Abstract

Building parameterized performance models of applications in an automatic way is difficult because of the large number of variables that affect performance, including architecture-dependent factors, algorithmic choices and input data parameters. In general, application performance is a non-convex and non-smooth function in this multivariate parameter space. This thesis describes techniques to measure and model application characteristics independent of the target architecture. This approach produces an architecture-neutral model for an application. For predictable applications, such models have a convex and differentiable profile. Our approach succeeds in modeling the most important application factors that affect performance and enables us to explore the interactions between a target architecture and application characteristics. To date, work has concentrated on modeling the performance of intervals of se-quential computation. Our models are designed to characterize node performance between synchronization points in parallel programs, with the eventual goal of mod-

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