Energy-Efficient Iterative Refinement using Dynamic Precision

JunKyu Lee, Hans Vandierendonck, Mahwish Arif, Gregory D. Peterson, Dimitrios S. Nikolopoulos

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Abstract

Mixed precision is a promising approach to save energy in iterative refinement algorithms since it obtains speedup without necessitating additional cores and parallelisation. However, conventional mixed precision methods utilise statically defined precision in a loop, thus hindering further speed-up and energy savings. We overcome this problem by proposing novel methods which allow iterative refinement to utilise variable precision arithmetic dynamically in a loop (i.e. a trans-precision approach). Our methods restructure a numeric algorithm dynamically according to runtime numeric behaviour and remove unnecessary accuracy checks. We implemented our methods by extending one conventional mixed precision iterative refinement algorithm on an Intel Xeon E5-2650 2GHz core with MKL 2017 and XBLAS 1.0. Our dynamic precision approach demonstrates 2.0–2.6× speed-up and 1.8–2.4× energy savings compared to mixed precision iterative refinement when double precision solution accuracy is required for forward error and with matrix dimensions ranging from 4K to 32K.
Original languageEnglish
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Early online date25 Jun 2018
DOIs
Publication statusEarly online date - 25 Jun 2018

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