Univ Sao Paulo, Inst Matemat & Estat, Sao Paulo - Brazil
Número total de Afiliações: 3
Tipo de documento:
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE;
n. 18, SI
SEP 25 2019.
Citações Web of Science:
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)