You can view the full text of this article for free using the link below.

Title: Performance improvement options of scientific applications on XeonPhi KNL architectures

Authors: Shajulin Benedict

Addresses: Indian Institute of Information Technology Kottayam, Kerala, India

Abstract: 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.

Keywords: knights landing; performance analysis; performance tuning; scientific applications.

DOI: 10.1504/IJKEDM.2018.092811

International Journal of Knowledge Engineering and Data Mining, 2018 Vol.5 No.1/2, pp.1 - 16

Received: 03 Apr 2018
Accepted: 09 Apr 2018

Published online: 19 Jun 2018 *

Full-text access for editors Access for subscribers Free access Comment on this article