A GUI for Parallel Code Generation

Programming Environments for Massively Parallel Distributed Systems ◽

10.1007/978-3-0348-8534-8_7 ◽

1994 ◽

pp. 69-79 ◽

Cited By ~ 2

Author(s):

Mark R. Gilder ◽

Mukkai S. Krishnamoorthy ◽

John R. Punin

Keyword(s):

Code Generation ◽

Parallel Code

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

International Journal on Software Tools for Technology Transfer ◽

10.1007/s10009-017-0465-2 ◽

2017 ◽

Vol 21 (2) ◽

pp. 183-202

Author(s):

Gervasio Pérez ◽

Sergio Yovine

Keyword(s):

Code Generation ◽

Formal Specification ◽

Parallel Code ◽

Specification And Implementation

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A Meta-heuristic Approach to Parallel Code Generation

Lecture Notes in Computer Science - High Performance Computing for Computational Science — VECPAR 2002 ◽

10.1007/3-540-36569-9_47 ◽

2003 ◽

pp. 693-702

Author(s):

Barry McCollum ◽

Pat H. Corr ◽

Peter Milligan

Keyword(s):

Code Generation ◽

Heuristic Approach ◽

Parallel Code

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Parallel Code Generation

Automatic Parallelization for a Class of Regular Computations ◽

10.1142/9789812819949_0007 ◽

1997 ◽

pp. 129-150

Keyword(s):

Code Generation ◽

Parallel Code

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

Journal of Information Processing ◽

10.2197/ipsjjip.25.426 ◽

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

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A Parallel Code Generation Toolkit for Nonlinear Model Predictive Control

2018 IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc.2018.8619035 ◽

2018 ◽

Cited By ~ 1

Author(s):

Haoyang Deng ◽

Toshiyuki Ohtsuka

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Code Generation ◽

Nonlinear Model Predictive Control ◽

Parallel Code

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

Journal of Systems and Software ◽

10.1016/j.jss.2007.11.715 ◽

2008 ◽

Vol 81 (8) ◽

pp. 1389-1405 ◽

Cited By ~ 1

Author(s):

Theodore Andronikos ◽

Florina M. Ciorba ◽

Panayiotis Theodoropoulos ◽

Dimitrios Kamenopoulos ◽

George Papakonstantinou

Keyword(s):

Computational Geometry ◽

Code Generation ◽

Parallel Code

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Compiling for Scalable Multiprocessors with Polaris

Parallel Processing Letters ◽

10.1142/s0129626497000413 ◽

1997 ◽

Vol 07 (04) ◽

pp. 425-436 ◽

Cited By ~ 1

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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