Kamesh Arumugam

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Recent development in Graphics Processing Units (GPUs) has enabled a new possibility for highly efficient parallel computing in science and engineering. Their massively parallel architecture makes GPUs very effective for algorithms where processing of large blocks of data can be executed in parallel. Multidimensional integration has important applications(More)
We present a memory-efficient algorithm and its implementation for solving multidimensional numerical integration on a cluster of compute nodes with multiple GPU devices per node. The effective use of shared memory is important for improving the performance on GPUs, because of the bandwidth limitation of the global memory. The best known sequential(More)
Ad hoc On Demand Distance Vector (AODV) routing is an extensively accepted routing protocol for Mobile Ad hoc Network (MANET). The inadequacy of security considerations in the design of AODV makes it vulnerable to black hole attack. In a black hole attack, malicious nodes attract data packets and drop them instead of forwarding. Among the existing black(More)
Social networking sites such as Facebook, twitter and orkut have millions of users. Millions of people are posting articles, photos to interact with other people. Visual representations of the social networks are important to understand the network data and convey the result of the analysis. Visualization often also facilitates qualitative interpretation of(More)
Accurate simulation of collective effects in electron beams is one of the most challenging and computationally intractable problems in accelerator physics. More recently, researchers have developed a GPU-accelerated, high-fidelity simulation of electron beam dynamics that models the collective effects much more accurately. The simulation, however, is(More)
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