OMI4papps: Optimisation, Modelling and Implementation for Highly Parallel Applications

TL;DR

This paper introduces Apex-MAP, a synthetic benchmark that models scientific application performance, and demonstrates its use in simulating and validating the performance of key mathematical kernels on high-performance computing systems.

Contribution

It presents a tunable benchmark, Apex-MAP, for simulating scientific application performance and validates it against real hardware measurements.

Findings

Apex-MAP effectively models memory access patterns and computational intensity.

Performance predictions closely match measured data for matrix operations.

The approach aids in performance analysis of scientific applications on supercomputers.

Abstract

This article reports on first results of the KONWIHR-II project OMI4papps at the Leibniz Supercomputing Centre (LRZ). The first part describes Apex-MAP, a tunable synthetic benchmark designed to simulate the performance of typical scientific applications. Apex-MAP mimics common memory access patterns and different computational intensity of scientific codes. An approach for modelling LRZ's application mix is given whichh makes use of performance counter measurements of real applications running on "HLRB II", an SGI Altix system based on 9728 Intel Montecito dual-cores. The second part will show how the Apex-MAP benchmark could be used to simulate the performance of two mathematical kernels frequently used in scientific applications: a dense matrix-matrix multiplication and a sparse matrix-vector multiplication. The performance of both kernels has been intensively studied on x86 cores and hardware accelerators. We will compare the predicted performance with measured data to validate our Apex-MAP approach.