Modeling the Weather with a Data Flow Supercomputer

Dennis, Jack B.; Gao, Guangrong; Todd, K. W.

doi:10.1109/tc.1984.5009332

Cited by 43 publications

(11 citation statements)

References 6 publications

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“…It is known that optimal balancing of an acyclic dataflow graph can be formulated into certain linear programming problems which have efficient algorithmic solutions [7]. More about dataflow software pipelining under an ideal dataflow model can be found in [5,7].…”

Section: Under An Ideal Dataflow Machine Modelmentioning

confidence: 99%

An efficient scheme for fine-grain software pipelining

Gao

Hum

Wong

1990

Conpar 90 — Vapp Iv

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Dataflow software pipelining was proposed as a means of structuring fine-grain parallelism and has been studied mostly under an idealized dataflow architecture model with infinite resources [7]. In this paper, we address some issues of software pipelining under a realistic architecture model with finite resources. A general framework for fine-grain code scheduling in pipelined machines is developed which simultaneously addresses both time and space efficiency issues for loops typically found in general-purpose scientific computations. This scheduling method exploits fine-grain parallelism through a loop optimization technique which limitedly balances 1 the program graph at compile time, while the instruction-level scheduling is done dynamically at runtime in a data-driven manner.

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Section: Under An Ideal Dataflow Machine Modelmentioning

confidence: 99%

An efficient scheme for fine-grain software pipelining

Gao

Hum

Wong

1990

Conpar 90 — Vapp Iv

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“…The market dominance of multi-and manycore processors and the growing importance and the increasing number of clusters in the Top500 list (top500.org) are making parallelism a key concern when implementing current applications such as weather modeling [13] or nuclear simulations [12]. These important applications require large computational power and thus need to be programmed to run on powerful parallel supercomputers.…”

Section: Introductionmentioning

confidence: 99%

Task Parallelism and Data Distribution: An Overview of Explicit Parallel Programming Languages

Khaldi

Jouvelot

Ancourt

et al. 2013

Languages and Compilers for Parallel Computing

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Abstract. Programming parallel machines as effectively as sequential ones would ideally require a language that provides high-level programming constructs to avoid the programming errors frequent when expressing parallelism. Since task parallelism is considered more error-prone than data parallelism, we survey six popular and efficient parallel language designs that tackle this difficult issue: Cilk, Chapel, X10, Habanero-Java, OpenMP and OpenCL. Using as single running example a parallel implementation of the computation of the Mandelbrot set, this paper describes how the fundamentals of task parallel programming, i.e., collective and point-to-point synchronization and mutual exclusion, are dealt with in these languages. We discuss how these languages allocate and distribute data over memory. Our study suggests that, even though there are many keywords and notions introduced by these languages, they all boil down, as far as control issues are concerned, to three key task concepts: creation, synchronization and atomicity. Regarding memory models, these languages adopt one of three approaches: shared memory, message passing and PGAS (Partitioned Global Address Space). The paper is designed to give users and language and compiler designers an upto-date comparative overview of current parallel languages.

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“…Arvind and Ekanadham [8] have compared the implementation of the hydrodynamics modeling application SIMPLE in Id and in Fortran; Arvind et al [7] have also explored the benefits of fine-grained parallelism in scientific applications. In one of the original papers about programming for dataflow machines, Dennis et al [16] describe the process of implementing a weather modeling code for a theoretical static dataflow architecture as well as the sources of parallelism within the code.…”

Section: Introductionmentioning

confidence: 99%