The Affinity Effects of Parallelized Libraries in Concurrent Environments

TL;DR

This paper investigates how the interaction between virtualization layers, application types, and parallel libraries affects performance in cloud-based high-performance computing environments, introducing the concept of affinity to analyze these effects.

Contribution

It introduces the concept of affinity to analyze the combined effects of virtualization, application class, and parallel libraries on performance, providing insights for better stability and efficiency.

Findings

Parallel libraries significantly influence application performance.

Certain library and application class combinations affect environment stability.

Affinity analysis helps optimize virtualized high-performance computing environments.

Abstract

The use of cloud computing grows as it appears to be an additional resource for High-Performance Parallel and Distributed Computing (HPDC), especially with respect to its use in support of scientific applications. Many studies have been devoted to determining the effect of the virtualization layer on the performance, but most of the studies conducted so far lack insight into the joint effects between application type, virtualization layer and parallelized libraries in applications. This work introduces the concept of affinity with regard to the combined effects of the virtualization layer, class of application and parallelized libraries used in these applications. Affinity is here defined as the degree of influence that one application has on other applications when running concurrently in virtual environments hosted on the same real server. The results presented here show how parallel libraries used in application implementation have a significant influence and how the combinations between these types of libraries and classes of applications could significantly influence the performance of the environment. In this context, the concept of affinity is then used to evaluate these impacts to contribute to better stability and performance in the computational environment.