High-performance conjugate-gradient benchmark: A new metric for ranking high-performance computing systems-Reference-Cited by-同舟云学术

High-performance conjugate-gradient benchmark: A new metric for ranking high-performance computing systems

Published:2015-08-17 Issue:1 Volume:30 Page:3-10
ISSN:1094-3420
Container-title:The International Journal of High Performance Computing Applications
language:en
Short-container-title:The International Journal of High Performance Computing Applications

Author:

Dongarra Jack¹,Heroux Michael A²,Luszczek Piotr³

Affiliation:

1. Department of Electrical Engineering and Computer Science, University of Tennessee, USA; Computer Science and Mathematics Division, Oak Ridge National Laboratory, ORNL School of Mathematics and School of Computer Science, University of Manchester, UK

2. Scalable Algorithm Department, Sandia National Laboratories, Albuquerque, New Mexico

3. Department of Electrical Engineering and Computer Science, University of Tennessee, USA

Abstract

We describe a new high-performance conjugate-gradient (HPCG) benchmark. HPCG is composed of computations and data-access patterns commonly found in scientific applications. HPCG strives for a better correlation to existing codes from the computational science domain and to be representative of their performance. HPCG is meant to help drive the computer system design and implementation in directions that will better impact future performance improvement.

Publisher

SAGE Publications

Subject

Hardware and Architecture,Theoretical Computer Science,Software

Link

http://journals.sagepub.com/doi/pdf/10.1177/1094342015593158

Reference25 articles.

1. Bailey D, Barscz E, Barton J, (1994) The NAS parallel benchmarks. Technical Report no. RNR-94-007, NASA Ames Research Center, USA.

2. Bailey D, Harris T, Saphir W, (1995) The NAS parallel benchmarks 2.0. Techinical Report no. NAS-95-020, NASA Ames Research Center, USA.

3. Byun JH, Lin R, Yelick KA, (2012) Autotuning sparse matrix-vector multiplication for multicore. Technical Report no. UCB/EECS-2012-215, University of California, USA.

4. s-step iterative methods for symmetric linear systems

5. D’Azevedo E, Eijkhout V, Romine C (1993) LAPACK working note 56: Reducing communication costs in the conjugate gradient algorithm on distributed memory multiprocessor. Technical Report no. CS-93-185, University of Tennessee, Knoxville, USA.

Cited by 120 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SpChar: Characterizing the sparse puzzle via decision trees;Journal of Parallel and Distributed Computing;2024-10

2. An automated OpenMP mutation testing framework for performance optimization;Parallel Computing;2024-09

3. Morpheus: A library for efficient runtime switching of sparse matrix storage formats;SoftwareX;2024-09

4. The co-evolution of computational physics and high-performance computing;Nature Reviews Physics;2024-08-23

5. Rewriting and Optimizing Vector Length Agnostic Intrinsics from Arm SVE to RVV;The 53rd International Conference on Parallel Processing Workshops;2024-08-12