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Vivek Chalotra

Publications and source records attributed to Vivek Chalotra.

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Energy Efficient Algorithms and Power Consumption Techniques in High Performance Computing

High Performance Computing is an internet based computing which makes computer infrastructure and services available to the user for research purpose. However, an important issue which needs to be resolved before High Performance Computing Cluster with large pool of servers gain widespread acceptance is the design of data centers with less energy consumption. It is only possible when servers produce less heat and consume less power. Systems reliability decreases with increase in temperature due to heat generation caused by large power consumption as computing in high temperature is more error-prone. Here in this paper our approach is to design and implement a high performance cluster for high-end research in the High Energy Physics stream. This involves the usage of fine grained power gating technique in microprocessors and energy efficient algorithms that reduce the overall running cost of the data center.

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HEP Analysis Facility An Approach to Grid Computing

HEP Analysis Facility is a cluster designed and implemented in Scientific Linux Cern 5.5 to grant High Energy Physics researchers one place where they can go to undertake a particular task or to provide a parallel processing architecture in which CPU resources are shared across a network and all machines function as one large supercomputer.

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Hep Cluster First Step Towards Grid Computing

HEP Cluster is designed and implemented in Scientific Linux Cern 5.5 to grant High Energy Physics researchers one place where they can go to undertake a particular task or to provide a parallel processing architecture in which CPU resources are shared across a network and all machines function as one large supercomputer. It gives physicists a facility to access computers and data, transparently, without having to consider location, operating system, account administration, and other details. By using this facility researchers can process their jobs much faster than the stand alone desktop systems. Keywords: Cluster, Network, Storage, Parallel Computing & Gris.

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