An Environment for Portable Distributed Memory Parallel Programming

1994 ◽  
pp. 159-170 ◽  
Author(s):  
C. Clémençon ◽  
A. Endo ◽  
J. Fritscher ◽  
A. Müller ◽  
R. Rühl ◽  
...  
Keyword(s):  
Parallel Programming ◽  
Distributed Memory

scholarly journals Parallel programming technologies on computer complexes

2020 ◽  
Vol 30 (3) ◽  
pp. 28-33 ◽  
Author(s):  
S. A. Pryadko ◽  
A. Yu. Troshin ◽  
V. D. Kozlov ◽  
A. E. Ivanov
Keyword(s):  
Parallel Programming ◽  
Shared Memory ◽  
Programming Language ◽  
Operating Systems ◽  
Distributed Memory ◽  
Programming Models ◽  
Writing Programs ◽  
Advantages And Disadvantages ◽  
C Programming Language ◽  
C Programming

The article describes various options for speeding up calculations on computer systems. These features are closely related to the architecture of these complexes. The objective of this paper is to provide necessary information when selecting the capability for the speeding process of solving the computation problem. The main features implemented using the following models are described: programming in systems with shared memory, programming in systems with distributed memory, and programming on graphics accelerators (video cards). The basic concept, principles, advantages, and disadvantages of each of the considered programming models are described. All standards for writing programs described in the article can be used both on Linux and Windows operating systems. The required libraries are available and compatible with the C/C++ programming language. The article concludes with recommendations on the use of a particular technology, depending on the type of task to be solved.

A DATA RE-DISTRIBUTION LIBRARY FOR MULTI-PROCESSOR TASK PROGRAMMING

2006 ◽  
Vol 17 (02) ◽  
pp. 251-270 ◽  
Author(s):  
THOMAS RAUBER ◽  
GUDULA RÜNGER
Keyword(s):  
Parallel Programming ◽  
Data Structures ◽  
Distributed Memory ◽  
Programming Model ◽  
Distributed Data ◽  
Data Format ◽  
Arbitrary Size ◽  
Distributed Memory Machines ◽  
Parallel Programming Model ◽  
Task Program

Multiprocessor task (M-task) programming is a suitable parallel programming model for coding application problems with an inherent modular structure. An M-task can be executed on a group of processors of arbitrary size, concurrently to other M-tasks of the same application program. The data of a multiprocessor task program usually include composed data structures, like vectors or arrays. For distributed memory machines or cluster platforms, those composed data structures are distributed within one or more processor groups. Thus, a concise parallel programming model for M-tasks requires a standardized distributed data format for composed data structures. Additionally, functions for data re-distribution with respect to different data distributions and different processor group layouts are needed to glue program parts together. In this paper, we present a data re-distribution library which extends the M-task programming with Tlib, a library providing operations to split processor groups and to map M-tasks to processor groups.

scholarly journals Efficient Parallel Programming with Linda

1992 ◽  
Vol 1 (2) ◽  
pp. 177-183 ◽  
Author(s):  
Ashish Deshpande ◽  
Martin Schultz
Keyword(s):  
Parallel Programming ◽  
Programming Language ◽  
Associative Memory ◽  
Shallow Water Equations ◽  
Distributed Memory ◽  
Parallel Machines ◽  
Yale University ◽  
Coordination Language ◽  
High Level ◽  
Memory Architectures

Linda is a coordination language inverted by David Gelernter at Yale University, which when combined with a computation language (like C) yields a high-level parallel programming language for MIMD machines. Linda is based on a virtual shared associative memory containing objects called tuples. Skeptics have long claimed that Linda programs could not be efficient on distributed memory architectures. In this paper, we address this claim by discussing C-Linda's performance in solving a particular scientific computing problem, the shallow water equations, and make comparisons with alternatives available on various shared and distributed memory parallel machines.

PPT: A parallel programming tool for distributed memory multiprocessors

1995 ◽  
Vol 18 (3) ◽  
pp. 365-378
Author(s):  
Yeh‐Ching Chung ◽  
Wu‐Hsun Ho ◽  
Chia‐Cheng Liu
Keyword(s):  
Parallel Programming ◽  
Distributed Memory ◽  
Programming Tool

LLC: A PARALLEL SKELETAL LANGUAGE

2003 ◽  
Vol 13 (03) ◽  
pp. 437-448 ◽  
Author(s):  
ANTONIO J. DORTA ◽  
JESÚS A. GONZÁLEZ ◽  
CASIANO RODRÍGUEZ ◽  
FRANCISCO DE SANDE
Keyword(s):  
Theoretical Model ◽  
Parallel Programming ◽  
Distributed Memory ◽  
Parallel Architectures ◽  
Parallel Applications ◽  
Prototype Implementation ◽  
Memory Architectures

The skeletal approach to the development of parallel applications has been revealed to be one of the most successful and has been widely explored in the recent years. The goal of this approach is to develop a methodology of parallel programming based on a restricted set of parallel constructs. This paper presents llc, a parallel skeletal language, the theoretical model that gives support to the language and a prototype implementation for its compiler. The language is based on directives, uses a C-like syntax and gives support to the most widely used skeletal constructs. llCoMP is a source to source compiler for the language built on top of MPI. We evaluate the performance of our prototype compiler using four different parallel architectures and three algorithms. We present the results obtained in both shared and distributed memory architectures. Our model guarantees the portability of the language to any platform and its simplicity greatly eases its implementation.

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