A method for parallel program generation with an application to the
            <i>Booster</i>
            language

Paalvast, E.M.; Gemund, Arjan J. van; Sips, Henk

doi:10.1145/255129.255189

Cited by 14 publications

(4 citation statements)

References 12 publications

(6 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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“…The programmer does not specify communication explicitly, but must mark nonlocal accesses. In Booster [22,23], data distributions are specified separately from the algorithm in an annotation module; a distinction is made between work and data partitions.…”

Section: Related Workmentioning

confidence: 99%

Programming in Vienna Fortran

Chapman

Mehrotra

Zima

1992

Scientific Programming

194

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Exploiting the full performance potential of distributed memory machines requires a careful distribution of data across the processors. Vienna Fortran is a language extension of Fortran which provides the user with a wide range of facilities for such mapping of data structures. In contrast to current programming practice, programs in Vienna Fortran are written using global data references. Thus, the user has the advantages of a shared memory programming paradigm while explicitly controlling the data distribution. In this paper, we present the language features of Vienna Fortran for FoRTRAN 77, together with examples illustrating the use of these features

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“…This approach is followed by [Callahan88, Gerndt89, Kennedy89, Koelbel89] in FORTRAN, by [Rogers89] in Id Nouveau, and by [Quinn89] in C*. This concept is also followed in Booster [Paalvast90].…”

Section: Developments In Parallel Programmingmentioning

confidence: 99%

“…To this purpose, a formal framework V-cal (View-Calculus) underlying the language was defined that allows for the transformation and optimization of the produced target code for shared as well as distributed-memory machines. This formalism and optimizations are discussed in [Gemund89,Paalvast90]. The reason for the good transformational properties of V-cal can be found in the design of the Booster language.…”

Section: Translating Boostermentioning

confidence: 99%