Paper Info
Title
Performance improvement options of scientific applications on XeonPhi KNL architectures.
Abstract
Intelu0027s 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 chipu0027s 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 (NASAu0027s) 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.
Year
Venue
Field
2018
IJKEDM
Computer architecture,Manycore processor,Supercomputer,Computer science,Chip,Thread (computing),Exploit,Chip architecture,Artificial intelligence,Performance tuning,Machine learning,Performance improvement
DocType
Volume
Issue
Journal
5
1/2
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
shajulin benedict16913.68