Practical scalability assesment for parallel scientific numerical applications

TL;DR

This paper revisits scalability analysis for parallel scientific applications, demonstrating a power fit model to estimate performance and scalability, validated through climate model simulations and applicable to other scientific applications.

Contribution

It introduces a power fit model for scalability estimation of parallel applications, validated with climate simulations, and shows its robustness across different scientific applications.

Findings

Power fit model accurately estimates scalability for N cores.

Scaling capacity follows a log-linear behavior approximated by a power law.

Model remains consistent except at very large core counts.

Abstract

The concept of scalability analysis of numerical parallel applications has been revisited, with the specific goals defined for the performance estimation of research applications. A series of Community Climate Model System (CCSM) numerical simulations were used to test the several MPI implementations, determine optimal use of the system resources, and their scalability. The scaling capacity and model throughput performance metrics for $N$ cores showed a log-linear behavior approximated by a power fit in the form of $C(N)=bN^a$, where $a$ and $b$ are two empirical constants. Different metrics yielded identical power coefficients ($a$), but different dimensionality coefficients ($b$). This model was consistent except for the large numbers of N. The power fit approach appears to be very useful for scalability estimates, especially when no serial testing is possible. Scalability analysis of additional scientific application has been conducted in the similar way to validate the robustness of the power fit approach.