CUTLASS 2.11
CUTLASS 2.11 - November 2022
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN. 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), BFloat16 (BF16), Tensor Float 32 (TF32), single-precision floating point (FP32), FP32 emulation via tensor core instruction, double-precision floating point (FP64) types, integer data types (4b and 8b), and binary data types (1b). CUTLASS demonstrates warp-synchronous matrix multiply operations targeting the programmable, high-throughput Tensor Cores implemented by NVIDIA's Volta, Turing, and Ampere architectures.
CUTLASS implements high-performance Convolution via the implicit GEMM algorithm. Implicit GEMM is the formulation of a convolution operation as a GEMM thereby taking advantage of CUTLASS's modular GEMM pipeline. This allows CUTLASS to build convolutions by reusing highly optimized warp-wide GEMM components and below.
See the Quick Start Guide to get started quickly.
See the functionality listing for the list of operations supported at each level of the execution model hierarchy.
What's New in CUTLASS 2.11
CUTLASS 2.11 is an update to CUTLASS adding:
- Stream-K, which is a new general way to do split-K. It can not only improve performance, but can also significantly reduce the number of tile sizes that need to be profiled to find the best one.
- Fused multi-head attention kernel. It has two variants: one for fixed sequence lengths, and another for variable sequence lengths.
- Dual GEMM. It can run two GEMMs that share the same left input matrix in one kernel.
- Hopper improves double precision matrix multiplication by 2x compared to Ampere at iso-clocks. It is supported since CUDA 11.8.
- BLAS3 functions with Hoppers new double precision matrix multiplication instructions.
- ELL Block Sparse GEMM.
- Optimized Group Conv.
- Optimized DepthWise Conv.
- Scripts to fuse multiple back-to-back GEMM.
- FP8 data type definition and conversion routines.
- Updates and bugfixes from the community (thanks!). Big shout out to Meta's xFormers.
- Deprecation announcement: CUTLASS plans to deprecate the following in the next major release:
- Maxwell and Pascal GPU architectures
- Ubuntu 16.04
- CUDA 10.2
See the CHANGELOG for a detailed listing of releases and updates.
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 A100, an NVIDIA A2, an NVIDIA TitanV, and an NVIDIA GeForce 2080 Ti compiled with the CUDA 11.5 Toolkit. Tensor Core operations are implemented using CUDA's mma instruction.
When using CUTLASS building blocks to construct device-wide implicit gemm (Fprop, Dgrad, and Wgrad) kernels, CUTLASS performance is also comparable to cuDNN when running Resnet-50 layers on an NVIDIA A100 as shown in the above figure. Tensor Core operations are still implemented using CUDA's mma instruction.
Compatibility
CUTLASS requires a C++11 host compiler and performs best when built with the CUDA 11.8 Toolkit.
It is also compatible with CUDA 11.x.
Operating Systems
We have tested the following environments.
Operating System | Compiler |
---|---|
Windows 10 | Microsoft Visual Studio 2015 |
Microsoft Visual Studio 2017 | |
Microsoft Visual Studio 2019 | |
Ubuntu 18.04 | GCC 7.5.0 |
Ubuntu 20.04 | GCC 10.3.0 |
Ubuntu 22.04 | GCC 11.2.0 |
Additionally, CUTLASS may be built with clang. See these instructions for more details.
Hardware
CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on any Volta-, Turing-, or NVIDIA Ampere- architecture NVIDIA GPU.
GPU | CUDA Compute Capability | Minimum CUDA Toolkit | Minimum CUDA Toolkit Enabling Native Tensor Cores |
---|---|---|---|
NVIDIA Tesla V100 | 7.0 | 9.2 | 10.1 |
NVIDIA TitanV | 7.0 | 9.2 | 10.1 |
NVIDIA GeForce RTX 2080 TI, 2080, 2070 | 7.5 | 10.0 | 10.2 |
NVIDIA Tesla T4 | 7.5 | 10.0 | 10.2 |
NVIDIA A100 | 8.0 | 11.0 | 11.0 |
NVIDIA A10 | 8.6 | 11.1 | 11.1 |
NVIDIA GeForce 3090 | 8.6 | 11.1 | 11.1 |
NVIDIA H100 PCIe | 9.0 | 11.8 | Double-precision: 11.8 |
Documentation
CUTLASS is described in the following documents and the accompanying Doxygen documentation.
- Quick Start Guide - build and run CUTLASS
- Functionality - summarizes functionality available in CUTLASS
- Efficient GEMM in CUDA - describes how GEMM kernels may be implemented efficiently in CUDA
- GEMM API - describes the CUTLASS GEMM model and C++ template concepts
- Implicit GEMM Convolution - describes 2-D and 3-D convolution in CUTLASS
- Code Organization - describes the organization and contents of the CUTLASS project
- Terminology - describes terms used in the code
- Programming Guidelines - guidelines for writing efficient modern CUDA C++
- Fundamental types - describes basic C++ classes used in CUTLASS to represent numeric quantities and arrays
- Layouts - describes layouts of matrices and tensors in memory
- Tile Iterators - describes C++ concepts for iterating over tiles of matrices in memory
- CUTLASS Profiler - command-line driven profiling application
- CUTLASS Utilities - additional templates used to facilate rapid development
Resources
We have also described the structure of an efficient GEMM in our talk at the GPU Technology Conference 2018.
