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The Efficiency of Linda for General Purpose Scientific Programming

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Linda (Linda is a registered trademark of Scientific Computing Associates, Inc.) is a programming language for coordinating the execution and interaction of processes. When combined with a language for computation (such as C or Fortran), the resulting hybrid language can be used to write portable programs for parallel and distributed multiple instruction multiple data (MIMD) computers. The Linda programming model is based on operations that read, write, and erase a virtual shared memory. It is easy to use, and lets the programmer code in a very expressive, uncoupled programming style. These benefits, however, are of little value unless Linda programs execute efficiently. The goal of this article is to demonstrate that Linda programs are efficient making Linda an effective general purpose tool for programming MIMD parallel computers. Two arguments for Linda′s efficiency are given; the first is based on Linda′s implementation and the second on a range of case studies spanning a complete set of parallel algorithm classes.
Title: The Efficiency of Linda for General Purpose Scientific Programming
Description:
Linda (Linda is a registered trademark of Scientific Computing Associates, Inc.
) is a programming language for coordinating the execution and interaction of processes.
When combined with a language for computation (such as C or Fortran), the resulting hybrid language can be used to write portable programs for parallel and distributed multiple instruction multiple data (MIMD) computers.
The Linda programming model is based on operations that read, write, and erase a virtual shared memory.
It is easy to use, and lets the programmer code in a very expressive, uncoupled programming style.
These benefits, however, are of little value unless Linda programs execute efficiently.
The goal of this article is to demonstrate that Linda programs are efficient making Linda an effective general purpose tool for programming MIMD parallel computers.
Two arguments for Linda′s efficiency are given; the first is based on Linda′s implementation and the second on a range of case studies spanning a complete set of parallel algorithm classes.

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