Abstract
The use of high performance computing systems is steadily increasing. For instance, systems composed of PC clusters interconnected by high performance local networks is one of the major trends in parallel/distributed computing nowadays, since these clusters yield parallel systems more powerful than some super-computers, for a fraction of the price. Although a great deal of effort has been undertaken on system-level and programming environment issues for such systems, little attention has been paid to computing issues. In this paper we discuss theoretical (BSP-like) models and their adaptation to solve important classes of problems on high performance computing systems. We show as an example a coarse-grained parallel algorithm to solve the maximum weighted clique problem in interval graphs. It is theoretically optimal, since it requires only a constant number of communication rounds, and is also efficient in practice as demonstrated by reported experimental results obtained on a Myrinet-connected PC cluster. Noticeably, even super-linear speedups can occur for large data because of swapping factors. We conclude that these models are well adapted to the task of algorithm design for high performance computing systems.
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Ferreira, A. (1999). On the Design of Parallel Discrete Algorithms for High Performance Computing Systems. In: Pardalos, P.M. (eds) Parallel Processing of Discrete Problems. The IMA Volumes in Mathematics and its Applications, vol 106. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1492-2_4
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DOI: https://doi.org/10.1007/978-1-4612-1492-2_4
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