elapsed time: 0.2104ms (CUDA Measured) + passed +==== thrust scan, power-of-two ==== + elapsed time: 0.621248ms (CUDA Measured) + passed +==== thrust scan, non-power-of-two ==== + elapsed time: 0.183968ms (CUDA Measured) + passed + +***************************** +** STREAM COMPACTION TESTS ** +***************************** + [ 0 1 1 0 1 0 3 3 0 0 3 0 0 ... 1 0 ] +==== cpu compact without scan, power-of-two ==== + elapsed time: 2.4059ms (std::chrono Measured) + [ 1 1 1 3 3 3 1 1 1 3 1 3 1 ... 3 1 ] + passed +==== cpu compact without scan, non-power-of-two ==== + elapsed time: 2.4662ms (std::chrono Measured) + [ 1 1 1 3 3 3 1 1 1 3 1 3 1 ... 3 2 ] + passed +==== cpu compact with scan, power-of-two ==== + elapsed time: 4.6895ms (std::chrono Measured) + passed +==== cpu compact with scan, non-power-of-two ==== + elapsed time: 5.1045ms (std::chrono Measured) + passed +==== work-efficient compact, power-of-two, no shared memory ==== + elapsed time: 1.39776ms (CUDA Measured) + passed +==== work-efficient compact, non-power-of-two, no shared memory ==== + elapsed time: 0.817856ms (CUDA Measured) + passed +==== work-efficient compact, power-of-two, shared memory ==== + elapsed time: 0.602912ms (CUDA Measured) + passed +==== work-efficient compact, non-power-of-two, shared memory ==== + elapsed time: 0.588288ms (CUDA Measured) + passed +==== thrust compact, power-of-two ==== + elapsed time: 0.28656ms (CUDA Measured) + passed +==== thrust compact, non-power-of-two ==== + elapsed time: 0.283488ms (CUDA Measured) + passed +``` diff --git a/img/compaction_with_scan.PNG b/img/compaction_with_scan.PNG new file mode 100644 index 0000000000000000000000000000000000000000..a6f780f9b8fbdda26eddd57617e11ff82e2cffe9 GIT binary patch literal 17865 zcmcJ%2Ut^IoaT=pA}ELyQL0J@0i`!Vr1utjQ|V2RDkVVyLAp{klmMak4xvRUA|Rd6 zAp%0^p_hd6zxbQko!OcH?(9ChJP%xM5^~Qu_uP~Fe%|*>gpQUnIT<|}0RaKIs)~Xh z0Ra&s0l}3U*RKJO=&QdY1U6SZ^^~6zl=U;N0y|gjEOY-3FCL? zaIhl#yu2p6yrz8Ty!`c}v%ocd6zb_L%D$)wmfx@EDoPBrpMW6eH#%G5@|Q#=5yodf zY8=s=rbW!DV*($O)%0U*A111|<|Rky@ON{i1g4qASC+FCFMAYfiZAo+3lY#31z{bs z98XDIf6!$s8@!mn*q-c6@6VOPl~GxJKAb6>TraG;Sfb&Cb&U6h#QKt}oQqLaHt zsqTr~nOAh|Y^7x+hA^yLDnDA|qdim&`@ZWHir2F(-c>W~>!JjU|7~JeC=sxI=A`n! zvn!bH{>Y?m7;U4NkX*em0WmtpN<)Uz9LRyU`b6O*F1WljK8DDRE8p#{-m+N3Tpqzx z1$elwDOo|izZAS@A-1=roUQzf+T~`RvZ%ySb&!PE#@>`_T&?AR{b;Y%#Bh4c()sIp z*H~}YI;lzRuDbPQJBTf2h4$W&LPgN&!wGHvm`yRLA<266a!@cf&`rxk<2tjz&%-DC 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    ;Q-+cSs=DVKAABc@zxoYvkUz^#{Eu|N~DCaPhqe>5D zyhfpfKdf#uX;M`n_sn&TR)E&`lHbvdFWw^*oJSqp9ynU>FQ^hDf68rqz~0WnlTI1a zH?HFL=A6)?;yFB=JUALdbT-V%K%WLaC=^#gvHb4xh)C6_tScd+Lw(c2Wkw4awPxtL ze+#=wLa014TyNGiabKL#hdYl6oV>AAi?Z)HJ=+gDc=L!f7d>?BaC&)J5;h<~!6~zc zdHAB#vt=%_8>gU#8am0d4tn>9_v|q5k>~N~OW!L> zOSO*z_n15CoPdnjkl@z9@R!cuM#+z2f(q2Ys3!+uK8_^@CF1j-wWGkxftM2h!^|_6 z`oN$0b`rV=42>UJQ~e^$6t_&&*Qu+C>)9Yezm3rx_wwDGY3!*Dd{VABHDz3+TdF5Y zT>I$F>rP%!a3y%6E@VU?jr%Ho5?E!|e@2c#G0saV<#ZwZ>x$V=ZV9#`#&teOjhI_H z!7H9uRaxB@P~Qs~CTHAv!ddkNkRQMMOeB?0gx-&HqcG5VPSY41%&PzN_PZNBAz9hx znT1&^-TuE0$ySBXNpuV{Uc!_EVLuC59t68++JjS&b zjal+U>AuX*vlmN(ZEp#8w;{4k&G5JHF$sK>pUhESc6Jj~&lopM`Rc^=VD5E!Kki z`a@5qlllT-XfUlKYAI3o@qah=oV;VxOilx=q$*D4`Fn9NB;{@;g^{Whzf?@H2gmm z3Ap(^I*HT^<=u3=Dx|81W(R#bt^Bu^kxtyTsnX#;9LP$%B&^BKS)Am~Nj7!yLddU5 z7Sd`I9-vl6WtFzq z$%KdzfYH=V=!h$D-llnl{UIjDofCMns%aczyG=86BA5o{UhRb7v6Z;~S{7C5^pemB zvM`aeyJ%>|Ya)X!G`MskdB_iHP2|~24}~4djrWk{wwE4BN#E&Icd^ESQ{!Q+v<4TU zM7q}>CtCEB?74c>hQ?ziJ67p?YUPl{Bh5`_A(9?~C2n9hF!M*009|w`b()_&HPPLS zoTz#_r?oO7L$0=~pM+X6+JuhR^rC-qq!&q_>ytN=Ec(+)Llz_3=UBS>y%h%cLzLLJ z7?r9PZf`mZsUXKt_su|?WQ&#? zAs{sP{V+~73AmzKO$FppACF3)XNVe9KJ6x#3u9EnExEMP^qeSf1~dHaX!FvvIN( zGqHv(^CNTOkKgHi&cZQJmPPBT?(xX4nk=$UZ^kvil`a6V(Mr~@Q$%zCBhdk!