triton-ascend-matmul

What problem does it solve?

Provides a structured guide to optimize matrix-multiplication kernels on Ascend hardware using Triton, enabling higher throughput and lower latency for dense compute workloads.

Core Features & Use Cases

• Targeted optimization for matmul-like operators: matmul, mm, bmm, linear, gemm, outer_product, einsum (including matrix multiply), and conv converted to matmul.
• Techniques include Cube Core utilization, tiling strategies, Swizzle optimization, and support for high-dimensional tensor shapes.
• Use Case: Apply when core compute is dominated by large matrix multiplications and you need to maximize performance on Ascend with Triton.

Quick Start

Apply this guide to optimize a matmul kernel on Ascend using Triton and validate performance on a representative workload.