Modern Generative Programming for Optimizing Small Matrix-Vector Multiplication
Jules Pénuchot, Joël Falcou, Amal Khabou · 2018
BLAS-level functions are the cornerstone of a large subset of applications. If a large body of work surrounding efficient and large-scale implementation of some routines such as gemv exists, the interest for small-sized, highly-optimized versions of those routines emerged. In this paper, we propose to show how a modern C++ approach based on generative programming techniques such as vectorization and loop unrolling in the framework of meta-programming can deliver efficient automatically generated codes for such routines, that are competitive with existing, hand-tuned library kernels with a very low programming effort compared to writing assembly code. In particular, we analyze the performance of automatically generated small-sized gemv kernels for both Intel x86 and ARM processors. We show through a performance comparison with the OpenBLAS gemv kernel on small matrices of sizes ranging from 4 to 32 that our C++ kernels are very efficient and may have a performance that is up to 3 times better than that of OpenBLAS gemv.