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By Jon "maddog" Hall, Visit Amazon's William Gropp Page, search results, Learn about Author Central, William Gropp, , Ewing L. Lusk, Thomas Sterling

This ebook contains easy methods to construct, application, and function a Beowulf process in response to the Linux working procedure. A spouse quantity within the sequence offers an analogous info for Beowulf clusters according to the Microsoft home windows working method.

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The Beowulf book presents three kinds of information to best meet the requirements of the broad and varied cluster computing community. It includes foundation material for students and people new to the field. It also includes reference material in each topic area, such as the major library calls to MPI and PVM or the basic controls for PBS. And, it gives explicit step-by-step guidance on how to accomplish specific tasks such as assembling a processor node from basic components or installing the Maui scheduler.

A Beowulf cluster system can be viewed as being made up of four major components, two hardware and two software. The two hardware components are the compute nodes that perform the work and the network that interconnects the node to form a single system. The two software components are the collection of tools used to develop user parallel application programs and the software environment for managing the parallel resources of the Beowulf cluster. The specification of a Beowulf cluster reflects user choices in each of these domains and determines the balance of cost, capacity, performance, and usability of the system.

Large commodity disks can contain more than 100 gigabytes, but commercial database and scientific data-intensive applications both can demand upwards of 100 terabytes of on-line storage. In addition, certain classes of memory intensive applications such as those manipulating enormous matrices of multivariate data can be processed effectively only if sufficient hardware main memory is brought to bear on the problem. Commodity clusters provide one method of accumulating sufficient DRAM (dynamic random access memory) in a single composite system for these large datasets.

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