Open AccessToward a new metric for ranking high performance computing systems.
Author(s) -
Michael A. Heroux,
Jack Dongarra
Publication year - 2013
Language(s) - English
Resource type - Reports
DOI - 10.2172/1089988
Subject(s) - benchmark (surveying) , ranking (information retrieval) , metric (unit) , computer science , conjugate gradient method , measure (data warehouse) , computation , performance improvement , supercomputer , performance metric , performance measurement , data mining , computer engineering , machine learning , algorithm , parallel computing , engineering , economics , geography , operations management , business , management , geodesy , marketing
The High Performance Linpack (HPL), or Top 500, benchmark [1] is the most widely recognized and discussed metric for ranking high performance computing systems. However, HPL is increasingly unreliable as a true measure of system performance for a growing collection of important science and engineering applications. In this paper we describe a new high performance conjugate gradient (HPCG) benchmark. HPCG is composed of computations and data access patterns more commonly found in applications. Using HPCG we strive for a better correlation to real scientific application performance and expect to drive computer system design and implementation in directions that will better impact performance improvement.
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