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

CHANGELOG.md

 # NVIDIA CUTLASS Changelog
 
+## [1.3.2](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.2) (2019-07-09)
+ * Performance improvement for Volta Tensor Cores TN and TT layouts.
+
 ## [1.3.1](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.1) (2019-04-09)
  * Corrected NVRTC unit tests.
 

README.md

@@ -2,7 +2,7 @@
 
 # CUTLASS 1.3
 
-_CUTLASS 1.3.1 - April 2019_
+_CUTLASS 1.3.2 - July 2019_
 
 CUTLASS is a collection of CUDA C++ template abstractions for implementing
 high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA.
@@ -28,9 +28,6 @@ CUTLASS 1.3 is described in the [CUTLASS Documentation](CUTLASS.md) and the acco
 We describe the structure of an efficient GEMM in our talk at the
 [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).
 
-# What's New in CUTLASS 1.3.1
-_April 2019_
-* CUTLASS 1.3.1 corrected NVRTC unit tests..
 
 # What's New in CUTLASS 1.3
 _March 2019_
@@ -60,6 +57,8 @@ _September 2018_
   * [Reference implementations](tools/util/reference) for tensor operations in [host](tools/util/reference/host) and [device](tools/util/reference/device) code
   * Added `HostMatrix<>` for simplified matrix creation
 
+For all updates, see the [CUTLASS changelog](CHANGELOG.md).
+
 # Performance
 
 <p align="center"><img src=/media/images/cutlass-performance-plot.png></p>

cutlass/cutlass.h

@@ -34,7 +34,7 @@
 
 #define CUTLASS_MAJOR 1
 #define CUTLASS_MINOR 3
-#define CUTLASS_PATCH 1
+#define CUTLASS_PATCH 2
 #define CUTLASS_VERSION ((CUTLASS_MAJOR)*100 + (CUTLASS_MINOR)*10 + CUTLASS_PATCH)
 
 #if defined(__NVCC__) || (defined(__clang__) && defined(__CUDA__))

cutlass/gemm/volta884_shared_tile_crosswise.h

@@ -237,6 +237,12 @@ struct Volta884ThreadblockMultiplicandStoreIterator<GemmOperand::kA,
       Coord<4> offset = offset_func(ptr_idx);
       pointer[ptr_idx] = _params.pointer + (_block_offset + offset).template dot<int>(stride);
     }
+  
+    if (((threadIdx.x >> 5) * Iterations::kD) & 2) {
+      Scalar *tmp = pointer[0];
+      pointer[0] = pointer[1];
+      pointer[1] = tmp;
+    }
   }
 
   /// Stores a fragment
@@ -254,16 +260,12 @@ struct Volta884ThreadblockMultiplicandStoreIterator<GemmOperand::kA,
         CUTLASS_PRAGMA_UNROLL
         for (int w = 0; w < Iterations::kW; ++w) {  // 2x STS operations per LDG
 
-          int warp_id = (threadIdx.x >> 5);
-
-          int ldg_idx = d + warp_id * Iterations::kD;
           int k_idx = w + h * 8;
           int smem_row = (d >> 1);
 
           // Two store pointers
-          int ptr_idx = ((ldg_idx & 1) ^ ((ldg_idx >> 1) & 1));
-
-          Scalar *_pointer = pointer[ptr_idx];
+          Scalar *_pointer = pointer[(d & 1) ^ ((d >> 1) & 1)];
+          
           Coord<4> sts_offset = make_Coord(k_idx, smem_row, 0, 0);
 
           Store<typename Fragment::Element, kAccessSize, kMemorySpace>::store(
@@ -277,6 +279,7 @@ struct Volta884ThreadblockMultiplicandStoreIterator<GemmOperand::kA,
 
   /// Increments store iterator to next tile
   __device__ Volta884ThreadblockMultiplicandStoreIterator &increment(int count = 1) {
+    CUTLASS_PRAGMA_UNROLL
     for (int ptr_idx = 0; ptr_idx < kPointerCount; ++ptr_idx) {
       pointer[ptr_idx] +=
           make_Coord(VectorizedShape::kD * count, 0, 0, 0).template dot<int>(stride);
@@ -293,6 +296,7 @@ struct Volta884ThreadblockMultiplicandStoreIterator<GemmOperand::kA,
 
   /// Increments store iterator to previous tile
   __device__ Volta884ThreadblockMultiplicandStoreIterator &decrement(int count = 1) {
+    CUTLASS_PRAGMA_UNROLL 
     for (int ptr_idx = 0; ptr_idx < kPointerCount; ++ptr_idx) {
       pointer[ptr_idx] -=
           make_Coord(VectorizedShape::kD * count, 0, 0, 0).template dot<int>(stride);

tools/test/unit/gemm/volta884_gemm.cu

@@ -183,7 +183,7 @@ TEST(Volta884_f16_s884gemm_128x128x32_tt, short_480x280x224) {
 // Contiguous - s884gemm
 //
 ////////////////////////////////////////////////////////////////////////////////////////////////////
-#if 0
+
 TEST(Volta884_f16_s884gemm_64x64x32_nt, 64x64x32) {
 
   typedef cutlass::gemm::Volta884GemmTraits<
@@ -218,7 +218,6 @@ TEST(Volta884_f16_s884gemm_64x64x32_nt, 64x64x30_residue) {
   run_gemm<GemmTraits>(64, 64, 30);
 }
 
-#if 0
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
 TEST(Volta884_f16_s884gemm_64x64x32_nt, 64x64x64) {
@@ -874,7 +873,6 @@ TEST(Volta884_f16_s884gemm_128x128x32_nn, 392x264x192) {
 
   run_gemm<GemmTraits>(392, 264, 192);
 }
-#endif
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -1281,7 +1279,6 @@ TEST(Volta884_f16_s884gemm_f16_128x256x32_tn, 480x280x224) {
 
   run_gemm<GemmTraits>(480, 280, 224);
 }
-#endif
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
 #endif // if defined(CUTLASS_ENABLE_TENSOR_CORE_MMA)