Improving the energy efficiency of sparse linear system solvers on multicore and manycore systems.
While most recent breakthroughs in scientific research rely on complex simulations carried out in large-scale supercomputers, the power draft and energy spent for this purpose is increasingly becoming a limiting factor to this trend. In this paper, we provide an overview of the current status in energy-efficient scientific computing by reviewing different technologies used to monitor power draft as well as power- and energy-saving mechanisms available in commodity hardware. For the particular domain of sparse linear algebra, we analyse the energy efficiency of a broad collection of hardware architectures and investigate how algorithmic and implementation modifications can improve the energy performance of sparse linear system solvers, without negatively impacting their performance.
Innovative Computing Laboratory (ICL), University of Tennessee at Knoxville, Knoxville, TN 37996, USA hanzt@icl.utk.edu.
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Departamento de Ingeniería y Ciencia de Computadores, Universidad Jaume I, Castellón, Spain.
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Quintana-Ortí
energy efficiency; graphics processing units; high performance computing; iterative solvers; multicore processors; sparse linear systems