Book Review [review of "Using OpenMP: Portable Shared Memory Parallel Programming" (Chapman, B. et al; 2007)]

2008 ◽  
Vol 9 (1) ◽  
pp. 3-3 ◽  
Author(s):  
Ami Marowka
Keyword(s):  
Parallel Programming ◽  
Shared 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.

scholarly journals Workload Decomposition Strategies for Shared Memory Parallel Systems with OpenMP

2001 ◽  
Vol 9 (2-3) ◽  
pp. 109-122 ◽  
Author(s):  
Beniamino Di Martino ◽  
Sergio Briguglio ◽  
Gregorio Vlad ◽  
Giuliana Fogaccia
Keyword(s):  
Programming Languages ◽  
Parallel Programming ◽  
Shared Memory ◽  
Memory Systems ◽  
Particle In Cell ◽  
Decomposition Strategies ◽  
Programming Effort ◽  
High Level ◽  
Memory Architectures

A crucial issue in parallel programming (both for distributed and shared memory architectures) is work decomposition. Work decomposition task can be accomplished without large programming effort with use of high-level parallel programming languages, such as OpenMP. Anyway particular care must still be payed on achieving performance goals. In this paper we introduce and compare two decomposition strategies, in the framework of shared memory systems, as applied to a case study particle in cell application. A number of different implementations of them, based on the OpenMP language, are discussed with regard to time efficiency, memory occupancy, and program restructuring effort.

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