Performance and Power Analysis of a Vector Computing System

Abstract

The performance of recent computing systems has drastically improved due to the increase in the number of cores. However, this approach is reaching the limitation due to the power constraints of facilities. Instead, this paper focuses on a vector processing with long vector length that has a potential to realize high performance and high power efficiency. This paper discusses the potential through the optimization of two benchmarks, the Himeno and HPCG benchmarks, for the latest vector computing system SX-Aurora TSUBASA. The architecture of SX-Aurora TSUBASA owes the high efficiency to making good of its long vector length. Considering these characteristics, various levels of optimizations required for a large-scale vector computing system are examined such as vectorization, loop unrolling, use of cache, domain decomposition, process mapping, and problem size tuning. The evaluation and analysis suggest that the optimizations improve the sustained performance, power efficiency, and scalability of both benchmarks. Therefore, it is clarified that the SX-Aurora TSUBASA architecture can achieve higher power efficiency due to its high sustained memory bandwidth paired with the long vector computing.