By Jacek Blazewicz, Klaus Ecker, Brigitte Plateau, Denis Trystram
In this quantity authors of academia and perform supply practitioners, scientists and graduate scholars with a great assessment of simple tools and paradigms, in addition to vital matters and tendencies around the wide spectrum of parallel and allotted processing. specifically, the ebook covers primary themes akin to effective parallel algorithms, languages for parallel processing, parallel working platforms, structure of parallel and disbursed platforms, administration of assets, instruments for parallel computing, parallel database platforms and multimedia item servers, and networking features of allotted and parallel computing. 3 chapters are devoted to purposes: parallel and dispensed medical computing, high-performance computing in molecular sciences, and multimedia purposes for parallel and dispensed platforms. Summing up, the guide is essential for teachers and pros who're drawn to studying the prime expert`s view of the topic.
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Additional info for Handbook on Parallel and Distributed Processing
We begin by storing each of the numbers in a distinct processor. Thus, initially, Pi holds Xi and sets Yi = Xi, for 0 ::; i ::; N - 1. Now, the processors repeatedly perform an operation known as a 'comparison-exchange', whereby Yi is compared to Yi+1 with the smaller of the two numbers ending up in Pi and the larger in PHi. The algorithm is given next. = Algorithm LINEAR ARRAY SORT for j = 0 to N - 1 do for i = 0 to n - 2 do in parallel if i mod 2 = j mod 2 24 Selim G. • It can be shown that this algorithm sorts correctly in N steps, that is, O(N) time.
0 The number of processors often allows us to derive a lower bound on the running time of any parallel algorithm for solving a given problem, independently of the model of computation. Suppose that a lower bound of Q(J(n)) on the number of operations required to solve a problem of size n is known. Then any algorithm for solving that problem in parallel using n processors must require Q(J(n)/n) time. For example, a lower bound on the number of operations required to multiply two n x n matrices is Q(n 2 ).
0 As a result of the arrangement just described, processor P", such that v = v(1)v(2) ... v(n), occupies that position of the (n-1)-dimensional array whose coordinates 1(1), 1(2), ... , l(n-1) are given by l(k) = k+1- E~=dv(k+1) > v(j)], for 1 ~ k ~ n - 1, where [v(k + 1) > v(j)] equals 1 if v(k + 1) > v(j) and equals 0 otherwise. It should be stressed here that two processors occupying adjacent positions in some dimension k, 1 $ k $ n - 1, on the (n - 1)-dimensional array II. The Design of Efficient Parallel Algorithms 37 neighbor 1 v(2) vel) ...
Handbook on Parallel and Distributed Processing by Jacek Blazewicz, Klaus Ecker, Brigitte Plateau, Denis Trystram