scholarly journals A method for parallel program generation with an application to the Booster language

1990 ◽  
Vol 18 (3b) ◽  
pp. 457-469 ◽  
Author(s):  
Edwin M. Paalvast ◽  
Arjan J. van Gemund ◽  
Henk J. Sips
Keyword(s):  
Parallel Program ◽  
Program Generation

scholarly journals Computer-Assisted Parallel Program Generation

2018 ◽  
pp. 4583-4593
Author(s):  
Shigeo Kawata
Keyword(s):  
Problem Solving ◽  
Large Scale ◽  
Parallel Program ◽  
Computer Assisted ◽  
Target Class ◽  
Simple Task ◽  
Program Generation ◽  
Huge Data ◽  
System P ◽  
Free Environment

Parallel computation is widely employed in scientific researches, engineering activities and product development. Parallel program writing itself is not always a simple task depending on problems solved. Large-scale scientific computing, huge data analyses and precise visualizations, for example, would require parallel computations, and the parallel computing needs the parallelization techniques. In this Chapter a parallel program generation support is discussed, and a computer-assisted parallel program generation system P-NCAS is introduced. Computer assisted problem solving is one of key methods to promote innovations in science and engineering, and contributes to enrich our society and our life toward a programming-free environment in computing science. Problem solving environments (PSE) research activities had started to enhance the programming power in 1970's. The P-NCAS is one of the PSEs; The PSE concept provides an integrated human-friendly computational software and hardware system to solve a target class of problems.

scholarly journals Computer-Assisted Parallel Program Generation

2019 ◽  
pp. 692-704
Author(s):  
Shigeo Kawata
Keyword(s):  
Problem Solving ◽  
Large Scale ◽  
Parallel Program ◽  
Computer Assisted ◽  
Target Class ◽  
Simple Task ◽  
Program Generation ◽  
Huge Data ◽  
System P ◽  
Free Environment

Parallel computation is widely employed in scientific researches, engineering activities, and product development. Parallel program writing itself is not always a simple task depending on problems solved. Large-scale scientific computing, huge data analyses, and precise visualizations, for example, would require parallel computations, and the parallel computing needs the parallelization techniques. In this chapter, a parallel program generation support is discussed, and a computer-assisted parallel program generation system, P-NCAS, is introduced. Computer-assisted problem solving is one of key methods to promote innovations in science and engineering, and contributes to enrich our society and our life toward a programming-free environment in computing science. Problem-solving environments (PSE) research activities started to enhance the programming power in 1970s. The P-NCAS is one of the PSEs; the PSE concept provides an integrated human-friendly computational software and hardware system to solve a target class of problems.

Application of Metaprogramming to Program Generation

2000 ◽  
Author(s):  
Emir Pasalic ◽  
Tim Sheard ◽  
Walid Taha
Keyword(s):  
Program Generation

JPF-HJ: A Tool for Task Parallel Program Analysis

2019 ◽  
Vol 44 (4) ◽  
pp. 19-19 ◽  
Author(s):  
Joshua Hooker ◽  
Peter Aldous ◽  
Eric Mercer ◽  
Benjamin Ogles ◽  
Kyle Storey ◽  
...  
Keyword(s):  
Program Analysis ◽  
Parallel Program ◽  
Task Parallel

scholarly journals Correct program parallelisations

2021 ◽  
Author(s):  
S. Blom ◽  
S. Darabi ◽  
M. Huisman ◽  
M. Safari
Keyword(s):  
Composition Operators ◽  
Parallel Programs ◽  
Parallel Program ◽  
Tool Support ◽  
Intermediate Representation ◽  
Data Race ◽  
Sequential Program ◽  
Functional Correctness ◽  
Correct Program ◽  
Basic Blocks

AbstractA commonly used approach to develop deterministic parallel programs is to augment a sequential program with compiler directives that indicate which program blocks may potentially be executed in parallel. This paper develops a verification technique to reason about such compiler directives, in particular to show that they do not change the behaviour of the program. Moreover, the verification technique is tool-supported and can be combined with proving functional correctness of the program. To develop our verification technique, we propose a simple intermediate representation (syntax and semantics) that captures the main forms of deterministic parallel programs. This language distinguishes three kinds of basic blocks: parallel, vectorised and sequential blocks, which can be composed using three different composition operators: sequential, parallel and fusion composition. We show how a widely used subset of OpenMP can be encoded into this intermediate representation. Our verification technique builds on the notion of iteration contract to specify the behaviour of basic blocks; we show that if iteration contracts are manually specified for single blocks, then that is sufficient to automatically reason about data race freedom of the composed program. Moreover, we also show that it is sufficient to establish functional correctness on a linearised version of the original program to conclude functional correctness of the parallel program. Finally, we exemplify our approach on an example OpenMP program, and we discuss how tool support is provided.

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