Increasing the degree of parallelism using speculative execution in task-based runtime systems

Task-based programming models have demonstrated their efficiency in the development of scientific applications on modern high-performance platforms. They allow delegation of the management of parallelization to the runtime system (RS), which is in charge of the data coherency, the scheduling, and th...

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Vydané v:PeerJ. Computer science Ročník 5; s. e183
Hlavný autor: Bramas, Bérenger
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States PeerJ. Ltd 18.03.2019
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Predmet:
Algorithms
Computer Science
Computer simulation
Distributed and Parallel Computing
Distributed, Parallel, and Cluster Computing
Energy
High performance computing
Instruction execution (Computers)
International conferences
Methods
Monte Carlo methods
Monte Carlo simulation
Monte-Carlo
Motivation
Parallel processing
Proteins
Review boards
Run time (computers)
Software
Speculation
STF
Task-based
Technology application
Temperature
Monte-Carlo
STF
Task-based
Speculation
ISSN:2376-5992, 2376-5992
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Shrnutí:Task-based programming models have demonstrated their efficiency in the development of scientific applications on modern high-performance platforms. They allow delegation of the management of parallelization to the runtime system (RS), which is in charge of the data coherency, the scheduling, and the assignment of the work to the computational units. However, some applications have a limited degree of parallelism such that no matter how efficient the RS implementation, they may not scale on modern multicore CPUs. In this paper, we propose using speculation to unleash the parallelism when it is uncertain if some tasks will modify data, and we formalize a new methodology to enable speculative execution in a graph of tasks. This description is partially implemented in our new C++ RS called SPETABARU, which is capable of executing tasks in advance if some others are not certain to modify the data. We study the behavior of our approach to compute Monte Carlo and replica exchange Monte Carlo simulations.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.183