Booster: a high-level language for portable parallel algorithms

Paalvast, E.M.; Sips, Henk; Breebaart, L.

doi:10.1016/0168-9274(91)90050-a

Cited by 10 publications

(4 citation statements)

References 18 publications

(10 reference statements)

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“…Paalvast et al [52] have developed a calculus that could be used as a language-independent framework for expressing programs and their data, which enables the representation and reasoning about code transformations in the form of rewrite rules. They have also shown that the language Booster [51] can be converted to their calculus.…”

Section: B New Methodologiesmentioning

confidence: 99%

Compiling for distributed-memory systems

Zima

Chapman

1993

Proc. IEEE

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Distributed-memop systems are potentiall) scalable to a very large number of processors and promise to be povcvrjiul tools ,for solving large-scale scientijc and engineering problems. Howwer. these machines are currently dificult to program, sirice the user has to distribute the data across the processors atid e.xplicitly formulate the communication required by the program under the selected distribution. During the past years. language extensions oj standard programming languages such as Fortrari were rlei~eloped that allow a concise formulation of data distribution. and new compilation methods were designed and impletnetired that allow the programming of such machines at this relatively high level. In this paper, we describe the current stare of the art in compiling procedural languages (in particular, Fortrun) for [ l i .~t r i b u t e [~-~n e m #~ machines, analyze the limitations of these approuches. tirid outline future research. (SPMD) program for execution on the target DMMP. The compiler analyzes the source code, translating global data references into local and nonlocal data references based on the distributions specified by the user. The nonlocal references are satisfied by inserting appropriate messagepassing statements in the generated code. Finally, the communication is optimized where possible, in particular by combining messages and by sending data at the earliest possible point in time. In algorithms where some data references are made through a level of indirection (such as for unstructured mesh codes and sparse matrix solvers), some of the analysis has to be performed at run-time and the task of the compiler is to generate code to perform this analysis and set up the required communication. This paper is devoted to compilation techniques ,for the source-to-source translation of programs in an ex-264

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Section: B New Methodologiesmentioning

confidence: 99%

Compiling for distributed-memory systems

Zima

Chapman

1993

Proc. IEEE

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“…Paalvast et al [28,29] describe the Booster language, a subproject of the ParTool Parallel Processing Environment project. Booster enables the description of parallel algorithms, based on arraylike data structures, for both shared memory multiprocessors and DMMs.…”

Section: Boostermentioning

confidence: 99%

Language Constructs for Data Partitioning and Distribution

Crooks

Perrott

1994

Scientific Programming

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This article presents a survey of language features for distributed memory multiprocessor systems (DMMs), in particular, systems that provide features for data partitioning and distribution. In these systems the programmer is freed from consideration of the low-level details of the target architecture in that there is no need to program explicit processes or specify interprocess communication. Programs are written according to the shared memory programming paradigm but the programmer is required to specify, by means of directives, additional syntax or interactive methods, how the data of the program are decomposed and distributed.

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“…The array manipulation constructs are inspired by Fortran 90 [21] and Booster [22]. Spar macros and parameterized classes (see Sections 2.7 and 2.4) are inspired by functional or other non-imperative languages, by the language theory of Boute [23] and by the FORFUN project [24].…”

Section: Introductionmentioning

confidence: 99%