An Approach for Realistically Simulating the Performance of Scientific Applications on High Performance Computing Systems

TL;DR

This paper presents a realistic simulation approach for scientific applications with irregular, stochastic tasks on HPC systems, enabling better selection of dynamic loop self-scheduling techniques through cost-effective exploratory experiments.

Contribution

It introduces a novel simulation strategy that transforms native application code into a simulative version, accurately predicting performance characteristics for HPC applications with load imbalance.

Findings

Simulation approach accurately captures most performance characteristics.

Transformative method compares native and simulated application performance.

Highlights importance of realistic simulations for scheduling technique selection.

Abstract

Scientific applications often contain large, computationally-intensive, and irregular parallel loops or tasks that exhibit stochastic characteristics. Applications may suffer from load imbalance during their execution on high-performance computing (HPC) systems due to such characteristics. Dynamic loop self-scheduling (DLS) techniques are instrumental in improving the performance of scientific applications on HPC systems via load balancing. Selecting a DLS technique that results in the best performance for different problems and system sizes requires a large number of exploratory experiments. A theoretical model that can be used to predict the scheduling technique that yields the best performance for a given problem and system has not yet been identified. Therefore, simulation is the most appropriate approach for conducting such exploratory experiments with reasonable costs. This work devises an approach to realistically simulate computationally-intensive scientific applications that employ DLS and execute on HPC systems. Several approaches to represent the application tasks (or loop iterations) are compared to establish their influence on the simulative application performance. A novel simulation strategy is introduced, which transforms a native application code into a simulative code. The native and simulative performance of two computationally-intensive scientific applications are compared to evaluate the realism of the proposed simulation approach. The comparison of the performance characteristics extracted from the native and simulative performance shows that the proposed simulation approach fully captured most of the performance characteristics of interest. This work shows and establishes the importance of simulations that realistically predict the performance of DLS techniques for different applications and system configurations.