{&pE^ zyX2#pcU7aYizWGOWBfHdOJ8Xut@s~M?pTI!tjSDK@ry~9g zcJT`_NTvk1jXSaMeHva2tnH0?JE4@P<)jYil1KrRu5=LfIsIIts zg5EocJBpIYM-qq3yIUckih?t_Kj3dCSW}pW4Urr*N+C%PpJO*ebiec1lsWUxuhy}p zV_vp_4h@`eq4@;k(x6SJbjw5fL46(W2@CA^DBCyZ9arP? z^M`lR^!3-KsIz73G%w4vR=kOeO-r0Xlbr(vBofvSpmMlS>zR-ftOCCldz>mQR>NS| z!6EJ{x5l9&LdR?WS+W!Qn`P(v52JoQsf&s;XLRhV1+kSyjy-{ic}D|z#dNdsMFu+NYU_#TI?qV%gY!=>sL28~SmM>0a z`-e?nDs{3=JWP8}3Le4vdUdEOg`yKnP!@<#@WO%PI|Be)?hw^$QcdI$10nI?7=+^iVyqf(g>^CO0a& zG||7a<5b`#Jr(a4_pEZrD=7@yy% zbAYY9N5n&p=wTWsKAp?DV0sZ`$phN_YJvdvne7ceam(z1EDNFJ-|P|T z6pS{%J3+)~0};3O%{#4VKl*lCZ>~1$ Date: Thu, 19 Sep 2024 00:02:17 -0400 Subject: [PATCH 14/16] minor README fix --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b031049..1cdfb08 100644 --- a/README.md +++ b/README.md @@ -123,7 +123,7 @@ Thrust clearly starts to pull away as we get to the largest array sizes, but I a ### Performance Bottlenecks -The Work-Efficient Scan is clearly faster than the Naive Scan since there is less overall work to do as described in the implementation details in the [Scan](#Scan) section. +The Work-Efficient Scan is clearly faster than the Naive Scan since there is less overall work to do as described in the implementation details in the [Part 2](#part-2-implementation-details) Scan section. However, since they both have a vast amount of read and write operations from/to global memory, they both perform poorly relative to the Hardware-Efficient implementations that use shared memory. Another inefficiency of the Naive Scan is the absense of warp partitioning best practices, which the rest of the GPU Scan implementations utilize. Warp paritioning is how threads from a block are divided into warps, and the goal is to partition based on consecutive increasing thread indices such that divergent branches are minimized and warps are retired early, freeing up resources for the GPU to perform any other available work with. Lastly, since the Naive & Hardware-Efficient and Work-Efficient & Hardware-Efficient implementations are optimized with shared memory and warp paritioning best practices, another bottleneck comes into play called bakn conflicts. Shared memory is split up to 32 banks such that each bank can service on address per cycle. This was not a problem when implementing the Naive & Hardware-Efficient Scan, but was introduced in the initial implementations of the Work-Efficient & Hardware-Efficient Scan. Through adding a padding offset to after every 32 shared memory elements, the bank conflicts were alleviated and performance increased. From 827ea50fbc8bc29e3dff0d6c6c3346fa586df32d Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Thu, 19 Sep 2024 00:16:36 -0400 Subject: [PATCH 15/16] fixing README typos --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1cdfb08..b1b733c 100644 --- a/README.md +++ b/README.md @@ -126,7 +126,7 @@ Thrust clearly starts to pull away as we get to the largest array sizes, but I a The Work-Efficient Scan is clearly faster than the Naive Scan since there is less overall work to do as described in the implementation details in the [Part 2](#part-2-implementation-details) Scan section. However, since they both have a vast amount of read and write operations from/to global memory, they both perform poorly relative to the Hardware-Efficient implementations that use shared memory. Another inefficiency of the Naive Scan is the absense of warp partitioning best practices, which the rest of the GPU Scan implementations utilize. Warp paritioning is how threads from a block are divided into warps, and the goal is to partition based on consecutive increasing thread indices such that divergent branches are minimized and warps are retired early, freeing up resources for the GPU to perform any other available work