Publication: Resource Management Optimization in Multi-Processor Platforms
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2016-12
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Abstract
The modern high-performance computing systems (HPCS) are composed of hundreds of thousand computational nodes. An
effective resource allocation in HPCS is a subject for many scientific research investigations. Many programming models for
effective resources allocation have been proposed. The main purpose of those models is to increase the parallel performance
of the HPCS. This paper investigates the efficiency of parallel algorithm for resource management optimization based on
Artificial Bee Colony (ABC) metaheuristic while solving a package of NP-complete problems on multi-processor platform.In
order to achieve minimal parallelization overhead in each cluster node, a multi-level hybrid programming model is proposed that
combines coarse-grain and fine-grain parallelism. Coarse-grain parallelism is achieved through domain decomposition by message
passing among computational nodes using Message Passing Interface (MPI) and fine-grain parallelism is obtained by loop-level
parallelism inside each computation node by compiler-based thread parallelization via Intel TBB. Parallel communications
profiling is made and parallel performance parameters are evaluated on the basis of experimental results.
Description
Proceedings of: Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016). Sofia (Bulgaria), October, 6-7, 2016.
Keywords
High-Performance Computing, Parallel Programming Model, Parallel Performance, Parallel Algorithm
Bibliographic citation
Carretero Pérez, Jesús; et.al. (eds.). (2016) Proceedings of the Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016): Sofia, Bulgaria. Universidad Carlos III de Madrid, pp. 23-29.