New approach to MPI program execution time prediction

TL;DR

This paper introduces two novel methods for predicting MPI program execution times in heterogeneous cloud environments, utilizing correlation-based grouping and embedding techniques inspired by recommendation systems.

Contribution

It presents new approaches for MPI execution time prediction, including correlation-based grouping and embedding representations, enhancing scheduling accuracy in cloud infrastructures.

Findings

Embedding technique effectively predicts execution times.

Correlation-based grouping improves prediction accuracy.

Methods outperform traditional approaches.

Abstract

The problem of MPI programs execution time prediction on a certain set of computer installations is considered. This problem emerges with orchestration and provisioning a virtual infrastructure in a cloud computing environment over a heterogeneous network of computer installations: supercomputers or clusters of servers (e.g. mini data centers). One of the key criteria for the effectiveness of the cloud computing environment is the time staying by the program inside the environment. This time consists of the waiting time in the queue and the execution time on the selected physical computer installation, to which the computational resource of the virtual infrastructure is dynamically mapped. One of the components of this problem is the estimation of the MPI programs execution time on a certain set of computer installations. This is necessary to determine a proper choice of order and place for program execution. The article proposes two new approaches to the program execution time prediction problem. The first one is based on computer installations grouping based on the Pearson correlation coefficient. The second one is based on vector representations of computer installations and MPI programs, so-called embeddings. The embedding technique is actively used in recommendation systems, such as for goods (Amazon), for articles (Arxiv.org), for videos (YouTube, Netflix). The article shows how the embeddings technique helps to predict the execution time of a MPI program on a certain set of computer installations.