A report on the sisal language project

Feo, John; Cann, D.C.; Oldehoeft, R. R.

doi:10.1016/0743-7315(90)90035-n

Cited by 153 publications

(66 citation statements)

References 8 publications

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“…Another early source to mention is the language Sisal [20], which pioneered highperformance functional array processing with stream communication. Sisal was not intended as a coordination language, though, and no attempt at the separation of communication and computation was made in it.…”

Section: Related Workmentioning

confidence: 99%

Asynchronous Stream Processing with S-Net

Grelck

Scholz

Shafarenko

2010

Int J Parallel Prog

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We present the rationale and design of S-Net, a coordination language for asynchronous stream processing. The language achieves a near-complete separation between the application code, written in any conventional programming language, and the coordination/communication code written in S-Net. Our approach supports a component technology with flexible software reuse. No extension of the conventional language is required. The interface between S-Net and the application code is in terms of one additional library function. The application code is componentised and presented to S-Net as a set of components, called boxes, each encapsulating a single tuple-to-tuple function. Apart from the boxes defined using an external compute language, S-Net features two built-in boxes: one for network housekeeping and one for data-flow style synchronisation. Streaming network composition under S-Net is based on four network combinators, which have both deterministic and nondeterministic versions. Flexible software reuse is comprehensive, with the box interfaces and even the network structure being subject to subtyping. We propose an inheritance mechanism, named flow inheritance, that is specifically geared towards stream

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Section: Related Workmentioning

confidence: 99%

Asynchronous Stream Processing with S-Net

Grelck

Scholz

Shafarenko

2010

Int J Parallel Prog

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“…The Macro data flow model was exemplified in the language SISAL [29,28]. SISAL is a functional parallel programming language developed in the early 1990's.…”

Section: Less Popular Modelsmentioning

confidence: 99%

Advanced parallel programming models research and development opportunities.

Wen¹,

Brightwell²

2004

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There is currently a large research and development effort within the highperformance computing community on advanced parallel programming models. This research can potentially have an impact on parallel applications, system software, and computing architectures in the next several years. Given Sandia's expertise and unique perspective in these areas, particularly on very large-scale systems, there are many areas in which Sandia can contribute to this effort. This technical report provides a survey of past and present parallel programming model research projects and provides a detailed description of the Partitioned Global Address Space (PGAS) programming model. The PGAS model may offer several improvements over the traditional distributed memory message passing model, which is the dominant model currently being used at Sandia. This technical report discusses these potential benefits and outlines specific areas where Sandia's expertise could contribute to current research activities. In particular, we describe several projects in the areas of high-performance networking, operating systems and parallel runtime systems, compilers, application development, and performance evaluation. 3 AcknowledgmentWe would like to express our appreciation to the people who helped us during the preparation of this technical report. Sue Goudy reviewed early drafts and identified several important Sandia applications. Thomas Christopher provided background on earlier parallel programming models and suggested the idea of a possible implementation of UPC on the PIM architecture. Mike Heroux shared his invaluable experiences about suitable applications for UPC and directed us to important applications, such as the Trilinos project (the Epetra package) and Co-array Fortran applications, that may be suitable for this work. Neil Pundit reviewed several drafts and provided input and guidance. Jonathan L. Brown assisted with editing and revising this report, provided several UPC coding examples, and conveyed early experiences with the language. Last but not least, Bill Camp provided many insightful comments and constructive suggestions, which helped to give a more complete and practical perspective to this report. 4

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“…Such languages include both data-parallel languages such a s hpf 24] and nesl 3], and control-parallel languages such as Multilisp 23], id 1], sisal 18] and Proteus 28]. Since there is often signi cantly more parallelism expressed in these languages than there are processors, the implementation must not only decide onto which processors to schedule computations, but in what order to schedule them.…”

Section: Introductionmentioning

confidence: 99%

“…Pointers to such s y n c hronization variables can be passed around among threads and synchronization can take place between two threads that have p o i n ters to the variable. Such synchronization variables can be used to implement futures in such languages as Multilisp 18] and are likely to be helpful in implementing the user-speci ed synchronization constraints in Jade 33]. We model computations that use synchronization variables as directed acyclic graphs (dags) in which each node is a unit time action and each edge represents either a control or data dependence between actions.…”

Section: Introductionmentioning

confidence: 99%

Space-efficient scheduling of parallelism with synchronization variables

Blelloch

Gibbons

Matias

et al. 1997

Proceedings of the Ninth Annual ACM Symposium on Parallel Algorithms and Architectures - SPAA '97

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Recent w ork on scheduling algorithms has resulted in provable bounds on the space taken by parallel computations in relation to the space taken by sequential computations. The results for online versions of these algorithms, however, have been limited to computations in which threads can only synchronize with ancestor or sibling threads. Such computations do not include languages with futures or user-speci ed synchronization constraints. Here we extend the results to languages with synchronization variables. Such languages include languages with futures, such as Multilisp and Cool, as well as other languages such a s id.The main result is an online scheduling algorithm which, given a computation with w work (total operations), synchronizations, d depth (critical path) and s1 sequential space, will run in O(w=p + log(pd)=p + d log(pd)) time and s1 + O(pd log(pd)) space, on a p-processor crcw pram with a fetch-and-add primitive. This includes all time and space costs for both the computation and the scheduler. The scheduler is non-preemptive i n t h e s e n s e t h a t i t w i l l o n l y move a thread if the thread suspends on a synchronization, forks a new thread, or exceeds a threshold when allocating space. For the special case where the computation is a planar graph with left-to-right synchronization edges, the scheduling algorithm can be implemented in O(w=p+d log p) time and s1 + O(pd log p) space. These are the rst nontrivial space bounds described for such languages.

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A report on the sisal language project

Cited by 153 publications

References 8 publications

Asynchronous Stream Processing with S-Net

Asynchronous Stream Processing with S-Net

Advanced parallel programming models research and development opportunities.

Space-efficient scheduling of parallelism with synchronization variables

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