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. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS. CUTLASS decomposes these "moving parts" into reusable, modular software components abstracted by C++ template classes. These thread-wide, warp-wide, block-wide, and device-wide primitives can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications.
To support a wide variety of applications, CUTLASS provides extensive support for
mixed-precision computations, providing specialized data-movement and
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. Even faster performance on Volta is possible via direct access to
Volta Tenor Cores via mma.sync (added in CUDA 10.1).
CUTLASS 1.3 is described in the CUTLASS Documentation and the accompanying Doxygen documentation. We describe the structure of an efficient GEMM in our talk at the GPU Technology Conference 2018.
March 2019
- CUTLASS 1.3 includes an efficient GEMM implementation with the
mma.syncinstruction added in CUDA 10.1.
October 2018
- Parallelized Reductions
- Batched strided WMMA GEMM
September 2018
- CUTLASS Documentation
- Examples
- Basic GEMM, tensor views, CUTLASS utilities, batched GEMM, WMMA GEMM
- Turing Features
- WMMA GEMM targeting TensorCores - INT8, INT4, 1-bit
- Batched Strided GEMM
- Threadblock rasterization strategies
- Improved performance for adverse problem sizes and data layouts
- Extended CUTLASS Core components
- Tensor views support arbitrary matrix and tensor layouts
- Zip iterators for structuring multiple data streams
- Enhanced CUTLASS utilities
- Reference implementations for tensor operations in host and device code
- Added
HostMatrix<>for simplified matrix creation
For all updates, see the CUTLASS changelog.
CUTLASS primitives are very efficient. When used to construct device-wide GEMM kernels, they exhibit performance comparable to cuBLAS for scalar GEMM computations. The above figure shows CUTLASS performance relative to cuBLAS for large matrix dimensions (M=10240, N=K=4096) running on an NVIDIA Titan V GPU when compiled with CUDA 10.0.
CUTLASS performs best when compiled with the CUDA 10.1 Toolkit. It is also compatible with CUDA 9.0, 9.1, 9.2, and 10.0.
We have tested the following environments.
| Operating System | Compiler |
|---|---|
| Windows 10 | Microsoft Visual Studio 2015 |
| Microsoft Visual Studio 2017 | |
| Ubuntu 14.04 | GCC 4.8.2 |
| Ubuntu 16.04 | GCC 5.4.0 |
| 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-, Volta-, and Turing-architecture NVIDIA GPUs.
| GPU |
|---|
| NVIDIA GeForce 1080 |
| NVIDIA TitanXP |
| NVIDIA Tesla P100 |
| NVIDIA Tesla V100 |
| NVIDIA TitanV |
| NVIDIA GeForce RTX 2080 TI, 2080, 2070 |
CUTLASS is a header-only template library and does not need to be built to be used by other projects. However, we distribute extensive unit tests and utility programs to demonstrate CUTLASS. These instructions are for building those test programs.
CUTLASS's unit tests depend on Google Test which exists as a git submodule. You can fetch submodules as follows.
$ git submodule update --init --recursive
CUTLASS can be build with CMake starting version 3.10. By default CUTLASS will build kernels
for CUDA architecture versions 5.0, 6.0, 6.1, 7.0 and 7.5. To reduce compile time you can specify
the architectures to build CUTLASS for by changing the CMake configuration setting
CUTLASS_NVCC_ARCHS.
Create a build directory within the CUTLASS project, then run CMake once.
$ mkdir build && cd build
$ cmake ..
Compile the CUTLASS project by running Make. Include the -j argument to compile sources in parallel and speed up the build process.
$ make -j12
...
$
Verify CUTLASS has been built correctly by running the unit tests from the build/ directory.
$ ./tools/test/unit/cutlass_unit_test
...
...
...
[----------] Global test environment tear-down
[==========] 946 tests from 57 test cases ran. (10812 ms total)
[ PASSED ] 946 tests.
All tests should pass, though the exact number of tests may vary over time.
CUTLASS is arranged as a header-only library with several example test programs that demonstrate instantiating a GEMM task within a CUDA kernel. The Doxygen documentation provides a complete list of files, classes, and template concepts defined in the CUTLASS project. A brief summary is described below.
The CUTLASS library is defined in the cutlass/ directory and consists of CUDA C++ template classes and other definitions for implementing efficient GPU GEMM kernels. A set of core classes and templates define basic primitives that are then applied to compute GEMM via templates in the cutlass/gemm directory.
cutlass/
gemm/
util/
<core API components>
Several tools and test programs are also distributed with the CUTLASS library. They are contained in the following directories.
examples/
00_basic_gemm/
01_tensor_view/
02_cutlass_utilities/
03_batched_gemm/
04_tile_iterator/
05_wmma_gemm/
tools/
test/
unit/
core/
gemm/
perf/
util/
reference/
device/
host/
<utilities>
The test/unit/ directory consist of unit tests implemented with Google Test that demonstrate
basic usage of Core API components and complete tests of the CUTLASS GEMM computations.
The tools/util directory contains CUTLASS utilities including reference implementations of GEMM and
several element-wise tensor operations.
The test/perf/ directory contains a command-line utility for launching each of the GEMM kernels.
Its usage is shown below.
Program usage:
cutlass_perf_test [options]
--help
--append=<true|false*> If true, appends output to existing CSV file. If false, overwrites.
--alpha=<alpha> Value for alpha to be used in GEMM experiments
--beta=<beta> Value for beta to be used in GEMM experiments
--dist=<distribution> Describes the random distribution of each of the input matrix operands.
--execution_mode=<mode> Specifies execution mode: profile, verify, single
--output=<filename.csv> Writes summary of profiling to specified .csv file
--iterations=<timing iterations> maximum number of iterations to execute when profiling
--m=<height>[:max height[:step]] Height of GEMM problem (number of rows of C). May specify a range with optional step size.
--n=<width>[:max width[:step]] Width of GEMM problem (number of columns of C). May specify a range with optional step size.
--k=<depth>[:max depth[:step]] Size of inner dimension of A and B. May specify a range with optional step size.
--kernels=<{s|d|h|i|wmma_}gemm_{nn,nt,tn,tt}> Select GEMM datatype and layout to use for tests
--peak=<bool> If true, only reports peak performance per kernel after profiling specified problem space.
--save_workspace={*never,incorrect,always} Specifies when to save the GEMM inputs and results to the filesystem.
--seed=<seed> Random seed used by the random number generator in initializing input matrices.
--tags=<column:tag,...> Inserts leading columns in output table and uniform values for each column.
Example usage:
# Runs one problem size for all kernels
$ ./tools/test/perf/cutlass_perf_test --m=10240 --n=1024 --k=1024
# 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
CUTLASS is released by NVIDIA Corporation as Open Source software under the 3-clause "New" BSD license.
Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this list of
conditions and the following disclaimer in the documentation and/or other materials
provided with the distribution.
* Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
to endorse or promote products derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent