A comparison between the usage of free and proprietary solutions for General-purpose computing on GPUs (GPGPU)

• Isabella B. do Amaral USP
• Alfredo G. vel Lejbman USP

DOI: https://doi.org/10.5753/eradsp.2023.232692

Resumo

In this research project, we explore the usage of open-source and proprietary solutions for performing general-purpose computing on graphics processing units (GPGPU). We begin by categorizing projects them according to their descriptions using unsupervised machine learning. Then, we compare the performance and usability of the solutions in each category, proposing enhancements to better support the development of open-source GPGPU tooling.

Referências

Barba, L. A. (2022). Defining the role of open source software in research reproducibility. arXiv preprint arXiv:2204.12564.

Barker, M., Chue Hong, N. P., Katz, D. S., Lamprecht, A.-L., Martinez-Ortiz, C., Psomopoulos, F., Harrow, J., Castro, L. J., Gruenpeter, M., Martinez, P. A., et al. (2022). Introducing the fair principles for research software. Scientific Data, 9(1):1–6.

Buber, E. and Banu, D. (2018). Performance analysis and cpu vs gpu comparison for deep learning. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT), pages 1–6. IEEE.

Carver, J. C., Weber, N., Ram, K., Gesing, S., and Katz, D. S. (2022). A survey of the state of the practice for research software in the united states. PeerJ Computer Science, 8:e963.

Fortunato, L. and Galassi, M. (2021). The case for free and open source software in research and scholarship. Philosophical Transactions of the Royal Society A, 379(2197):20200079.

Katz, D. S., McInnes, L. C., Bernholdt, D. E., Mayes, A. C., Hong, N. P. C., Duckles, J., Gesing, S., Heroux, M. A., Hettrick, S., Jimenez, R. C., et al. (2018). Community organizations: Changing the culture in which research software is developed and sustained. Computing in Science & Engineering, 21(2):8–24.

Sufi, S., Hong, N. C., Hettrick, S., Antonioletti, M., Crouch, S., Hay, A., Inupakutika, D., Jackson, M., Pawlik, A., Peru, G., et al. (2014). Software in reproducible research: advice and best practice collected from experiences at the collaborations workshop. In Proceedings of the 1st ACM sigplan workshop on reproducible research methodologies and new publication models in computer engineering, pages 1–4.

Wilson, G., Aruliah, D. A., Brown, C. T., Chue Hong, N. P., Davis, M., Guy, R. T., Haddock, S. H., Huff, K. D., Mitchell, I. M., Plumbley, M. D., et al. (2014). Best practices for scientific computing. PLoS biology, 12(1):e1001745.

Zheng, Z., Wang, L., Xu, J., Wu, T., Wu, S., and Tao, X. (2018). Measuring and predicting the relevance ratings between floss projects using topic features. In Proceedings of the Tenth Asia-Pacific Symposium on Internetware, pages 1–10.