Formal specification and implementation of an automated pattern-based parallel-code generation framework

2017 ◽  
Vol 21 (2) ◽  
pp. 183-202
Author(s):  
Gervasio Pérez ◽  
Sergio Yovine
Keyword(s):  
Code Generation ◽  
Formal Specification ◽  
Parallel Code ◽  
Specification And Implementation

A GUI for Parallel Code Generation

1994 ◽  
pp. 69-79 ◽  
Author(s):  
Mark R. Gilder ◽  
Mukkai S. Krishnamoorthy ◽  
John R. Punin
Keyword(s):  
Code Generation ◽  
Parallel Code

scholarly journals A Task-driven Parallel Code Generation Scheme for Coarse Grain Parallelization on Android Platform

2017 ◽  
Vol 25 (0) ◽  
pp. 426-437
Author(s):  
Akimasa Yoshida ◽  
Akira Kamiyama ◽  
Hiroki Oka
Keyword(s):  
Code Generation ◽  
Coarse Grain ◽  
Android Platform ◽  
Parallel Code

Cronus: A platform for parallel code generation based on computational geometry methods

2008 ◽  
Vol 81 (8) ◽  
pp. 1389-1405 ◽  
Author(s):  
Theodore Andronikos ◽  
Florina M. Ciorba ◽  
Panayiotis Theodoropoulos ◽  
Dimitrios Kamenopoulos ◽  
George Papakonstantinou
Keyword(s):  
Computational Geometry ◽  
Code Generation ◽  
Parallel Code

Compiling for Scalable Multiprocessors with Polaris

1997 ◽  
Vol 07 (04) ◽  
pp. 425-436 ◽  
Author(s):  
Yunheung Paek ◽  
David A. Padua
Keyword(s):  
Recent Work ◽  
Code Generation ◽  
Experimental Results ◽  
Development Costs ◽  
Work Distribution ◽  
Parallel Code ◽  
Scalable Multiprocessors ◽  
Distributed Memories ◽  
Compiler Techniques

Due to the complexity of programming scalable multiprocessors with physically distributed memories, it is onerous to manually generate parallel code for these machines. As a consequense, there has been much research on the development of compiler techniques to simplify programming, to increase reliability, and to reduce development costs. For code generation, a compiler applies a number of transformations in areas such as data privatization, data copying and replication, synchronization, and data and work distribution. In this paper, we discuss our recent work on the development and implementation of a few compiler techniques for some of these transformations. We use Polaris, a parallelizing Fortran restructurer developed at Illinois, as the infrastructure to implement our algorithms. The paper includes experimental results obtained by applying our techniques to several benchmark codes.

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