Work

Accelerating Hyperparameter Optimisation with PyCOMPSs

Year: 2019

Type: conference-paper

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.

Source: International Conference on Parallel Processing Workshops (ICPPW)

DOI: 10.1145/3339186.3339200

Authors: Albert Njoroge Kahira, Leonardo Bautista Gomez, Javier Conejero, Rosa M. Badia

Institution: Barcelona Supercomputing Center

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Awards: G1255325603, Innovative doctoral programme for talented early-stage researchers in Spanish host organisations excellent in the areas of Science, Technology, Engineering and Mathematics (STEM)., G7731791180