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Accelerating hyperparameter optimisation with PyCOMPSs
dc.contributor.author | Njoroge Kahira, Albert |
dc.contributor.author | Bautista Gomez, Leonardo |
dc.contributor.author | Conejero, Javier |
dc.contributor.author | Badia Sala, Rosa Maria |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2019-10-07T10:10:02Z |
dc.date.available | 2019-10-07T10:10:02Z |
dc.date.issued | 2019 |
dc.identifier.citation | Kahira, A. [et al.]. Accelerating hyperparameter optimisation with PyCOMPSs. A: International Conference on Parallel Processing. "ICPP 2019: Proceedings of the 48th International Conference on Parallel Processing: Workshops". New York: Association for Computing Machinery (ACM), 2019, p. 1-8. |
dc.identifier.isbn | 978-1-4503-7196-4 |
dc.identifier.uri | http://hdl.handle.net/2117/169294 |
dc.description.abstract | Machine Learning applications now span across multiple domains due to the increase in computational power of modern systems. There has been a recent surge in Machine Learning applications in High Performance Computing (HPC) in an attempt to speed up training. However, besides training, hyperparameters optimisation(HPO) is one of the most time consuming and resource intensive parts in a Machine Learning Workflow. Numerous algorithms and tools exist to accelerate the process of finding the right parameters for a model. Most of these tools do not utilize the parallelism provided by modern systems and are serial or limited to a single node. The few ones that are offer distributed execution require a serious amount of programming effort. There is, therefore, a need for a tool/scheme that can scale and leverage HPC infrastructures such as supercomputers, with minimum programmers effort and little or no overhead in performance. We present a HPO scheme built on top of PyCOMPSs, a programming model and runtime which aims to ease the development of parallel applications for distributed infrastructures. We show that PyCOMPSs is a powerful framework that can accelerate the process of Hyperparameter Optimisation across multiple devices and computing units. We also show that PyCOMPSs provides easy programmability, seamless distribution and scalability, key features missing in existing tools. Furthermore, we perform a detailed performance analysis showing different configurations to demonstrate the effectiveness our approach. |
dc.format.extent | 8 p. |
dc.language.iso | eng |
dc.publisher | Association for Computing Machinery (ACM) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
dc.subject.lcsh | Electronic data processing -- Distributed processing |
dc.subject.lcsh | High performance computing |
dc.subject.lcsh | Machine learning |
dc.subject.other | Hyperparameter optimisation |
dc.subject.other | Distributed computing |
dc.subject.other | HPC |
dc.subject.other | PyCOMPSs |
dc.title | Accelerating hyperparameter optimisation with PyCOMPSs |
dc.type | Conference report |
dc.subject.lemac | Processament distribuït de dades |
dc.subject.lemac | Càlcul intensiu (Informàtica) |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.identifier.doi | 10.1145/3339186.3339200 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://dl.acm.org/citation.cfm?id=3339200 |
dc.rights.access | Open Access |
local.identifier.drac | 25806170 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Kahira, A.; Bautista Gomez, L. A.; Conejero, J.; Badia, R.M. |
local.citation.contributor | International Conference on Parallel Processing |
local.citation.pubplace | New York |
local.citation.publicationName | ICPP 2019: Proceedings of the 48th International Conference on Parallel Processing: Workshops |
local.citation.startingPage | 1 |
local.citation.endingPage | 8 |