README > Code Organization
This document describes the layout of the CUTLASS repository. The main components are:
- CUTLASS Template Library - CUDA Templates for Linear Algebra Subroutines and Solvers (header only)
- CUTLASS Utilities - Additional templates
- CUTLASS Instance Library - instantiations of CUTLASS templates covering the design space
- CUTLASS Profiler - CUTLASS Library, Profiler, and Utilities
- Examples - SDK examples of CUTLASS Template Library and components
- Media - supporting documentation and media content
- Tests - test components for CUTLASS Template Library and tools
CUDA Templates for Linear Algebra Subroutines and Solvers is a library of CUDA C++ template classes for performing efficient matrix computations on NVIDIA GPUs.
Like NVIDIA CUB, the components of CUTLASS are organized hierarchically based on the scope of cooperative elements. For example, warp-level GEMM components perform a matrix multiply collectively by the set of threads within a warp. The following figure illustrates each layer.
Components are designed to be usable by client applications accessing functionailty at each scope.
CUTLASS Templates are implemented by header files in the following directory structure:
include/ # Top-level include directory. Client applications should target this path.
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
thread/ # thread-level operators
warp/ # warp-level operators
threadblock/ # CTA-level operators
kernel/ # CUDA kernel entry points
device/ # launches kernel(s) over a full device
* # scope-agnostic components and basic vocabular type definitions for GEMM
layout/ # layout definitions for matrices, tensors, and other mathematical objects in memory
*
reduction/ # bandwidth-limited reduction kernels that do not fit the "gemm" models
thread/ # thread-level operators
warp/ # warp-level operators
threadblock/ # CTA-level operators
kernel/ # CUDA kernel entry points
device/ # launches kernel(s) over a full device
* # scope-agnostic components and basic vocabular type definitions
transform/ # code specialized for layout, type, and domain transformations
thread/ # thread-level operators
warp/ # warp-level operators
threadblock/ # CTA-level operators
kernel/ # CUDA kernel entry points
device/ # launches kernel(s) over a full device
* # scope-agnostic components and basic vocabulary type definitions
util/ # miscellaneous CUTLASS components
*
* # core vocabulary types and fundamental arithmetic operators
See Programming Guidelines for further details about conventions and design patterns used throughout CUTLASS.
The tools/ directory contains clients of the CUTLASS Template library and includes the following.
The CUTLASS Instance Library contains instantiations of the above CUTLASS templates covering supported configurations, data types, block structure, and tile sizes. These instantiations are procedurally generated using a set of scripts to span the design space.
tools/
library/ # static/dynamic library containing all kernel instantiations of interest
# (with some build-level filter switches to compile specific subsets)
include/
cutlass/
library/ # header files for CUTLASS Deliverables Library (in cutlass::library:: namespace)
handle.h # implements a host-side API for launching kernels, similar to cuBLAS
library.h # defines enums and structs to describe the tiled structure of operator instances
manifest.h # collection of all instances
scripts/ # scripts to procedurally generate CUTLASS template instances
gemm_operations.py
library.py
generator.py # entry point of procedural generation scripts - invoked by cmake
manifest.py
src/
When CMake is executed, the CUTLASS Instance Library generator scripts are executed to construct a set of
instantiations in build/tools/library/generated/.
The CUTLASS Profiler is designed to load the CUTLASS Instance Library and execute all operations contained therein. This command-line driven application constructs an execution environment for evaluating functionality and performance. It is implemented in
tools/
profiler/
and may be built as follows.
$ make cutlass_profiler -j
Further details about the CUTLASS Profiler are described here.
tools/util/ defines a companion library of headers and sources that support the CUTLASS test programs, examples, and other client applications. Its structure is as follows:
tools/
util/
include/
cutlass/
util/ # CUTLASS Utility companion library
reference/ # functional reference implementation of CUTLASS operators
# (minimal consideration for performance)
detail/
*
device/ # device-side reference implementations of CUTLASS operators
thread/
kernel/
*
host/ # host-side reference implementations of CUTLASS operators
*
*
More details about CUTLASS Utilities may be found here.
To demonstrate CUTLASS components, several SDK examples are implemented in 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
10_planar_complex/ # example demonstrating planar complex GEMM kernels
11_planar_complex_array/ # example demonstrating planar complex kernels with batch-specific problem sizes
12_gemm_bias_relu/ # example demonstrating GEMM fused with bias and relu
13_fused_two_gemms/ # example demonstrating two GEMms fused in one kernel
This directory contains documentation, images, and performance result data which accompanies the CUTLASS library and components.
Test programs for CUTLASS. Tests are organized hierarchically, mirroring the organization of source files.
test/ # unit tests for CUTLASS Template Library
unit/
arch/
core/
gemm/
device/
kernel/
thread/
threadblock/
warp/
reduction/
kernel/
thread/
transform/
threadblock/
*
Tests can be built and run at the top level scope by invoking make test_unit or by building
and explicitly executing each individual target, e.g. cutlass_test_unit_gemm_device.
Tests are configured to specify appropriate GTest filter strings to avoid running except on architectures where they are expected to pass. Thus, no tests should fail. The actual number of tests run may vary over time as more are added.
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