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.
This paper presents a performance modeling methodology that is faster than traditional cycle-accurate simulation, more sophisticated than performance estimation based on system peak-performance metrics, and is shown to be effective on a class of High Performance Computing benchmarks. The method yields insight into the factors that affect performance on single-processor and parallel computers.
This work presents a performance modeling framework, developed by the Performance Modeling and Characterization (PMaC) Lab at the San Diego Supercomputer Center, that is faster than traditional cycle-accurate simulation, more sophisticated than performance estimation based on system peakperformance metrics, and is shown to be effective on the LINPACK benchmark and a synthetic version of an ocean modeling application (NLOM). The LINPACK benchmark is further used to investigate methods to reduce the time required to make accurate performance predictions with the framework. These methods are applied to the predictions of the synthetic NLOM application.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.