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CUTLASS 2.0

CUTLASS 2.0 - November 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 half-precision floating point (FP16), single-precision floating point (FP32), double-precision floating point (FP64) types, integer data types (4b and 8b), and binary data types (1b). Furthermore, CUTLASS demonstrates warp-synchronous matrix multiply operations for targeting the programmable, high-throughput Tensor Cores implemented by NVIDIA's Volta and Turing architectures.

See the Quick Start Guide to get started quickly.

What's New in CUTLASS 2.0

CUTLASS 2.0 is a substantial refactoring from the previous version, intended to offer:

  • Better performance over 1.x, particularly for kernels targeting Turing Tensor Cores
  • Robust and durable templates that reliably span the design space
  • Encapsulated functionality that may be reusable in other contexts

See the CHANGELOG for more details.

See the functionality listing for the list of operations supported at each level of the execution model hierarchy.

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 on an NVIDIA GeForce 2080 Ti and an NVIDIA TitanV using CUDA 10.2. Tensor Core operations are implemented using CUDA's mma instruction.

Compatibility

CUTLASS requires a C++11 host compiler and performs best when built with the CUDA 10.2 Toolkit. It is compatible with CUDA 9.2, CUDA 10.0, and CUDA 10.1.

We have tested the following environments.

|Operating System | Compiler | |-----------------|----------| | Windows 10 | Microsoft Visual Studio 2015| | | Microsoft Visual Studio 2017| | Ubuntu 16.04 | GCC 5.4.0 | | Ubuntu 18.04 | GCC 7.3.0 |

Additionally, CUTLASS may be built with clang. See these instructions for more details.

CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on any Maxwell-, Pascal-, Volta-, or Turing- architecture NVIDIA GPU.

|GPU|Minimum CUDA Toolkit|CUDA Toolkit Enabling Native Tensor Cores| |---|---|---| |NVIDIA GeForce 1080|9.2| | |NVIDIA TitanXP|9.2| | |NVIDIA Tesla P100|9.2| | |NVIDIA Tesla V100|9.2|10.1| |NVIDIA TitanV|9.2|10.1| |NVIDIA GeForce RTX 2080 TI, 2080, 2070|10.0|10.2| |NVIDIA Tesla T4|10.0|10.2|

Documentation

CUTLASS 2.0 is described in the following documents and the accompanying Doxygen documentation.

We have also described the structure of an efficient GEMM in our talk at the GPU Technology Conference 2018.

Building CUTLASS

CUTLASS is a header-only template library and does not need to be built to be used by other projects. Client applications should target CUTLASS's include/ directory in their include paths.

CUTLASS unit tests, examples, and utilities can be build with CMake starting version 3.12. Make sure the CUDACXX environment variable points to NVCC in the CUDA Toolkit installed on your system.

$ export CUDACXX=${CUDA_INSTALL_PATH}/bin/nvcc

Create a build directory within the CUTLASS project, then run CMake. 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.

$ mkdir build && cd build

$ cmake .. -DCUTLASS_NVCC_ARCHS=75               # compiles for NVIDIA's Turing GPU architecture

From the build/ directory, compile and run the CUTLASS unit tests by building the target test_unit with make.

The unit tests are organized as several binaries mirroring the top-level namespaces of CUTLASS, and they may be executed in parallel via make's -j command line argument.

$ make test_unit -j
...
...
...
[----------] Global test environment tear-down
[==========] 946 tests from 57 test cases ran. (10812 ms total)
[  PASSED  ] 946 tests.

All tests should pass on supported platforms, though the exact number of tests may vary over time.

Project Structure

CUTLASS is arranged as a header-only library along with Utilities, Tools, Examples, and unit tests. Doxygen documentation provides a complete list of files, classes, and template concepts defined in the CUTLASS project.

A detailed explanation of the source code organization may be found in the CUTLASS documentation, but several main components are summarized below.

CUTLASS Template Library

include/                     # client applications should target this directory in their build's include paths

  cutlass/                   # CUDA Templates for Linear Algebra Subroutines and Solvers - headers only

    arch/                    # direct exposure of architecture features (including instruction-level GEMMs)

    gemm/                    # code specialized for general matrix product computations

    layout/                  # layout definitions for matrices, tensors, and other mathematical objects in memory

    platform/                # CUDA-capable Standard Library components

    reduction/               # bandwidth-limited reduction kernels that do not fit the "gemm" model

    transform/               # code specialized for layout, type, and domain transformations

    *                        # core vocabulary types, containers, and basic numeric operations

CUTLASS SDK Examples

CUTLASS SDK examples apply CUTLASS templates to implement basic computations.

examples/
  00_basic_gemm/             # launches a basic GEMM with single precision inputs and outputs

  01_cutlass_utilities/      # demonstrates CUTLASS Utilities for allocating and initializing tensors

  02_dump_reg_smem/          # debugging utilities for printing register and shared memory contents

  03_visualize_layout/       # utility for visualizing all layout functions in CUTLASS

  04_tile_iterator/          # example demonstrating an iterator over tiles in memory

  05_batched_gemm/           # example demonstrating CUTLASS's batched strided GEMM operation

  06_splitK_gemm/            # exmaple demonstrating CUTLASS's Split-K parallel reduction kernel

  07_volta_tensorop_gemm/    # example demonstrating mixed precision GEMM using Volta Tensor Cores

  08_turing_tensorop_gemm/   # example demonstrating integer GEMM using Turing Tensor Cores

Tools

tools/
  library/                   # CUTLASS Instance Library - contains instantiations of all supported CUTLASS templates

  profiler/                  # CUTLASS Profiler         - command-line utility for executing operations in the
                             #                            CUTLASS Library

  util/                      # CUTLASS Utilities        - contains numerous helper classes for
    include/                 #                            manging tensors in device memory, reference
      cutlass/               #                            implementations for GEMM, random initialization
        util/                #                            of tensors, and I/O.

Test

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.

Instructions for building and running the Unit tests are described in the Quickstart guide.

Performance Profiling

The tools/profiler/ directory contains a command-line utility for launching each of the GEMM kernels. It can be built as follows:

$ make cutlass_profiler -j

To limit compilation time, only one tile size is instantiated for each data type, math instruction, and layout. To instantiate all, set the following environment variable when running CMake from an empty build/ directory.

$ cmake .. -DCUTLASS_NVCC_ARCHS=75 -DCUTLASS_LIBRARY_KERNELS=all
...
$ make cutlass_profiler -j

Example command line for profiling SGEMM kernels is as follows:

$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=4352 --n=4096 --k=4096

=============================
  Problem ID: 1

    Provider: CUTLASS
   Operation: cutlass_simt_sgemm_128x128_nn

 Disposition: Passed
      Status: Success

   Arguments:  --m=4352 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0  \
               --split_k_slices=1 --batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8  \
               --stages=2 --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50  \
               --max_cc=1024

       Bytes: 52428800  bytes
       FLOPs: 146064539648  flops

     Runtime: 10.5424  ms
      Memory: 4.63158 GiB/s

        Math: 13854.9 GFLOP/s

Further details about the CUTLASS Profiler are described here.

About

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

Contributors

The official list of CUTLASS developers and contributors is available here: CONTRIBUTORS.

Copyright

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