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Cutlass 1.3 Release (#42)

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CUTLASS 1.3 Release
- Efficient GEMM kernel targeting Volta Tensor Cores via mma.sync instruction added in CUDA 10.1.
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Andrew Kerr
2019-03-20 10:49:17 -07:00
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README.md
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![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
# CUTLASS 1.2
# CUTLASS 1.3
_CUTLASS 1.2 - October 2018_
_CUTLASS 1.3.0 - March 2019_
CUTLASS is a collection of CUDA C++ template abstractions for implementing
high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA.
@@ -20,13 +20,18 @@ multiply-accumulate abstractions for 8-bit integer, half-precision floating
point (FP16), single-precision floating point (FP32), and double-precision floating
point (FP64) types. Furthermore, CUTLASS demonstrates CUDA's WMMA API for targeting
the programmable, high-throughput _Tensor Cores_ provided by NVIDIA's Volta architecture
and beyond.
and beyond. Even faster performance on Volta is possible via direct access to
Volta Tenor Cores via `mma.sync` (added in CUDA 10.1).
CUTLASS 1.2 is described in the [CUTLASS Documentation](CUTLASS.md) and the accompanying
CUTLASS 1.3 is described in the [CUTLASS Documentation](CUTLASS.md) and the accompanying
[Doxygen documentation](https://nvidia.github.io/cutlass).
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
_March 2019_
* CUTLASS 1.3 includes an efficient GEMM implementation with the `mma.sync` instruction added in CUDA 10.1.
# What's New in CUTLASS 1.2
_October 2018_
* [Parallelized Reductions](CUTLASS.md#parallel-reductions-across-gemm-k)
@@ -63,8 +68,8 @@ when compiled with CUDA 10.0.
# Compatibility
CUTLASS performs best when compiled with the [CUDA 10.0 Toolkit](ttps://developer.nvidia.com/cuda-toolkit).
It is compatible with CUDA 9.0, 9.1, and 9.2, but these versions of the CUDA Toolkit do not support new Turing WMMA features.
CUTLASS performs best when compiled with the [CUDA 10.1 Toolkit](ttps://developer.nvidia.com/cuda-toolkit).
It is also compatible with CUDA 9.0, 9.1, 9.2, and 10.0.
We have tested the following environments.
@@ -77,7 +82,7 @@ We have tested the following environments.
| Ubuntu 18.04 | GCC 7.3.0 |
CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on
any Maxwell-, Pascal-, or Volta-architecture NVIDIA GPU.
any Maxwell-, Pascal-, Volta-, and Turing-architecture NVIDIA GPUs.
|**GPU**|
|---|
@@ -220,6 +225,9 @@ Program usage:
# Varies GEMM K dimension for SGEMM and IGEMM with column-major multiplicands
$ ./tools/test/perf/cutlass_perf_test --m=10240 --n=4096 --k=1024:8192:128 --kernels=sgemm_nn,igemm_nn
# Executes GEMM kernel on Volta Tensor Cores
$ ./tools/test/perf/cutlass_perf_test --kernels=s884gemm_nt
```
# About
@@ -230,7 +238,7 @@ CUTLASS is released by NVIDIA Corporation as Open Source software under the
# Copyright
Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
```
Redistribution and use in source and binary forms, with or without modification, are permitted
@@ -253,4 +261,3 @@ Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
```