A framework for performance modeling and prediction

  • Authors:
  • Allan Snavely;Laura Carrington;Nicole Wolter;Jesus Labarta;Rosa Badia;Avi Purkayastha

  • Affiliations:
  • The San Diego Supercomputer Center;The San Diego Supercomputer Center;The San Diego Supercomputer Center;The Technical University of Catalonia;The Technical University of Catalonia;The Texas Advanced Computing Center

  • Venue:
  • Proceedings of the 2002 ACM/IEEE conference on Supercomputing
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

Cycle-accurate simulation is far too slow for modeling the expected performance of full parallel applications on large HPC systems. And just running an application on a system and observing wallclock time tells you nothing about why the application performs as it does (and is anyway impossible on yet-to-be-built systems). Here we present a framework for performance modeling and prediction that is faster than cycle-accurate simulation, more informative than simple benchmarking, and is shown useful for performance investigations in several dimensions.