Release v4.0.0 (#2294)
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Kihiro Bando
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.. _overview:
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Overview
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===========================
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CUTLASS 4.x bridges the gap between productivity and performance for CUDA kernel development.
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By providing Python-based DSLs to the powerful CUTLASS C++ template library, it enables
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faster iteration, easier prototyping, and a gentler learning curve for high-performance linear
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algebra on NVIDIA GPUs.
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Overall we envision CUTLASS DSLs as a family of domain-specific languages (DSLs).
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With the release of 4.0, we are releasing the first of these in CuTe DSL.
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This is a low level programming model that is fully consistent with CuTe C++ abstractions — exposing
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core concepts such as layouts, tensors, hardware atoms, and full control over the hardware thread and data hierarchy.
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Why CUTLASS DSLs?
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============================
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While CUTLASS offers exceptional performance through its C++ template abstractions, the complexity
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can present challenges for many developers. CUTLASS 4.x addresses this by:
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- **Simplifying metaprogramming**: Metaprogramming in Python is a lot more intuitive than with C++
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- **Accelerating Iteration**: Rapid prototyping with familiar Python syntax and blazing fast compile times
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- **Lowering Barriers**: Reduced learning curve for GPU programming concepts and consistency between CuTe C++ and DSL
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- **Maintaining Performance**: Generated code leverages optimized CUTLASS primitives
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Students can learn GPU programming concepts without the complexity of C++ templates.
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Researchers and performance engineers can rapidly explore algorithms, prototype, and tune
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kernels before moving to production implementations.
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Key Concepts and Approach
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================================
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CUTLASS DSLs translate Python code into a custom intermediate representation (IR),
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which is then Just-In-Time (JIT) compiled into optimized CUDA kernels using MLIR and `ptxas`.
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Core CuTe DSL Abstractions
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-----------------------------------
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- **Layouts** – Describe how data is organized in memory and across threads.
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- **Tensors** – Combine data pointers or iterators with layout metadata.
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- **Atoms** – Represent fundamental hardware operations like matrix multiply-accumulate (MMA) or memory copy.
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- **Tiled Operations** – Define how atoms are applied across thread blocks and warps (e.g., ``TiledMma``, ``TiledCopy``).
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For more on CuTe abstractions, refer to the `CuTe C++ library documentation <https://github.com/NVIDIA/cutlass/blob/main/media/docs/cute/00_quickstart.md>`__.
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**Pythonic Kernel Expression**
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Developers express kernel logic, data movement, and computation using familiar Python syntax and control flow.
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The DSLs simplify expressing loop tiling, threading strategies, and data transformations using concise Python code.
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**JIT Compilation**
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Python kernels are compiled at runtime into CUDA device code using MLIR infrastructure and NVIDIA’s ``ptxas`` toolchain,
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enabling rapid iteration and interactive debugging.
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Relationship to CUTLASS C++
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=================================
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CUTLASS DSLs are not a replacement for the CUTLASS C++ library or its 2.x and 3.x APIs. Instead, it aims to be a high-productivity kernel
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authoring framework that shares all concepts with CUTLASS 3.x C++ API such as CuTe, pipelines, schedulers etc.
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- **Performance**: Generated kernels aim to match CUTLASS C++ kernels in performance; however, some performance gaps
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may exist due to missing optimizations that have been added over the years to CUTLASS C++ and may be missing in the DSLs examples.
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- **Library**: The CUTLASS DSLs do not currently ship with a full GEMM/Conv autotuning profiler or library interface
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akin to CUTLASS C++. Instead, it focuses on generating and autotuning individual kernel instances (for example: via tile size exploration) and via native integration DL frameworks that support auto-tuning.
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Getting Started
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================================
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- :doc:`quick_start` – Initial setup and installation.
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- :doc:`cute_dsl` – Overview of the typical development and workflow using CuTe DSL.
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- :doc:`cute_dsl_api` – Refer to the full API documentation.
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- :doc:`limitations` – Understand current CuTe DSL constraints and differences from C++.
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- :doc:`faqs` – Common questions and known issues.
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Current Status & Roadmap
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=================================
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CuTe DSL is in public beta and actively evolving. Interfaces and features are subject to
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change as we improve the system.
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Upcoming Milestones
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----------------------------------
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- Public release targeted for **Summer 2025**
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- Expanded support for additional data types and kernel types
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- Usability improvements: better error messages, debugging tools, and streamlined APIs
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- Broader integration of CUTLASS primitives and features
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For known issues and workarounds, please consult the :doc:`limitations` and :doc:`faqs`.
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Community & Feedback
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==================================
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We welcome contributions and feedback from the developer community!
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You can:
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- Submit bug reports or feature requests via our `GitHub Issues page <https://github.com/NVIDIA/cutlass/issues>`__
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- Join the CUTLASS community on `Discord <https://discord.com/channels/1019361803752456192/1150868614921064590>`__ to ask questions and share ideas
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- Contribute examples, tutorials, or enhancements to the DSLs
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- Report unclear or missing documentation
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- Propose support for additional data types or kernel variants
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- Help prioritize roadmap features by upvoting GitHub issues
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Thank you for helping shape the future of CUTLASS DSLs!
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