Prediction for Distributional Outcomes in High-Performance Computing I/O Variability

TL;DR

This paper introduces a modified Gaussian process model to accurately predict the distribution of I/O throughput variability in HPC systems, incorporating system factors and monotonic constraints, outperforming existing methods.

Contribution

The paper presents a novel distribution prediction framework using a constrained Gaussian process that handles both quantitative and qualitative inputs for HPC performance variability.

Findings

The proposed model accurately predicts I/O throughput distributions.

It outperforms existing distribution prediction methods.

The model can generate scalar summaries like mean and quantiles from the predicted distributions.

Abstract

Although high-performance computing (HPC) systems have been scaled to meet the exponentially-growing demand for scientific computing, HPC performance variability remains a major challenge and has become a critical research topic in computer science. Statistically, performance variability can be characterized by a distribution. Predicting performance variability is a critical step in HPC performance variability management and is nontrivial because one needs to predict a distribution function based on system factors. In this paper, we propose a new framework to predict performance distributions. The proposed model is a modified Gaussian process that can predict the distribution function of the input/output (I/O) throughput under a specific HPC system configuration. We also impose a monotonic constraint so that the predicted function is nondecreasing, which is a property of the cumulative distribution function. Additionally, the proposed model can incorporate both quantitative and qualitative input variables. We evaluate the performance of the proposed method by using the IOzone variability data based on various prediction tasks. Results show that the proposed method can generate accurate predictions, and outperform existing methods. We also show how the predicted functional output can be used to generate predictions for a scalar summary of the performance distribution, such as the mean, standard deviation, and quantiles. Our methods can be further used as a surrogate model for HPC system variability monitoring and optimization.