By Fayez Gebali

ISBN-10: 0470902108

ISBN-13: 9780470902103

There's a software program hole among the capability and the functionality that may be attained utilizing today's software program parallel software improvement instruments. The instruments desire guide intervention through the programmer to parallelize the code. Programming a parallel machine calls for heavily learning the objective set of rules or software, extra so than within the conventional sequential programming we've got all realized. The programmer needs to be conscious of the verbal exchange and information dependencies of the set of rules or software. This e-book offers the strategies to discover the prospective how one can application a parallel laptop for a given program.

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**Sample text**

Assume also that the total surface of the earth to be 510 × 106 km2 and the thickness of the atmospheric layer to be approximately 1,000 km. Then, we need to simulate approximately 5 × 1011 weather cells. Assume further that each cell needs to do 200 floating point operations for each iteration of the simulation. Thus, we have to perform a total of 1014 floating point operations per iteration. Let us now assume that we need to run the simulation 106 times to simulate the climate over some long duration of the weather cycle.

It is not trivial to explore the possible parallelization options of a RIA. In fact, Chapters 9–11 are dedicated to just exploring the parallelization of this class of algorithms. 1. 1 Matrix–matrix multiplication algorithm. 1 show regular dependence on the algorithm indices i, j, and k. Traditionally, such algorithms are studied using the dependence graph technique, which shows the links between the different tasks to be performed [10–12]. The dependence graph is attractive when the number of algorithm indices is 1 or 2.

6) where Tp(1) is the algorithm processing time on a single processor and Tp(N) is the processing time on the parallel processors. In an ideal situation, for a fully 16 Chapter 1 Introduction parallelizable algorithm, and when the communication time between processors and memory is neglected, we have Tp (N) = Tp (1)/N, and the above equation gives S( N ) = N . 7) It is rare indeed to get this linear increase in computation domain due to several factors, as we shall see in the book. 2 Communication Overhead For single and parallel computing systems, there is always the need to read data from memory and to write back the results of the computations.

### Algorithms and Parallel Computing (Wiley Series on Parallel and Distributed Computing) by Fayez Gebali

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