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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2012

Authors: Ajay Prakash
Keywords: XNETLIB
Issue Date: 6-Jan-2011
Abstract: Parallel processing, the method of having many small tasks solve one large problem, has emerged as a key enabling technology in modern computing. The past several years have witnessed an ever-increasing acceptance and adoption of parallel processing, both for high-performance scientific computing and for more \general-purpose" applications, was a result of the demand for higher performance, lower cost, and sustained productivity. The acceptance has been facilitated by two major developments: massively parallel processors (MPPs) and the widespread use of distributed computing. MPPs are now the most powerful computers in the world. These machines combine a few hundred to a few thousand CPUs in a single large cabinet connected to hundreds of gigabytes of memory. MPPs o er enormous computational power and are used to solve computational Grand Challenge problems such as global climate modeling and drug design. As simulations become more realistic, the computational power required to produce them grows rapidly. Thus, researchers on the cutting edge turn to MPPs and parallel processing in order to get the most computational power possible. The second major development a selecting scientific problem solving is distributed computing. Distributed computing is a process whereby a set of computers connected by a network are used collectively to solve a single large problem. As more and more organizations have high-speed local area networks interconnecting many general-purpose workstations, the combined computational resources may exceed the power of a single high-performance computer. In some cases, several MPPs have been combined using distributed computing to produce unequaled computational power. The most important factor in distributed computing is cost. Large MPPs typically cost more than $10 million. In contrast, users see very little cost in running their problems on a local set of existing computers. It is uncommon for distributed-computing users to realize the raw computational power of a large MPP, but they are able to solve problems several times larger than they could using one of their local computers.
Description: Seminar report submitted in Sept 2010 in partial fulfillment of the requirements for the Degree of Bachelor of Technology (B.Tech ) in Computer Science and Engineering under the Guideship of Sudheep Elayidom.
URI: http://hdl.handle.net/123456789/2012
Appears in Collections:Seminar Reports

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