Code Organization — NVIDIA CUTLASS Documentation (original) (raw)

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This document describes the layout of the CUTLASS repository. The main components are:

CUTLASS Template Library#

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
  collective/            #   3.x API operators for all threads a tiled mma/copy are built over
  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 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 vocabulary 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

cute / # CuTe Layout, layout algebra, MMA/Copy atoms, tiled MMA/Copy algorithm/ # Definitions of core operations such as copy, gemm, and operations on cute::tuples arch/ # Bare bones PTX wrapper structs for copy and math instructions atom/ # Meta-information either link to or built from arch/ operators mma_atom.hpp # cute::Mma_Atom and cute::TiledMma copy_atom.hpp # cute::Copy_Atom and cute::TiledCopy sm.hpp # Arch specific meta-information for copy and math operations container/ # Core container types used across CuTe, namely, cute::tuple numeric/ # CuTe's internal numerics implementation * # Core library types such as Shape, Stride, Layout, Tensor, and associated operations

See Programming Guidelines for further details about conventions and design patterns used throughout CUTLASS.

CuTe#

CuTe is a collection of C++ CUDA template abstractions for defining and operating on hierarchically multidimensional layouts of threads and data. CuTe provides Layout and Tensor objects that compactly packages the type, shape, memory space, and layout of data, while performing the complicated indexing for the user. This lets programmers focus on the logical descriptions of their algorithms while CuTe does the mechanical bookkeeping for them. With these tools, we can quickly design, implement, and modify all dense linear algebra operations. More documentation for CuTe can be found in cute/.

Tools#

The tools/ directory contains clients of the CUTLASS Template library and includes the following.

CUTLASS Instance Library#

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

src/

python/ cutlass_library/ # 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

When CMake is executed, the CUTLASS Instance Library generator scripts are executed to construct a set of instantiations in build/tools/library/generated/.

CUTLASS Profiler#

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

and may be built as follows.

$ make cutlass_profiler -j

Further details about the CUTLASS Profiler are described here.

CUTLASS Utilities#

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.

Examples#

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 activation function

13_fused_two_gemms/ # example demonstrating two GEMMs fused into one kernel

Media#

This directory contains documentation, images, and performance result data which accompanies the CUTLASS library and components.

Tests#

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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