Portable, High Performance Matrix Multiplication Micro-Kernels for RISC-V with ExO
Adrián Castelló, Hèctor Martínez, Sandra Catalán, Jie Lei, Yuka Ikarashi, Grace Dinh, Francisco D. Igual, Enrique S. Quintana–Ort́ı · 2025
The proliferation of RISC-V platforms and their use in a wide variety of scientific applications, including deep learning scenarios, has dramatically increased the interest to generate optimized code for them. In the field of HPC (High Performance Computing), the RISCV ISA (Instruction Set Architecture) has been adopted by a wide variety of designs with different micro-architecture; as a result, performance portability of existing codes is a major endeavor. Code generators and compilers such as Apache TVM, MLIR, or EXO provide a hardware abstraction for implementing optimized hardware-aware codes, thus reducing development time and potential errors. These generators can handle the full software stack, from basic micro-kernels to complex operations. In this work, we focus on the optimization of GEMM (general matrix-matrix multiplication), a key operation on top of which dense linear algebra libraries and deep learning frameworks are built. Specifically, we present an EXO-based GEMM microkernel generator for the RISC-V ISA with RVV vector extensions that addresses the lack of high-performance and portable GEMM micro-kernels. Our results demonstrate that, by generating a wide range of micro-kernels, one can obtain GEMM realizations that outperform those in the state-of-the-art high performance libraries.