HPM-Frame: A Decision Framework for Executing Software on Heterogeneous Platforms

TL;DR

This paper introduces HPM-Frame, a systematic decision framework designed to assist engineers in refactoring CPU-centric applications for execution on heterogeneous platforms like GPUs and FPGAs, addressing the complexity of such transitions.

Contribution

It presents a structured five-step framework covering the software engineering lifecycle to facilitate the refactoring process for heterogeneous computing platforms.

Findings

Framework is feasible based on practitioner feedback.

Case study demonstrates step-by-step application in automotive computer vision.

Positive impressions from practitioners support framework's utility.

Abstract

Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance. It typically allows the main flow of applications to be executed on a CPU while the most computationally intensive tasks are assigned to one or more accelerators, such as GPUs and FPGAs. The refactoring of systems for execution on such platforms is highly desired but also difficult to perform, mainly due the inherent increase in software complexity. After exploration, we have identified a current need for a systematic approach that supports engineers in the refactoring process -- from CPU-centric applications to software that is executed on heterogeneous platforms. In this paper, we introduce a decision framework that assists engineers in the task of refactoring software to incorporate heterogeneous platforms. It covers the software engineering lifecycle through five steps, consisting of questions to be answered in order to successfully address aspects that are relevant for the refactoring procedure. We evaluate the feasibility of the framework in two ways. First, we capture the practitioner's impressions, concerns and suggestions through a questionnaire. Then, we conduct a case study showing the step-by-step application of the framework using a computer vision application in the automotive domain.