Prediction of High-Performance Computing Input/Output Variability and Its Application to Optimization for System Configurations

TL;DR

This paper develops a statistical framework to predict HPC input/output variability, enabling better system configuration optimization by evaluating various predictive models on large-scale experimental data.

Contribution

It introduces a new data analytic framework and compares multiple methods for predicting HPC I/O variability, addressing a gap in existing statistical approaches.

Findings

Predictive models achieve high accuracy in unseen configurations

Method comparisons reveal strengths and limitations of different approaches

Optimizing configurations based on variability estimates improves HPC performance

Abstract

Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between numerous factors, such as CPU frequency, the number of input/output (IO) threads, and the IO scheduler. In this paper, we focus on HPC IO variability. The prediction of HPC variability is a challenging problem in the engineering of HPC systems and there is little statistical work on this problem to date. Although there are many methods available in the computer experiment literature, the applicability of existing methods to HPC performance variability needs investigation, especially, when the objective is to predict performance variability both in interpolation and extrapolation settings. A data analytic framework is developed to model data collected from large-scale experiments. Various promising methods are used to build predictive models for the variability of HPC systems. We evaluate the performance of the methods by measuring prediction accuracy at previously unseen system configurations. We also discuss a methodology for optimizing system configurations that uses the estimated variability map. The findings from method comparisons and developed tool sets in this paper yield new insights into existing statistical methods and can be beneficial for the practice of HPC variability management. This paper has supplementary materials online.