A Gain Function for Architectural Decision-Making in Scientific Computing

TL;DR

This paper introduces a Gain Function model to assist in selecting optimal scientific computing architectures by balancing application performance, costs, and relative importance, facilitating confident decision-making.

Contribution

It presents a novel Gain Function for evaluating and choosing scientific computing architectures considering multiple factors and priorities.

Findings

The Gain Function effectively evaluates architecture options.

Application and architecture characteristics are integrated into decision-making.

Case studies demonstrate the model's practical applicability.

Abstract

Scientific Computing typically requires large computational needs which have been addressed with High Performance Distributed Computing. It is essential to efficiently deploy a number of complex scientific applications, which have different characteristics, and so require distinct computational resources too. However, in many research laboratories, this high performance architecture is not dedicated. So, the architecture must be shared to execute a set of scientific applications, with so many different execution times and relative importance to research. Also, the high performance architectures have different characteristics and costs. When a new infrastructure has to be acquired to meet the needs of this scenario, the decision-making is hard and complex. In this work, we present a Gain Function as a model of an utility function, with which it is possible a decision-making with confidence. With the function is possible to evaluate the best architectural option taking into account aspects of applications and architectures, including the executions time, cost of architecture, the relative importance of each application and also the relative importance of performance and cost on the final evaluation. This paper presents the Gain Function, examples, and a real case showing their applicabilities.