Power Management Framework for Post-petascale Supercomputers

Kondo, Masaaki; Miyoshi, Ikuo; Inoue, Katsuro; Miwa, Shinobu

doi:10.1007/978-981-13-1924-2_13

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Kondo et al [30] proposed a project called PomPP project; they developed many strategies to save power under a given power budget. They also introduced a poweraware resource manager, power-performance simulation and analysis framework for future supercomputer systems.…”

Section: Introductionmentioning

confidence: 99%

Investigating power efficiency of mergesort

et al. 2019

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Excessive power consumption emerged as a major obstacle to achieving exascale performance in next-generation supercomputers, creating a need to explore new ways to reduce those requirements. In this study, we present a comprehensive empirical investigation of a power advantage anticipated in the mergesort method based on identifying a feature expected to be physically power efficient. We use a highperformance quicksort as a realistic baseline to compare. Results show a generic mergosort to have a distinct advantage over an optimized quicksort lending support to our expectation. They also help develop some insights toward power efficiency gains likely meaningful in a future exascale context where trading some of the abundant performance for much needed power savings in a ubiquitous computation may prove interesting.

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Section: Introductionmentioning

confidence: 99%

Investigating power efficiency of mergesort

et al. 2019

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Investigating the effect of varying block size on power and energy consumption of GPU kernels

Ikram¹,

Saleh

Al-Hashimi

et al. 2022

J Supercomput

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Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have become an accepted architectural feature for exascale computing due to their scalable performance and power efficiency. In a recent study, we found that we can achieve a reasonable amount of power and energy savings based on the selection of algorithms. In this research, we suggest that we can save more power and energy by varying the block size in the kernel configuration. We show that we may attain more savings by selecting the optimum block size while executing the workload. We investigated two kernels on NVIDIA Tesla K40 GPU, a Bitonic Mergesort and Vector Addition kernels, to study the effect of varying block sizes on GPU power and energy consumption. The study should offer insights for upcoming exascale systems in terms of power and energy efficiency.

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Power Management Framework for Post-petascale Supercomputers

Cited by 2 publications

References 14 publications

Investigating power efficiency of mergesort

Investigating power efficiency of mergesort

Investigating the effect of varying block size on power and energy consumption of GPU kernels

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