Parallel Computing on Heterogeneous Networks by Alexey L. Lastovetsky

By Alexey L. Lastovetsky

* New ways to parallel computing are being built that make higher use of the heterogeneous cluster structure * presents a close advent to parallel computing on heterogenous clusters * All options and algorithms are illustrated with operating courses that may be compiled and completed on any cluster * The algorithms mentioned have sensible functions in a variety of real-life parallel computing difficulties, corresponding to the N-body challenge, portfolio administration, and the modeling of oil extraction

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By Alexey L. Lastovetsky

* New ways to parallel computing are being built that make higher use of the heterogeneous cluster structure * presents a close advent to parallel computing on heterogenous clusters * All options and algorithms are illustrated with operating courses that may be compiled and completed on any cluster * The algorithms mentioned have sensible functions in a variety of real-life parallel computing difficulties, corresponding to the N-body challenge, portfolio administration, and the modeling of oil extraction

Show description

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The main specific optimization performed by optimizing C and Fortran 77 compilers in order to minimize the 42 Chapter 2: Vector and Superscalar Processors Chapter 2: Vector and Superscalar Processors 43 number of cache misses is loop tiling. Consider the following loop nest: for(i=0; i

The new thread executes the function start_routine. Only one argument can be passed to this function via arg. For cases where multiple arguments must be passed, this limitation is easily overcome by creating a structure that contains all of the arguments and then passing a pointer to that structure in the pthread_create function. The attr argument specifies thread attributes to be applied to the new thread. The attr argument can also be NULL, in which case default attributes are used. On success, the ID of the newly created thread is stored in the location pointed by the thread argument, and 0 is returned.

Pthreads introduce three classes of objects and operations on the objects: • Threads • Mutexes • Condition variables Any thread of an MT program is represented by its ID, which is a reference to an opaque data object holding full information about the thread. This information is used and modified by operations on threads. The operations can create threads, terminate threads, join threads, and so on. There are also operations to set and query thread attributes. Mutex is an abbreviation for mutual exclusion.

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