NVIDIA CUTLASS is a comprehensive C++ template library and Python DSL for implementing high-performance CUDA kernels, primarily focused on matrix multiply (GEMM), convolution, and attention operations. It provides a layered architecture from low-level warp matrix multiply-accumulate (MMA) and copy instructions (CuTe atoms) up through threadblock-level collective mainloops and epilogues, kernel-level schedulers (persistent, stream-K, warp-specialized), and a high-level operator API for runtime dispatch with fusion support. The repository includes a Python-embedded DSL (CuTeDSL) that uses MLIR-based compilation to generate optimized CUDA kernels, a separate Python code generation backend (cutlass_cppgen) for JIT compilation, and an extensive profiling and testing infrastructure. It spans NVIDIA GPU architectures from Volta to Blackwell and is actively maintained with contributions to deep learning frameworks like PyTorch and JAX.
This repository implements `cuda_tile`, an MLIR-based compiler dialect and toolchain for GPU tile programming, primarily targeting NVIDIA CUDA architectures. It defines a rich set of operations for tile-level computation (arithmetic, matrix multiply, memory views, atomics, control flow) with explicit memory ordering and synchronization. The project includes a custom bytecode serialization format (.tileirbc) with versioned backward compatibility, allowing efficient IR transport and JIT compilation. Key tools are `cuda-tile-opt` (dialect testing), `cuda-tile-translate` (MLIR-to-bytecode conversion), `cuda-tile-optimize` (bytecode-to-bytecode optimization passes), and `cuda-tile-tblgen` (TableGen-based code generation for bytecode readers/writers). Python bindings enable programmatic IR construction. The architecture is a set of modular libraries (dialect, bytecode, optimizer, C API) that can be integrated into larger compiler pipelines. External dependencies are LLVM/MLIR at a pinned commit and CUDA. The codebase demonstrates high engineering rigor with extensive test coverage (bytecode stability, version compatibility, round-trip, transformation passes) and careful version management for evolving the IR format. While primarily a compiler infrastructure application, the project's focus on low-precision matrix operations (FP8, FP4) and tensor core optimizations touches on ML research areas.