- CUTLASS: Software Primitives for Dense Linear Algebra at All Levels and Scales within CUDA
- Developing CUDA Kernels to Push Tensor Cores to the Absolute Limit on NVIDIA A100
- Accelerating Convolution with Tensor Cores in CUTLASS
- Accelerating Backward Data Gradient by Increasing Tensor Core Utilization in CUTLASS
- CUTLASS: Python API, Enhancements, and NVIDIA Hopper
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, 7.5, 8.0, and 8.6. 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=80 # compiles for NVIDIA's Ampere 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)
conv/ # code specialized for convolution
epilogue/ # code specialized for the epilogue of gemm/convolution
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
thread/ # simt code that can be performed within a CUDA thread
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.
Tools
tools/
library/ # CUTLASS Instance Library - contains instantiations of all supported CUTLASS templates
include/
cutlass/
library/
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 -j16
Building all GEMM and Convolution kernels (long build times)
By default, 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.
Beware, this results in thousands of kernels and long build times.
$ cmake .. -DCUTLASS_NVCC_ARCHS=75 -DCUTLASS_LIBRARY_KERNELS=all
...
$ make cutlass_profiler -j16
Building a subset of GEMM and Convolution kernels (reduced build times)
To compile strictly one kernel or a small set of kernels, a comma-delimited list of kernel names with wildcard characters may be used to reduce the set of kernels. The following examples show building exactly one or a subset of kernels for NVIDIA Ampere and Turing architecture:
Building a subset Tensor Core GEMM kernels
To compile a subset of Tensor Core GEMM kernels with FP32 accumulation and FP16 input targetting NVIDIA Ampere and Turing architecture, use the below cmake command line:
$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*gemm_f16_*_nt_align8
...
$ make cutlass_profiler -j16
Example command line for profiling a subset of Tensor Core GEMM kernels is as follows:
./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*gemm_f16_*_nt_align8 --m=3456 --n=4096 --k=4096
...
=============================
Problem ID: 1
Provider: CUTLASS
OperationKind: gemm
Operation: cutlass_tensorop_s1688gemm_f16_256x128_32x2_nt_align8
Status: Success
Verification: ON
Disposition: Passed
reference_device: Passed
cuBLAS: Passed
Arguments: --gemm_kind=universal --m=3456 --n=4096 --k=4096 --A=f16:column --B=f16:row --C=f32:column --alpha=1 \
--beta=0 --split_k_slices=1 --batch_count=1 --op_class=tensorop --accum=f32 --cta_m=256 --cta_n=128 \
--cta_k=32 --stages=2 --warps_m=4 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=8 --min_cc=75 \
--max_cc=1024
Bytes: 118489088 bytes
FLOPs: 115992428544 flops
Runtime: 1.55948 ms
Memory: 70.7616 GiB/s
Math: 74378.8 GFLOP/s
=============================
...
Building one CUDA Core GEMM kernel
To compile one SGEMM kernel targetting NVIDIA Ampere and Turing architecture, use the below cmake command line:
$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sgemm_128x128_8x2_nn_align1
...
$ make cutlass_profiler -j16
Example command line for profiling single SGEMM CUDA kernel is as follows:
$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096
=============================
Problem ID: 1
Provider: CUTLASS
OperationKind: gemm
Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1
Status: Success
Verification: ON
Disposition: Passed
cuBLAS: Passed
Arguments: --m=3456 --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=4 \
--warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024
Bytes: 180355072 bytes
FLOPs: 115992428544 flops
Runtime: 6.73655 ms
Memory: 24.934 GiB/s
Math: 17218.4 GFLOP/s
=============================
Building a subset of Tensor Core Convolution kernels
To compile a subset of Tensor core convolution kernels implementing forward propagation (fprop) with FP32 accumulation and FP16 input targetting NVIDIA Ampere and Turing architecture, use the below cmake command line:
$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*fprop_optimized_f16
...
$ make cutlass_profiler -j16
Example command line for profiling a subset of Tensor Core convolution kernels is as follows:
$ ./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*fprop_optimized_f16 --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3
...
=============================
Problem ID: 1
Provider: CUTLASS
OperationKind: conv2d
Operation: cutlass_tensorop_s16816fprop_optimized_f16_128x128_32x5_nhwc
Status: Success
Verification: ON
Disposition: Passed
reference_device: Passed
Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \
--stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f16:nhwc --Filter=f16:nhwc --Output=f32:nhwc \
--conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \
--eq_gemm_provider=none --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=32 --stages=5 \
--warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024
Bytes: 1130659840 bytes
FLOPs: 118482796544 flops
Runtime: 0.711496 ms
Memory: 1479.99 GiB/s
Math: 166526 GFLOP/s
=============================
...
Building one Convolution CUDA kernel
To compile and run one CUDA Core convolution kernel implementing forward propagation (fprop) with F32 accumulation and FP32 input targetting NVIDIA Ampere and Turing architecture, use the below cmake command line:
$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc
...
$ make cutlass_profiler -j16
Example command line for profiling one CUDA Core convolution kernel:
$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3
=============================
Problem ID: 1
Provider: CUTLASS
OperationKind: conv2d
Operation: cutlass_simt_sfprop_optimized_128x128_8x2_nhwc
Status: Success
Verification: ON
Disposition: Passed
reference_device: Passed
Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1 \
--stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f32:nhwc --Filter=f32:nhwc --Output=f32:nhwc \
--conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 \
--eq_gemm_provider=none --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \
--warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024
Bytes: 2055798784 bytes
FLOPs: 118482796544 flops
Runtime: 7.34266 ms
Memory: 260.752 GiB/s
Math: 16136.2 GFLOP/s
=============================
More Details on Compiling CUTLASS Kernels and CUTLASS Profiler
- Please follow the links for more CMake examples on selectively compiling CUTLASS kernels:
- 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
Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. SPDX-License-Identifier: BSD-3-Clause
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