A hybrid CPU-GPU-MIC algorithm for minimal hitting set enumeration

Author(s)

Carastan-Santos, Danilo ; Martins-, David C., Jr. ; Song, Siang W. ; Rozante, Luiz C. S. ; de Camargo, Raphael Y.

• Document type: Journal article
• Source: CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE; v. 31, n. 18, p. 12-pg., 2019-09-25.

Abstract

We present a hybrid exact algorithm for the Minimal Hitting Set (MHS) Enumeration Problem for highly heterogeneous CPU-GPU-MIC platforms. With several techniques that permit an efficient exploitation of each architecture, low communication cost, and effective load balancing, we were able to enumerate MHSs for large instances in reasonable time, achieving good performance and scalability. We obtained speedups of up to 25.32 in comparison with using two six-core CPUs and we also enumerated MHSs for instances with tens of thousands of variables in less than 5 hours. We also evaluated our algorithm with a real-world driven dataset, and with a large CPU-GPU cluster, we unprecedentedly enumerated in parallel large minimal hitting sets of this dataset in less than 8 hours. These results reinforce the statement that heterogeneous clusters of CPUs, GPUs, and MICs can be used efficiently for high-performance computing. (AU)

• FAPESP's process: 14/50937-1 - INCT 2014: on the Internet of the Future
• Grantee: Fabio Kon
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 13/26644-1 - Algorithms and programming models for efficient execution of parallel applications in heterogeneous clusters
• Grantee: Raphael Yokoingawa de Camargo
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/24485-9 - Future internet for smart cities
• Grantee: Fabio Kon
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/01587-0 - Storage, modeling and analysis of dynamical systems for e-Science applications
• Grantee: João Eduardo Ferreira
• Support Opportunities: Research Grants - eScience and Data Science Program - Thematic Grants