with. -Lastly, since the Naive & Hardware-Efficient and Work-Efficient & Hardware-Efficient implementations are optimized with shared memory and warp paritioning best practices, another bottleneck comes into play called bakn conflicts. Shared memory is split up to 32 banks such that each bank can service on address per cycle. This was not a problem when implementing the Naive & Hardware-Efficient Scan, but was introduced in the initial implementations of the Work-Efficient & Hardware-Efficient Scan. Through adding a padding offset to after every 32 shared memory elements, the bank conflicts were alleviated and performance increased. +Lastly, since the Naive & Hardware-Efficient and Work-Efficient & Hardware-Efficient implementations are optimized with shared memory and warp paritioning best practices, another bottleneck comes into play called bank conflicts. Shared memory is split up into 32 banks such that each bank can service one address per cycle. This was not a problem when implementing the Naive & Hardware-Efficient Scan, but was introduced in the initial implementation stages of the Work-Efficient & Hardware-Efficient Scan. Through adding a padding element after every 32 shared memory elements, the bank conflicts were alleviated and performance increased. ### Sample Output This output is used to test the correctness and timing of all Scan and Stream Compaction implementations. From 77135e18442a0b3e19255950f6bc463d2181cbb5 Mon Sep 17 00:00:00 2001 From: mrabbitz <54726631+mrabbitz@users.noreply.github.com> Date: Fri, 13 Dec 2024 17:07:48 -0500 Subject: [PATCH 16/16] Readme updates (#6) Readme updates (#6) --- README.md | 82 +++++++++++++++++++----------------- img/scan_array_arb_size.PNG | Bin 0 -> 126758 bytes 2 files changed, 44 insertions(+), 38 deletions(-) create mode 100644 img/scan_array_arb_size.PNG diff --git a/README.md b/README.md index b1b733c..ef14151 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ CUDA Stream Compaction ## Part 1: Introduction -This project focuses on implementing various Stream Compaction algorithms, including those utilizing the Scan algorithm, to emphasize the importance of designing GPU hardware-optimized algorithms that leverage parallel computation for superior performance compared to CPU implementations. +This project implements various Stream Compaction algorithms, with an emphasis on those utilizing the Scan algorithm, to highlight the importance of designing GPU hardware-optimized algorithms that leverage parallel computation for superior performance. ### Stream Compaction Stream compaction involves filtering an input data set to produce a new collection that contains only the elements meeting a specified condition, while preserving their original order. This process reduces the size of the data set by removing unwanted elements, which is crucial for optimizing performance and memory usage in applications like path tracing, collision detection, etc. @@ -27,52 +27,49 @@ The Scan algorithm, also known as the all-prefix-sums operation, computes prefix ## Part 2: Implementation Details -**n represents the number of elements in the array** - -**All implementations support arrays of arbitrary n - small, large, powers of two, not powers of two** +**All implementations support arrays of arbitrary size** ### Scan #### Implementations 1. **CPU:** O(n) addition operations - sequential loop over array elements, accumulating a sum at each iteration 2. **GPU Naive Algorithm:** O(n * log2(n)) addition operations - over log2(n) passes, for pass p starting at p = 1, compute the partial sums of n - 2p - 1 elements in parallel 3. **GPU Work-Efficient Algorithm:** O(n) operations - performs scan into two phases: parallel upsweep (reduction) with n - 1 adds (O(n)), and parallel downsweep with n - 1 adds (O(n)) and n - 1 swaps (O(n)) -4. **GPU Naive Algorithm with Hardware Efficiency:** shared memory - divide the array into evenly-sized blocks, each of which is scanned by a single thread block. Utilize shared memory within each thread block to perform the scan and write the total sum of each block to a separate array of block sums. Then, scan the array of block sums to create an array of block increments, which are added to all elements within their respective blocks. -5. **GPU Work-Efficient Algorithm with Hardware Efficiency:** shared memory - same process as above +4. **GPU Naive Algorithm with Hardware Efficiency:** uses shared memory - divide the array into blocks, then scan each block in parallel over many SMs (with each block corresponding to a thread block). Utilize shared memory within each thread block to perform the scan and write the total sum of each block to a new array of block sums. Scan the array of block sums to compute an array of block increments, which are then added to each element in the corresponding scanned block from the initial scan (e.g. the zero-indexed block increment from the block increments array is added to each element in the zero-indexed scanned block of the divided input array). +5. **GPU Work-Efficient Algorithm with Hardware Efficiency:** uses shared memory - same process as above 6. **GPU using [Thrust CUDA library](https://nvidia.github.io/cccl/thrust):** wrapper function using thrust::exclusive_scan -#### Inclusive Scan - GPU Naive Algorithm: -

    - -

    +|Inclusive Scan - GPU Naive Algorithm| +|:--:| +|![GPU Inclusive Naive](img/gpu_inclusive_naive.PNG)| -#### Exclusive Scan - GPU Work-Efficient Algorithm: -**Upsweep** -

    - -

    +|Exclusive Scan - GPU Work-Efficient Algorithm| +|:--:| +|Upsweep | +|![GPU Exclusive Efficient Upsweep](img/gpu_exclusive_efficient_upsweep.PNG) | +|Downsweep | +|![GPU Exclusive Efficient Upsweep](img/gpu_excluisve_efficient_downsweep.PNG)| -**Downsweep** -

    - -

    +|Inclusive Scan - GPU with Hardware Efficiency using shared memory| +|:--:| +|![GPU Scan Arb Size Array](img/scan_array_arb_size.PNG)| ### Stream Compaction -**Compaction removes invalid elements (0s) from an array of randomized ints** +**For this project, Stream Compaction is performed on an array of randomized non-negative integers, where elements with a value of 0 are considered 'invalid' and those with positive values are considered 'valid'.** #### Stream Compaction with Scan is described in 3 steps: -1. **Create Binary Map:** use the input array to create a binary map array indicating the validity of each input element -2. **Scan:** perform Scan on the binary array to generate a map of index values which correspond to the compacted array index of sequential valid input elements -3. **Scatter:** for the indices of the binary array that indicate valid input elements, use those indices to index the Scan output array to determine the compated array index of the valid element, then place valid element data into the compacted array +1. **Create Binary Map:** use the input array to generate a binary map array indicating the validity of each input element (0 for invalid, 1 for valid) +2. **Scan:** perform Scan on the binary map to generate an array of indices that indicate the compacted array positions of valid input elements +3. **Scatter:** for each index in the binary map that indicates a valid input element - use the index to retrieve the valid element data from the input array, and use the same index to retrieve the compacted array index from the Scan output array. Use these two retrieved values to place the valid element data into the compacted array

