A parallel programming assessment for stream processing applications on multi-core systems

Gabriella Andrade

doi:10.1016/j.csi.2022.103691

Return

A parallel programming assessment for stream processing applications on multi-core systems

2023-03-01

PRE

Gabriella Andrade

Dalvan Griebler *

Rodrigo Pereira dos Santos

Luiz Gustavo Fernandes

DOI：10.1016/j.csi.2022.103691

Original

Original request for help

Save

Abstract

En 中文

Multi-core systems are any computing device nowadays and stream processing applications are becoming recurrent workloads, demanding parallelism to achieve the desired quality of service. As soon as data, tasks, or requests arrive, they must be computed, analyzed, or processed. Since building such applications is not a trivial task, the software industry must adopt parallel APIs (Application Programming Interfaces) that simplify the exploitation of parallelism in hardware for accelerating time-to-market. In the last years, research efforts in academia and industry provided a set of parallel APIs, increasing productivity to software developers. However, a few studies are seeking to prove the usability of these interfaces. In this work, we aim to present a parallel programming assessment regarding the usability of parallel API for expressing parallelism on the stream processing application domain and multi-core systems. To this end, we conducted an empirical study with beginners in parallel application development. The study covered three parallel APIs, reporting several quantitative and qualitative indicators involving developers. Our contribution also comprises a parallel programming assessment methodology, which can be replicated in future assessments. This study revealed important insights such as recurrent compile-time and programming logic errors performed by beginners in parallel programming, as well as the programming effort, challenges, and learning curve. Moreover, we collected the participants' opinions about their experience in this study to understand deeply the results achieved.

Keywords:

Parallel software

Parallel computing systems

Programming productivity

Programming effort

Stream parallelism

Programming usability