A survey of power and energy efficient techniques for high performance numerical linear algebra operations

Extreme scale supercomputers available before the end of this decade are expected to have 100 million to 1 billion computing cores. The power and energy efficiency issue has become one of the primary concerns of extreme scale high performance scientific computing. This paper surveys the research on saving power and energy for numerical linear algebra algorithms in high performance scientific computing on supercomputers around the world. We first stress the significance of numerical linear algebra algorithms in high performance scientific computing nowadays, followed by a background introduction on widely used numerical linear algebra algorithms and software libraries and benchmarks. We summarize commonly deployed power management techniques for reducing power and energy consumption in high performance computing systems by presenting power and energy models and two fundamental types of power management techniques: static and dynamic. Further, we review the research on saving power and energy for high performance numerical linear algebra algorithms from four aspects: profiling, trading off performance, static saving, and dynamic saving, and summarize state-of-the-art techniques for achieving power and energy efficiency in each category individually. Finally, we discuss potential directions of future work and summarize the paper.