Performance enhancement for Volta Tensor Cores TN layout (#53)
* Fixed performance defect with indirect access to pointer array for Volta TensorCores TN arrangement. * Updated patch version and changelog. * Updated patch version and changelog. * Added link to changelog in readme. * Fixed markdown link
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Andrew Kerr
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# NVIDIA CUTLASS Changelog
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## [1.3.2](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.2) (2019-07-09)
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* Performance improvement for Volta Tensor Cores TN and TT layouts.
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## [1.3.1](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.1) (2019-04-09)
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* Corrected NVRTC unit tests.
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@ -2,7 +2,7 @@
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# CUTLASS 1.3
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_CUTLASS 1.3.1 - April 2019_
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_CUTLASS 1.3.2 - July 2019_
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CUTLASS is a collection of CUDA C++ template abstractions for implementing
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high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA.
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@ -28,9 +28,6 @@ CUTLASS 1.3 is described in the [CUTLASS Documentation](CUTLASS.md) and the acco
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We describe the structure of an efficient GEMM in our talk at the
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[GPU Technology Conference 2018](http://on-demand.gputechconf.com/gtc/2018/presentation/s8854-cutlass-software-primitives-for-dense-linear-algebra-at-all-levels-and-scales-within-cuda.pdf).
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# What's New in CUTLASS 1.3.1
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_April 2019_
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* CUTLASS 1.3.1 corrected NVRTC unit tests..
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# What's New in CUTLASS 1.3
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_March 2019_
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@ -60,6 +57,8 @@ _September 2018_
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* [Reference implementations](tools/util/reference) for tensor operations in [host](tools/util/reference/host) and [device](tools/util/reference/device) code
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* Added `HostMatrix<>` for simplified matrix creation
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For all updates, see the [CUTLASS changelog](CHANGELOG.md).
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# Performance
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<p align="center"><img src=/media/images/cutlass-performance-plot.png></p>
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@ -34,7 +34,7 @@
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#define CUTLASS_MAJOR 1
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#define CUTLASS_MINOR 3
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#define CUTLASS_PATCH 1
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#define CUTLASS_PATCH 2
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#define CUTLASS_VERSION ((CUTLASS_MAJOR)*100 + (CUTLASS_MINOR)*10 + CUTLASS_PATCH)
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#if defined(__NVCC__) || (defined(__clang__) && defined(__CUDA__))
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@ -237,6 +237,12 @@ struct Volta884ThreadblockMultiplicandStoreIterator<GemmOperand::kA,
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Coord<4> offset = offset_func(ptr_idx);
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pointer[ptr_idx] = _params.pointer + (_block_offset + offset).template dot<int>(stride);
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}
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if (((threadIdx.x >> 5) * Iterations::kD) & 2) {
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Scalar *tmp = pointer[0];
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pointer[0] = pointer[1];
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pointer[1] = tmp;
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}
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}
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/// Stores a fragment
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@ -254,16 +260,12 @@ struct Volta884ThreadblockMultiplicandStoreIterator<GemmOperand::kA,
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CUTLASS_PRAGMA_UNROLL
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for (int w = 0; w < Iterations::kW; ++w) { // 2x STS operations per LDG
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int warp_id = (threadIdx.x >> 5);
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int ldg_idx = d + warp_id * Iterations::kD;
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int k_idx = w + h * 8;
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int smem_row = (d >> 1);
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// Two store pointers
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int ptr_idx = ((ldg_idx & 1) ^ ((ldg_idx >> 1) & 1));
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Scalar *_pointer = pointer[(d & 1) ^ ((d >> 1) & 1)];
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Scalar *_pointer = pointer[ptr_idx];
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Coord<4> sts_offset = make_Coord(k_idx, smem_row, 0, 0);
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Store<typename Fragment::Element, kAccessSize, kMemorySpace>::store(
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@ -277,6 +279,7 @@ struct Volta884ThreadblockMultiplicandStoreIterator<GemmOperand::kA,
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/// Increments store iterator to next tile
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__device__ Volta884ThreadblockMultiplicandStoreIterator &increment(int count = 1) {
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CUTLASS_PRAGMA_UNROLL
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for (int ptr_idx = 0; ptr_idx < kPointerCount; ++ptr_idx) {
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pointer[ptr_idx] +=
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make_Coord(VectorizedShape::kD * count, 0, 0, 0).template dot<int>(stride);
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@ -293,6 +296,7 @@ struct Volta884ThreadblockMultiplicandStoreIterator<GemmOperand::kA,
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/// Increments store iterator to previous tile
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__device__ Volta884ThreadblockMultiplicandStoreIterator &decrement(int count = 1) {
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CUTLASS_PRAGMA_UNROLL
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for (int ptr_idx = 0; ptr_idx < kPointerCount; ++ptr_idx) {
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pointer[ptr_idx] -=
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make_Coord(VectorizedShape::kD * count, 0, 0, 0).template dot<int>(stride);
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@ -183,7 +183,7 @@ TEST(Volta884_f16_s884gemm_128x128x32_tt, short_480x280x224) {
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// Contiguous - s884gemm
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//
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////////////////////////////////////////////////////////////////////////////////////////////////////
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#if 0
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TEST(Volta884_f16_s884gemm_64x64x32_nt, 64x64x32) {
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typedef cutlass::gemm::Volta884GemmTraits<
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@ -218,7 +218,6 @@ TEST(Volta884_f16_s884gemm_64x64x32_nt, 64x64x30_residue) {
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run_gemm<GemmTraits>(64, 64, 30);
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}
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#if 0
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////////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(Volta884_f16_s884gemm_64x64x32_nt, 64x64x64) {
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@ -874,7 +873,6 @@ TEST(Volta884_f16_s884gemm_128x128x32_nn, 392x264x192) {
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run_gemm<GemmTraits>(392, 264, 192);
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}
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#endif
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////////////////////////////////////////////////////////////////////////////////////////////////////
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@ -1281,7 +1279,6 @@ TEST(Volta884_f16_s884gemm_f16_128x256x32_tn, 480x280x224) {
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run_gemm<GemmTraits>(480, 280, 224);
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}
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#endif
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////////////////////////////////////////////////////////////////////////////////////////////////////
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#endif // if defined(CUTLASS_ENABLE_TENSOR_CORE_MMA)
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