Power and energy consumption are becoming the most important restrictions on driving computer systems. Especially, HPC systems still require more performance despite their limited power supply, and it is necessary to explore methodologies to enhance their performance within this limitation. Approximate computing (AC) is a promising technique for optimizing the trade-offs among application execution performance, application output correctness, and power consumption during execution. However, applying AC sometimes causes unexpected effects on the output and performance due to cache behavior, compiler optimization, and so on. This paper discusses dynamic approximate computing methods, which enable changing data precision in applications at runtime, for more flexible and effective approximate computing (AC). This paper also evaluates the benefits and drawbacks of dynamic AC with some high-performance computing (HPC) applications and workloads.
各種検索
サポート
ヘルプ