Effectiveness of simple memory models for performance prediction

Many situations call for an estimation of the execution time of applications, e.g., during design or evaluation of computer systems. In this paper we focus on large applications where the execution times heavily depend on the performance of the memory system. Since such applications are computationally expensive, direct simulation is not an option and an analytical model is called for. This paper addresses this problem by developing and evaluating two simple analytical models. These models focus on an application's interaction with the memory system. Applications are characterized by their memory access types. A regular application has continuous and stride memory accesses. An irregular application has three memory access types: continuous accesses, accesses within the same L1/L2 cache line, and random accesses. The analytical models are combined with results from micro-benchmarking or with appropriate performance estimates of memory accesses to predict application performance, either on real or future machines. We apply these models to executions of CHARMM (Chemistry at HARvard Molecular Mechanics) - a scientific application written in FORTRAN, SMV (Symbolic Model Verifier) - coded in C++. For all three applications, the approaches described here produce results with 5% accuracy on average (compared to the effective run-time measured on a real SPARC system).