Optimization Strategies to Accelerate BLAS Operations with ARM SVE

Optimized mathematical libraries designed for specific hardware platforms are critical for achieving maximum performance in scientific and engineering applications. These libraries play a key role in accelerating computations and improving code efficiency. The Scalable Vector Extension (SVE) for the ARM architecture is a recent development that enhances vectorization capabilities, with wide vectors, leading to significant performance improvements. This paper explores vector optimizations for Basic Linear Algebra Subprograms (BLAS) routines, targeting both single and double precision data. It details the strategies for vectorizing BLAS operations using SVE. The approach is implemented with OpenBLAS, and experimental results reveal notable performance gains, demonstrating the efficacy of SVE in accelerating computational tasks on ARM platforms.