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

Abstract

Summary 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.

Foreword to the special issue of the workshop on high performance computing systems (XVIII Simpósio em Sistemas Computacionais de Alto Desempenho, WSCAD 2017)

“…Finally, the paper “ A Hybrid CPU‐GPU‐MIC Algorithm for Minimal Hitting Set Enumeration ” proposes a hybrid exact algorithm for the Minimal Hitting Set (MHS) Enumeration Problem for highly heterogeneous platforms . The experiments were carried out on heterogeneous platforms composed of Intel Xeon E5‐2620v2 CPUs, Intel Xeon Phi 3120A, and a GTX TITAN X GPUs.…”

HeteroPP: A directive‐based heterogeneous cooperative parallel programming framework

“…In recent years, many researchers have focused on fully exploiting multiple different types of computing devices in heterogeneous platforms to cooperatively accelerate the execution of specific applications, such as minimal hitting set enumeration problem, 11 protein sequence alignment algorithms, 12 sparse matrix‐vector multiplication, 13 solidification modeling, 14 and high‐resolution image restoration algorithms 15 . The above research can fully utilize both multi‐core CPUs and many‐core GPUs/MICs to accelerate the execution of specified computational tasks, and the experimental results show that the performance is significantly improved compared with utilizing the CPUs, GPUs, or MICs alone.…”