| .. _overview: | |
| Overview | |
| =========================== | |
| CUTLASS 4.x bridges the gap between productivity and performance for CUDA kernel development. | |
| By providing Python-based DSLs to the powerful CUTLASS C++ template library, it enables | |
| faster iteration, easier prototyping, and a gentler learning curve for high-performance linear | |
| algebra on NVIDIA GPUs. | |
| Overall we envision CUTLASS DSLs as a family of domain-specific languages (DSLs). | |
| With the release of 4.0, we are releasing the first of these in CuTe DSL. | |
| This is a low level programming model that is fully consistent with CuTe C++ abstractions — exposing | |
| core concepts such as layouts, tensors, hardware atoms, and full control over the hardware thread and data hierarchy. | |
| Why CUTLASS DSLs? | |
| ============================ | |
| While CUTLASS offers exceptional performance through its C++ template abstractions, the complexity | |
| can present challenges for many developers. CUTLASS 4.x addresses this by: | |
| - **Simplifying metaprogramming**: Metaprogramming in Python is a lot more intuitive than with C++ | |
| - **Accelerating Iteration**: Rapid prototyping with familiar Python syntax and blazing fast compile times | |
| - **Lowering Barriers**: Reduced learning curve for GPU programming concepts and consistency between CuTe C++ and DSL | |
| - **Maintaining Performance**: Generated code leverages optimized CUTLASS primitives | |
| 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 DSLs translate Python code into a custom intermediate representation (IR), | |
| which is then Just-In-Time (JIT) compiled into optimized CUDA kernels using MLIR and `ptxas`. | |
| Core CuTe DSL Abstractions | |
| ----------------------------------- | |
| - **Layouts** – Describe how data is organized in memory and across threads. | |
| - **Tensors** – Combine data pointers or iterators with layout metadata. | |
| - **Atoms** – Represent fundamental hardware operations like matrix multiply-accumulate (MMA) or memory copy. | |
| - **Tiled Operations** – Define how atoms are applied across thread blocks and warps (e.g., ``TiledMma``, ``TiledCopy``). | |
| For more on CuTe abstractions, refer to the `CuTe C++ library documentation <https://github.com/NVIDIA/cutlass/blob/main/media/docs/cpp/cute/00_quickstart.md>`__. | |
| **Pythonic Kernel Expression** | |
| Developers express kernel logic, data movement, and computation using familiar Python syntax and control flow. | |
| The DSLs simplify expressing loop tiling, threading strategies, and data transformations using concise Python code. | |
| **JIT Compilation** | |
| Python kernels are compiled at runtime into CUDA device code using MLIR infrastructure and NVIDIA’s ``ptxas`` toolchain, | |
| enabling rapid iteration and interactive debugging. | |
| Relationship to CUTLASS C++ | |
| ================================= | |
| 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 | |
| authoring framework that shares all concepts with CUTLASS 3.x C++ API such as CuTe, pipelines, schedulers etc. | |
| - **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**: The CUTLASS DSLs do not currently ship with a full GEMM/Conv autotuning profiler or library interface | |
| 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. | |
| Getting Started | |
| ================================ | |
| - :doc:`quick_start` – Initial setup and installation. | |
| - :doc:`cute_dsl` – Overview of the typical development and workflow using CuTe DSL. | |
| - :doc:`cute_dsl_api` – Refer to the full API documentation. | |
| - :doc:`limitations` – Understand current CuTe DSL constraints and differences from C++. | |
| - :doc:`faqs` – Common questions and known issues. | |
| Current Status & Roadmap | |
| ================================= | |
| CuTe DSL is in public beta and actively evolving. Interfaces and features are subject to | |
| change as we improve the system. | |
| Upcoming Milestones | |
| ---------------------------------- | |
| - Public release targeted for **Summer 2025** | |
| - Expanded support for additional data types and kernel types | |
| - Usability improvements: better error messages, debugging tools, and streamlined APIs | |
| - Broader integration of CUTLASS primitives and features | |
| For known issues and workarounds, please consult the :doc:`limitations` and :doc:`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 <https://github.com/NVIDIA/cutlass/issues>`__ | |
| - Join the CUTLASS community on `Discord <https://discord.com/channels/1019361803752456192/1150868614921064590>`__ 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! |