Overview#

CUTLASS Python is the Python kernel-authoring stack in CUTLASS 4.x. It provides the CUTLASS DSL package and documentation for writing high-performance CUDA kernels with Python syntax while retaining explicit control over the GPU memory, thread, and data hierarchy.

CUTLASS Python is organized as layered kernel-authoring APIs. Basic data types provide typed building blocks. Above those, CuTe DSL provides a Python programming model aligned with CuTe C++ concepts such as layouts, tensors, copy atoms, MMA atoms, and tiled operations, while pipeline APIs compose asynchronous data movement and work orchestration. These layers compile Python kernel code through the CUTLASS Python compiler stack and NVIDIA CUDA toolchain to JIT-compile CUDA kernels.

The CUTLASS Python documentation is organized around:

Why CUTLASS Python?#

While CUTLASS offers exceptional performance through its C++ template abstractions, the complexity can present challenges for many developers. CUTLASS Python addresses this by:

  • Simplifying metaprogramming: express compile-time configuration with Python instead of deeply nested C++ templates.

  • Accelerating iteration: prototype kernels with familiar Python syntax and JIT compilation.

  • Lowering barriers: learn GPU programming concepts through APIs that mirror CuTe C++ abstractions.

  • Maintaining performance focus: generated code uses CUDA hardware primitives exposed through CUTLASS and CuTe APIs.

Students can learn GPU programming concepts without the complexity of C++ templates. Researchers and performance engineers can rapidly explore algorithms, prototype, and tune kernels before moving to production implementations.

Key Concepts and Approach#

CUTLASS Python lets developers describe CUDA kernels with Python syntax while retaining explicit control over data types, memory hierarchy, tiling, and work decomposition. Kernel code is compiled just-in-time through the CUTLASS Python compiler stack and NVIDIA CUDA toolchain.

Core Abstractions#

  • Basic data types – Provide typed numeric, pointer, vector, and array building blocks used across the DSL.

  • CuTe – Provides composable abstractions for tensor layout, data movement, and compute.

    • Tensor – Basic CuTe data object that combines a pointer or iterator with layout metadata.

    • Layout – Describes how data is organized in memory and across threads.

    • Atom – Represents a fundamental hardware operation such as matrix multiply-accumulate (MMA) or memory copy.

    • Tiled operation – Applies atoms across thread blocks and warps, for example TiledMma and TiledCopy.

  • Pipelines – Coordinate asynchronous copies, barriers, compute work, and producer/consumer warp groups.

For more on CuTe abstractions, refer to the CuTe C++ library documentation.

Pythonic Kernel Expression#

Developers express kernel logic, data movement, and computation using familiar Python syntax and control flow. The APIs make tiling, threading strategies, and data transformations explicit without requiring C++ template metaprogramming.

JIT Compilation#

Python kernels are compiled at runtime into CUDA device code through the CUTLASS Python compiler stack and CUDA toolchain, enabling rapid iteration and interactive debugging.

Supported Features#

For exact dependency versions, driver requirements, and installation commands, refer to the Quick Start Guide section.

Platform Support#

  • Linux on x86_64 and aarch64.

  • Python and CUDA Toolkit versions listed in Quick Start Guide.

Kernel Authoring Capabilities#

  • Python JIT functions and kernels with explicit static and dynamic values.

  • Basic data types for typed values, pointers, and arrays.

  • CuTe layout algebra, tensors, tiled copies, tiled MMA, and register/shared/global memory views.

  • Architecture-specific GPU operations for warp-level, warpgroup-level, and Blackwell tcgen05 programming.

  • Pipeline APIs for producer/consumer and warp-specialized kernels.

  • Integration guides for PyTorch, TVM FFI, notebooks, IKET profiling, autotuning, and ahead-of-time compilation.

Architecture Coverage#

  • NVIDIA Ampere and Ada: warp-level MMA programming.

  • NVIDIA Hopper: warpgroup MMA programming and TMA-oriented kernels.

  • NVIDIA Blackwell: tcgen05 MMA programming, TMEM-oriented kernels, and Blackwell-specific primitives.

For current constraints and unsupported features, refer to the Limitations section.

Relationship to CUTLASS C++#

CUTLASS Python is not a replacement for the CUTLASS C++ library or its 2.x and 3.x APIs. Instead, it is a high-productivity kernel authoring framework that shares concepts with CUTLASS C++, including CuTe, pipelines, schedulers, tiled copy, and tiled MMA.

  • Performance: Generated kernels aim to match CUTLASS C++ kernels in performance; however, some performance gaps may exist due to missing optimizations that have been added over the years to CUTLASS C++ and may be missing in the DSLs examples.

  • Library scope: CUTLASS Python focuses on authoring and tuning individual kernel instances. It does not currently provide the full GEMM/Conv profiler or library interface available in CUTLASS C++.

Getting Started#

Use this path when learning CUTLASS Python:

  • Start with Quick Start Guide to install dependencies and verify the environment.

  • Read DSL Programming Model for the DSL programming model, including JIT functions, kernels, static values, control flow, and argument handling.

  • Use Guides for task-focused workflows such as framework integration, debugging, profiling, autotuning, AOT compilation, and MMA programming.

  • Use API Reference as the reference for Basic Data Types, CuTe, Pipeline, and Utilities.

  • Check Limitations, Deprecation Policy, and FAQs for support boundaries, compatibility policy, common questions, and known issues.

Current Status#

CUTLASS Python is actively evolving with the CUTLASS 4.x releases. Interfaces that are experimental or subject to change are documented through the Deprecation Policy policy, API reference, and limitations pages. For known issues and workarounds, consult Limitations and FAQs.

Community & Feedback#

We welcome contributions and feedback from the developer community!

You can:

  • Submit bug reports or feature requests via our GitHub Issues page

  • Join the CUTLASS community on Discord to ask questions and share ideas

  • Contribute examples, tutorials, or enhancements to the DSLs

  • Report unclear or missing documentation

  • Propose support for additional data types or kernel variants

  • Help prioritize roadmap features by upvoting GitHub issues

Thank you for helping shape the future of CUTLASS DSLs!