ReCoTOS: A Platform for Resource-sparing Computing Task Optimization

Jānis Kampars, Guntis Mosāns, Jānis Zuters, Aldis Gulbis, Rasa Gulbe

2022

Abstract

To cope with the growing volume of data and complexity of the associated processing logic, modern CPU capabilities such as vector registers and SIMD (Single Instruction Multiple Data) instructions need to be taken advantage of. Although from a technical point of view, usage of SIMD instructions is not complicated, building computing tasks with good SIMD capabilities has always been a challenging task. Modern compilers assist developers to some extent with solutions like Compiler Automatic Vectorization, which is not always sufficient, and several researchers demonstrate that manual code optimization is still necessary. The paper gives an overview of the existing computing task optimization approaches, designs and describes development of a cloud-based software optimization platform and demonstrates its usage by optimizing a software correlator.

Paper Citation

in Harvard Style

Kampars J., Mosāns G., Zuters J., Gulbis A. and Gulbe R. (2022). ReCoTOS: A Platform for Resource-sparing Computing Task Optimization. In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-568-5, pages 251-258. DOI: 10.5220/0010977100003176

in Bibtex Style

@conference{enase22,
author={Jānis Kampars and Guntis Mosāns and Jānis Zuters and Aldis Gulbis and Rasa Gulbe},
title={ReCoTOS: A Platform for Resource-sparing Computing Task Optimization},
booktitle={Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2022},
pages={251-258},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010977100003176},
isbn={978-989-758-568-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - ReCoTOS: A Platform for Resource-sparing Computing Task Optimization
SN - 978-989-758-568-5
AU - Kampars J.
AU - Mosāns G.
AU - Zuters J.
AU - Gulbis A.
AU - Gulbe R.
PY - 2022
SP - 251
EP - 258
DO - 10.5220/0010977100003176