    #### Implementations -1. **CPU without Scan:** - sequential loop over input elements while placing valid input data into the compacted array -2. **CPU with Scan:** - perform Step 1 with sequential loop over n elements, perform Step 2 using CPU Scan, then perform Step 3 with sequential loop over n elements -3. **GPU with Work-Efficient Scan:** - perform Step 1 over n elements in one parallel pass, perform Step 2 using Work-Efficient Scan, then perform Step 3 over n elements in one parallel pass -4. **GPU with Work-Efficient and Hardware-Efficient Scan:** - same as above line except using Work-Efficient and Hardware-Efficient Scan +1. **CPU without Scan:** sequential loop over n input elements while placing valid input data into the compacted array +2. **CPU with Scan:** Create Binary Map with sequential loop over n input elements, Scan using CPU Scan, then Scatter with sequential loop over n elements +3. **GPU with Work-Efficient Scan:** Create Binary Map over n input elements in one parallel pass, Scan using Work-Efficient Scan, then Scatter over n elements in one parallel pass +4. **GPU with Work-Efficient & Hardware-Efficient Scan:** same as above line except using Work-Efficient & Hardware-Efficient Scan 5. **GPU using [Thrust CUDA library](https://nvidia.github.io/cccl/thrust):** wrapper function using thrust::remove_if ## Part 3: Performance Analysis @@ -114,24 +111,33 @@ Based on the results, it is clear that block size of 128 is optimal. |222|2.8498 |2.9690 |0.7290 |1.4791 |0.4043 |0.3592 | |224|13.7399 |12.8068 |1.9275 |5.9673 |1.2438 |0.6996 | -Based on the results, we see that the CPU implementation is the fastest up to array size 218. -As array size continues to increase past this, all of the GPU algorithms besides Naive get relatively exponentially faster. -Work-Efficient, although much better than CPU and Naive, does itself start to have exponentially longer run times as we get to very large sizes. -The Naive & Hardware-Efficient Scan and Work-Efficient & Hardware-Efficient Scan both exemplify the signficance of using shared memory best practices (no bank conflicts) and warp partitioning. -Thrust clearly starts to pull away as we get to the largest array sizes, but I am proud of how my Work-Efficient & Hardware-Efficient Scan gives it a run for its money! +Based on the results, CPU Scan is the fastest up to an array size of 218. +Beyond this size, all GPU Scans, except for Naive Scan, exhibit exponential speedups relative to CPU Scan. +Work-Efficient Scan, although much faster than both CPU Scan and Naive Scan at large array sizes, begins to experience exponentially longer run times as we reach these sizes. +The results of Naive & Hardware-Efficient Scan and Work-Efficient & Hardware-Efficient Scan demonstrate the importance of effectively utilizing shared memory (e.g. avoiding bank conflicts) and incorporating warp partitioning. +As we approach the largest tested array sizes, Thrust clearly pulls ahead, but I am proud of how my Work-Efficient & Hardware-Efficient Scan competes closely with it! ### Performance Bottlenecks -The Work-Efficient Scan is clearly faster than the Naive Scan since there is less overall work to do as described in the implementation details in the [Part 2](#part-2-implementation-details) Scan section. -However, since they both have a vast amount of read and write operations from/to global memory, they both perform poorly relative to the Hardware-Efficient implementations that use shared memory. -Another inefficiency of the Naive Scan is the absense of warp partitioning best practices, which the rest of the GPU Scan implementations utilize. Warp paritioning is how threads from a block are divided into warps, and the goal