![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "CUTLASS Code Organization")

[README](/README.md#documentation) > **Code Organization**

# CUTLASS 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)
* **CuTe Template Library** - CUTLASS's core vocabulary layout type and associated algebra (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

## 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](/media/docs/programming_guidelines.md) 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 [`/media/docs/cute/`](/media/docs/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
```
tools/
  profiler/
```

and may be built as follows.
```
$ make cutlass_profiler -j
```

[Further details about the CUTLASS Profiler are described here.](/media/docs/profiler.md)

### 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.](/media/docs/utilities.md)


## 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.

# Copyright

Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: BSD-3-Clause

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