A general and efficient divide-and-conquer algorithm framework for multi-core clusters

Divide-and-conquer is one of the most important patterns of parallelism, being applicable to a large variety of problems. In addition, the most powerful parallel systems available nowadays are computer clusters composed of distributed-memory nodes that contain an increasing number of cores that shar...

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Vydáno v:Cluster computing Ročník 20; číslo 3; s. 2605 - 2626
Hlavní autoři: González, Carlos H., Fraguela, Basilio B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.09.2017
Springer Nature B.V
Témata:
Algorithms
Clusters
Computer Communication Networks
Computer Science
Distributed memory
Hybrid systems
Libraries
Maintenance costs
Message passing
Operating Systems
Parallel programming
Polymorphism
Processor Architectures
Software
Divide-and-conquer
High performance computing
Hybrid parallelism
Algorithmic skeletons
Template metaprogramming
Multi-core clusters
ISSN:1386-7857, 1573-7543
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Shrnutí:Divide-and-conquer is one of the most important patterns of parallelism, being applicable to a large variety of problems. In addition, the most powerful parallel systems available nowadays are computer clusters composed of distributed-memory nodes that contain an increasing number of cores that share a common memory. The optimal exploitation of these systems often requires resorting to a hybrid model that mimics the underlying hardware by combining a distributed and a shared memory parallel programming model. This results in longer development times and increased maintenance costs. In this paper we present a very general skeleton library that allows to parallelize any divide-and-conquer problem in hybrid distributed-shared memory systems with little effort while providing much flexibility and good performance. Our proposal combines a message-passing paradigm at the process level and a threaded model inside each process, hiding the related complexity from the user. The evaluation shows that this skeleton provides performance comparable, and often better than that of manually optimized codes while requiring considerably less effort when parallelizing applications on multi-core clusters.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-017-0766-y