The Sisal model of functional programming and its implementation

Gaudiot, Jean‐Luc; Bohm, W.; Najjar, Walid; DeBoni, T.M.; Feo, John; Miller, Patrick J. O.

doi:10.1109/aispas.1997.581640

Cited by 16 publications

(15 citation statements)

References 16 publications

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“…Moreover, the exchange of user-defined types aside from intrinsic types is planned. We would also like to introduce the streaming computations as part of the domain-specific foreignlanguage interface and runtime which will manage parallel resources of underlying machine more efficiently, similar to interfaces in [23][24][25]. This approach should bring most benefits in enabling efficient thread creation and reuse inside the streaming runtime as well as more flexible usage of the streaming model of computation and more parallelization opportunities by implementing the high-level pipelining of the streaming computations and the host application.…”

Section: Discussionmentioning

confidence: 99%

Integrating Streaming Computations for Efficient Execution on Novel Multicore Architectures

2010

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Original scientific paperStreaming has emerged as an important model in present-day applications, ranging from multimedia to scientific computing. Moreover, the emergence of new multicore architectures has resulted with new challenges in efficient utilization of available computational resources. Streaming model offers the portability and scalability of performance with the increasing number of cores. In this paper we propose a tool which enables the implementation of the compute-intensive stream processing kernels as portable modules in general-purpose applications. Resulting modules can be efficiently reused with high degree of scalability in regard to increasing number of processing cores.Key words: Streaming model, Parallel programming, Signal processing, MulticoreIntegracija tokovnog modela za učinkovito izvoenje na višejezgrenim računalnim arhitekturama. Tokovni računalni model predstavlja zanimljivo područje istraživanja s ciljem ubrzanja kako multimedijskih, tako i znanstvenih aplikacija. Isto tako, pojava višejezgrenih računalnih arhitektura rezultirala je povećanjem zanimanja za istraživanje metoda i modela koji bi omogućili učinkovito iskorištavanje postojećih paralelnih resursa. Tokovni model omogućuje istovremeno visok stupanj apstrakcije, prenosivost i skalabinost aplikacija s obzirom na povećanje računskih jezgri. U ovom ječlanku predložen pristup koji omogućuje implementaciju računski zahtjevnih dijelova aplikacija u tokovnom modelu te njihovu integraciju u vidu prenosivih modula. Na taj način ostvareno je ubrzanje cjelokupnih aplikacija pri izvoenju na višejezgrenim procesorima.Ključne riječi: tokovni računalni model, paralelni sustavi, obrada signala, višejezgrena računala

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Section: Discussionmentioning

confidence: 99%

Integrating Streaming Computations for Efficient Execution on Novel Multicore Architectures

2010

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“…Workflows are, by definition, sets of tasks bound by causal/temporal dependencies. Dataflow languages such as SISAL [11], SAC [19], or other declarative or functional languages are natural candidates for workflow use, but their somewhat limited acceptance in the programming world makes them no more familiar than grid workflow languages to most users. Existing parallel processing models such as skeletons in structured parallel programming [7] could be used, but are as unfamiliar as dataflow languages to noncomputer scientists.…”

Section: Workflow Definition Alternativesmentioning

confidence: 99%

Clustered Workflow Execution of Retargeted Data Analysis Scripts

Wang

Zender

Jenks

2008

2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID)

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Supercomputing advances have enabled computational science data volumes to grow at ever increasing rates, commonly resulting in more data produced than can be practically analyzed. Whole-dataset download costs have grown to impractical heights, even with multi-Gbps networks, forcing scientists to rely on server-side subsetting and limiting the scope of data they can analyze on a workstation.Our system supplements existing scientific data services with lightweight computational capability, providing a means of safely relocating analysis from the desktop to the server where clustered execution can be coordinated, exploiting data locality, reducing unnecessary data transfer, and providing end-users with results several times faster. We show how dataflow and other compiler-inspired analyses of shell scripts of scientists' most common analysis tools enables parallelization and optimizations in disk and network I/O bandwidth. We benchmark using an actual geoscience analysis script, illustrating the crucial performance gains of extracting workflows defined in scripts and optimizing their execution. Current results quantify significant improvements in performance, showing the promise of bringing transparent high-performance analysis to the scientist's desktop.

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“…tuples and arrays) which provide a fork-join pattern of computation as well as a parallel case expression which can introduce non-determinism. Neither GHC nor Manticore support implicit parallelism without the need for user annotations which has been implemented in other functional languages like Id (Nikhl 1991), pH (Nikhl and Arvind 2001) and Sisal (Gaudiot et al 1997).…”

Section: Related Workmentioning

confidence: 99%

Runtime support for multicore Haskell

Marlow

Jones

Singh

2009

Proceedings of the 14th ACM SIGPLAN International Conference on Functional Programming

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Purely functional programs should run well on parallel hardware because of the absence of side effects, but it has proved hard to realise this potential in practice. Plenty of papers describe promising ideas, but vastly fewer describe real implementations with good wall-clock performance. We describe just such an implementation, and quantitatively explore some of the complex design tradeoffs that make such implementations hard to build. Our measurements are necessarily detailed and specific, but they are reproducible, and we believe that they offer some general insights.

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The Sisal model of functional programming and its implementation

Cited by 16 publications

References 16 publications

Integrating Streaming Computations for Efficient Execution on Novel Multicore Architectures

Integrating Streaming Computations for Efficient Execution on Novel Multicore Architectures

Clustered Workflow Execution of Retargeted Data Analysis Scripts

Runtime support for multicore Haskell

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