Conference Paper

Reliable scalable symbolic computation: The design of SymGridPar2

Citation

Maier P, Stewart R & Trinder PW (2014) Reliable scalable symbolic computation: The design of SymGridPar2. 28th ACM Symposium on Applied Computing, Coimbra, Portugal. Computer Languages, Systems & Structures, 40 (1), pp. 19-35. https://doi.org/10.1016/j.cl.2014.03.001

Abstract
Symbolic computation is an important area of both Mathematics and Computer Science, with many large computations that would benefit from parallel execution. Symbolic computations are, however, challenging to parallelise as they have complex data and control structures, and both dynamic and highly irregular parallelism. The SymGridPar framework (SGP) has been developed to address these challenges on small-scale parallel architectures. However the multicore revolution means that the number of cores and the number of failures are growing exponentially, and that the communication topology is becoming increasingly complex. Hence an improved parallel symbolic computation framework is required. This paper presents the design and initial evaluation of SymGridPar2 (SGP2), a successor to SymGridPar that is designed to provide scalability onto 105 cores, and hence also provide fault tolerance. We present the SGP2 design goals, principles and architecture. We describe how scalability is achieved using layering and by allowing the programmer to control task placement. We outline how fault tolerance is provided by supervising remote computations, and outline higher-level fault tolerance abstractions. We describe the SGP2 implementation status and development plans. We report the scalability and efficiency, including weak scaling to about 32,000 cores, and investigate the overheads of tolerating faults for simple symbolic computations.

Keywords
Parallel functional programming; locality control; fault tolerance;

Journal
Computer Languages, Systems & Structures: Volume 40, Issue 1

StatusPublished
FundersEngineering and Physical Sciences Research Council
Publication date30/04/2014
Publication date online31/03/2014
Date accepted by journal03/03/2014
URLhttp://hdl.handle.net/1893/30036
PublisherElsevier BV
ISSN1477-8424
Conference28th ACM Symposium on Applied Computing
Conference locationCoimbra, Portugal

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