is to partition based on consecutive increasing thread indices such that divergent branches are minimized and warps are retired early, freeing up resources for the GPU to perform any other available work with. -Lastly, since the Naive & Hardware-Efficient and Work-Efficient & Hardware-Efficient implementations are optimized with shared memory and warp paritioning best practices, another bottleneck comes into play called bank conflicts. Shared memory is split up into 32 banks such that each bank can service one address per cycle. This was not a problem when implementing the Naive & Hardware-Efficient Scan, but was introduced in the initial implementation stages of the Work-Efficient & Hardware-Efficient Scan. Through adding a padding element after every 32 shared memory elements, the bank conflicts were alleviated and performance increased. +The Work-Efficient Scan is clearly faster than the Naive Scan due to the reduced overall work involved, as described in the implementation details in the [Part 2](#part-2-implementation-details) Scan section. +However, both implementations suffer from a significant number of read and write operations to and from global memory, resulting in poor performance compared to Hardware-Efficient implementations that utilize shared memory. + +Another inefficiency of the Naive Scan is the absence of warp partitioning best practices, which the other GPU Scan implementations adopt. +Warp partitioning involves dividing threads from a block into warps, aiming to partition based on consecutive increasing thread indices. +This approach minimizes divergent branches and allows warps to retire early, freeing up GPU resources for other available work. + +Lastly, while the Naive & Hardware-Efficient Scan and Work-Efficient & Hardware-Efficient Scan are optimized with shared memory and warp partitioning practices, they introduce a bottleneck known as bank conflicts that are specific to shared memory. +Shared memory is divided into 32 banks, allowing each bank to service one address per cycle. +Although bank conflicts were not an issue in the Naive & Hardware-Efficient Scan, they emerged during the later implementation stages of the Work-Efficient & Hardware-Efficient Scan. +To address this, a padding element was added after every 32 shared memory elements, effectively alleviating these bank conflicts and leading to improved performance. + + ### Sample Output -This output is used to test the correctness and timing of all Scan and Stream Compaction implementations. +This is a sample of the output used to test the correctness and timing of all Scan and Stream Compaction implementations. -In this sample, Array Size of 220 is used. +In this sample, array size is 220 ``` **************** diff --git a/img/scan_array_arb_size.PNG b/img/scan_array_arb_size.PNG new file mode 100644 index 0000000000000000000000000000000000000000..c6daf54fffb2bf86c76c194df77130e97f75b2eb GIT binary patch literal 126758 zcmb@tg;!h67cNY3cP&=jokH;9?(S|y0~B{D?p7pFyakH8d(Z%-xCRRphhoJ(-u}Kn z;NG?FS|`cOS;?7MGkfo6&-3iWYN*L$p}$3kgM-6TRFKhvgF_&IgM-IKMSi{Wx1CSv z_2Z4Fmb?^P4TR$GwS#CcsVWHvSD%dWXo>XNM{`#&^n`=M9{lh12JH6D1`h6}Oi@Ns z$Jgu_gcPbXPcqVbvSAx>dU~3LD=ArT%=4jN=4lnQgw>J}8ucSIl+;U5!KZPZ=jCcW zCwh<@V*+{dxS-I??q;w3cD?;Ju(cprHME$SN>)vc^YJkd`bk1gZ!upnBZgKfOpEl4n(4+N#yS9hLKIL%k=t6(OS z|GglWx_D3*=aeJ+1fe+>D+ZPo``n7uaAdJB?xT?Fx*r@tZ*D}#`>H(npT_6c&9U7= zmmGb0ziH}MEdy!@_sw+P^41P94V46%BrKgS9Uy7YyRFxgXR;2_0+^SE2FcGe6Vg(? zg#Kz$(W%m!MWuD?pPv5wCqV@f8BwIg7v<6P^7c@*SM_)EaXy8Ojomv|QK@m6GZr1@ zQ$e@Y*pBAP3$6z_u29?Yf+v(RQ8X!)c4oY%jjPp+T=1j7pwvKDIXnN|Zm}_VF{Fgi%!So5zV#%ZGgSQn1s=bx1D4AF3^}nh)nx8=fzS9K#ANi-c zy;IWB2&wsRLxtaKcaNXa$vd`6u{Am9y;}kjPh5{ky>x`^Q5oT 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