A hybrid heuristic resource allocation model for computational grid for optimal energy usage

Kaushik, Achal; Vidyarthi, Deo Prakash

doi:10.1504/ijguc.2018.090228

Cited by 3 publications

(1 citation statement)

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“…High performance computing (HPC) constantly evolves based on novel computing architectures, scalable algorithms (Müller et al, 2018), and energy efficient solutions (O'Brien et al, 2017;Kaushik and Vidyarthi, 2018;Xiong et al, 2017;Digalwar et al, 2017) for solving science problems, year by year. These updates have consistently retained HPC researchers over decades for solving the emerging challenges and fine-tuning the available solutions at various levels of implementing scientific applications.…”

Section: Introductionmentioning

confidence: 99%

Performance improvement options of scientific applications on XeonPhi KNL architectures

Benedict¹

2018

IJKEDM

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Intel's recent manycore processor KNights Landing (KNL) promises high performance for scientific applications. Careful tuning for the complex chip architecture is required to efficiently exploit the chip's hardware resources. This paper describes performance improvement techniques and demonstrates their effectiveness for scientific applications. Experiments were conducted with some of the National Aeronautics and Space Administration (NASA's) advanced supercomputing (NAS) parallel benchmarks, and the effectiveness of: 1) advanced vector extensions (AVX-512) vectorisation support; 2) manycore threading support; 3) the utilisation of thread affinities for different KNL modes, was analysed.

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Section: Introductionmentioning

confidence: 99%