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CUTLASS 1.3

CUTLASS 1.3.1 - April 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.

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

  • 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

What's New in CUTLASS 1.1

September 2018

Performance

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.

Compatibility

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

Building CUTLASS

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.

Project Structure

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.

Performance Profiling

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

About

CUTLASS is released by NVIDIA Corporation as Open Source software under the 3-clause "New" BSD license.

Copyright

Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.

  Redistribution and use in source and binary forms, with or without modification, are permitted
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      * 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
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