Overview

Tile Language (tile-lang) is a powerful domain-specific language (DSL) and compiler infrastructure designed to simplify the development of high-performance kernels for GPUs, CPUs, and other accelerators. Built on top of TVM, tile-lang empowers developers to achieve state-of-the-art performance by focusing on productivity without compromising on low-level hardware optimizations.

Key Features

• Pythonic Syntax: Offers a familiar and concise programming model, making kernel development more accessible.
• High-Performance Kernels: Optimized for computationally intensive operations such as GEMM, Dequant GEMM, FlashAttention, and LinearAttention.
• Multi-Backend Support: Seamlessly compiles for a wide range of hardware, including NVIDIA CUDA, AMD ROCm/HIP, Huawei Ascend NPUs, and Apple Metal. Experimental support extends to LLVM CPU and WebGPU.
• Advanced Optimization Techniques: Leverages techniques like tiling, shared memory usage, TMA (Tensor Memory Access), and MMA (Matrix Multiply-Accumulate) instructions for maximum efficiency.
• Compiler Infrastructure: Integrates with TVM for robust compilation and optimization.
• Language Server Protocol (LSP): Enhanced developer experience with features like inlay hints, hover details, and precise diagnostics through an open-sourced LSP implementation.
• Modular Backend Architecture: Facilitates the integration of new hardware targets and simplifies porting efforts.

Typical Use Cases

• Accelerating Machine Learning Workloads: Develop highly optimized kernels for core deep learning operations like matrix multiplication and attention mechanisms, crucial for training and inference.
• High-Performance Computing (HPC): Create efficient numerical computation kernels for scientific simulations and data analysis tasks on accelerators.
• Custom Hardware Acceleration: Enable developers to harness the full potential of specialized hardware like NPUs and custom ASICs by providing a flexible DSL.
• Research and Development: Rapidly prototype and benchmark new algorithms and hardware-specific optimizations for cutting-edge AI models.
• Optimizing Existing Libraries: Improve the performance of computationally bound routines within larger software systems.