Energy analysis of code regions of HPC applications using EnergyAnalyzer tool

Benedict, Shajulin; Rejitha, R.S.; Preethi, C.; Bright, C. Bency; Judyfer, W.S.

doi:10.1504/ijcse.2017.084163

Cited by 2 publications

(1 citation statement)

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“…Performance analysis of HPC applications (Mishra and Mishra, 2017;Li et al, 2017;Beserra et al, 2017;Gajić et al, 2017;Gong et al, 2018;Benedict et al, 2017) and tool assisted performance monitoring of applications are remaining as a mandatory step to HPC or cloud application developments for decades. Tools such as paradyn (Miller et al, 1995;Roth and Miller, 2006), periscope tool (Benedict and Gerndt, 2012), scalasca toolkit (Geimer et al, 2006), tuning and utility (TAU) (Shende andMalony, 2005), vampir (Knufer et al, 2008), mpiP (Jerey and Chambreau, 2018) and so forth had emerged for analysing the OpenMP or message passing interface (MPI)-based scientific applications on various architectures.…”

Section: Related Workmentioning

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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