PARSAC-2: A parallel SAC-2 based on threads

Kuechlin, Wolfgang

doi:10.1007/3-540-54195-0_63

Cited by 24 publications

(9 citation statements)

References 8 publications

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“…Early systems focused on shared-memory architectures, and some prominent examples are Aldor [34], PARSAC2 [35] and PACLIB [36]. Because our focus is on distributed memory systems, we do not discuss these further.…”

Section: Parallel Computational Algebramentioning

confidence: 99%

HPC‐GAP: engineering a 21st‐century high‐performance computer algebra system

Behrends

Hammond

Janjić

et al. 2016

Concurrency and Computation

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SUMMARYSymbolic computation has underpinned a number of key advances in Mathematics and Computer Science. Applications are typically large and potentially highly parallel, making them good candidates for parallel execution at a variety of scales from multi-core to high-performance computing systems. However, much existing work on parallel computing is based around numeric rather than symbolic computations. In particular, symbolic computing presents particular problems in terms of varying granularity and irregular task sizes that do not match conventional approaches to parallelisation. It also presents problems in terms of the structure of the algorithms and data. This paper describes a new implementation of the free open-source GAP computational algebra system that places parallelism at the heart of the design, dealing with the key scalability and cross-platform portability problems. We provide three system layers that deal with the three most important classes of hardware: individual shared memory multi-core nodes, mid-scale distributed clusters of (multi-core) nodes and full-blown high-performance computing systems, comprising large-scale tightly connected networks of multi-core nodes. This requires us to develop new cross-layer programming abstractions in the form of new domain-specific skeletons that allow us to seamlessly target different hardware levels. Our results show that, using our approach, we can achieve good scalability and speedups for two realistic exemplars, on high-performance systems comprising up to 32 000 cores, as well as on ubiquitous multi-core systems and distributed clusters. The work reported here paves the way towards full-scale exploitation of symbolic computation by high-performance computing systems, and we demonstrate the potential with two major case studies.

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Section: Parallel Computational Algebramentioning

confidence: 99%

HPC‐GAP: engineering a 21st‐century high‐performance computer algebra system

Behrends

Hammond

Janjić

et al. 2016

Concurrency and Computation

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“…The primary motivation for the development of DTS was to extend the target platforms of the PARSAC-2 library [17,18] from shared memory multiprocessors to a network of shared memory multiprocessors. Besides being suitable for the realization of this initial goal, it turned out that the distributed fork/join paradigm provided by DTS could successfully be used for parallelizing many other applications.…”

Section: Mathematics and Computers In Simulation 49 (1999) 161±178mentioning

confidence: 99%

An object-oriented platform for distributed high-performance symbolic computation

Blochinger

Küchlin

Ludwig

et al. 1999

Mathematics and Computers in Simulation

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“…In the early nineties, SAC-2, and later ReDuX, were translated from ALDES to C; the C version of SAC-2 is known as SACLIB (Buchberger et al, 1993). At the same time, parallelization of SACLIB programs began; historically, the first multi-threaded parallel form is PARSAC (Küchlin, 1990b;, and a similar more recent variant is PACLIB (Schreiner and Hong, 1993).…”

Section: History and Overview Of The Paredux Systemmentioning

confidence: 99%