Dip Sankar Banerjee
The computing industry has undergone several paradigm shifts in the last few decades. Fueled by the need of faster computing, larger data and real time processing needs parallel computing has emerged as one of the dominant paradigms. Motivated by the success achieved in distributed computing models and the limitations faced by single core processors, parallel […]
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Zheng Wei
Current trends in processor architectures increasingly include more cores on a single chip and more complex memory hierarchies, and such a trend is likely to continue in the foreseeable future. These processors offer unprecedented opportunities for speeding up demanding computations if the available resources can be effectively utilized. Simultaneously, parallel programming languages such as OpenMP […]
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Dip Sankar Banerjee, Aman Kumar Bahl, Kishore Kothapalli
The use of manycore architectures and accelerators, such as GPUs, with good programmability has allowed them to be deployed for vital computational work. The ability to use randomness in computation is known to help in several situations. For such computations to be made possible on a general purpose computer, a source of randomness, or in […]
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Praveen K., Vamshi Krishna K., Anil Sri Harsha B., S. Balasubramanian, P.K. Baruah
The PageRank algorithm for determining the "importance" of Web pages forms the core component of Google’s search technology. As the Web graph is very large, containing over a billion nodes, PageRank is generally computed offline, during the preprocessing of the Web crawl, before any queries have been issued. Viewed mathematically, PageRank is nothing but the […]
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Dip Sankar Banerjee, Kishore Kothapalli
The advent of multicore and many-core architectures saw them being deployed to speed-up computations across several disciplines and application areas. Prominent examples include semi-numerical algorithms such as sorting, graph algorithms, image processing, scientific computations, and the like. In particular, using GPUs for general purpose computations has attracted a lot of attention given that GPUs can […]
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Tianji Wu, Bo Wang, Yi Shan, Feng Yan, Yu Wang, Ningyi Xu
Google’s famous PageRank algorithm is widely used to determine the importance of web pages in search engines. Given the large number of web pages on the World Wide Web, efficient computation of PageRank becomes a challenging problem. We accelerated the power method for computing PageRank on AMD GPUs. The core component of the power method […]
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Zheng Wei, Joseph JaJa
We present a number of optimization techniques to compute prefix sums on linked lists and implement them on multithreaded GPUs using CUDA. Prefix computations on linked structures involve in general highly irregular fine grain memory accesses that are typical of many computations on linked lists, trees, and graphs. While the current generation of GPUs provides […]
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M. Suhail Rehman, Kishore Kothapalli, P. J. Narayanan
General purpose programming on the graphics processing units (GPGPU) has received a lot of attention in the parallel computing community as it promises to offer the highest performance per dollar. The GPUs have been used extensively on regular problems that can be easily parallelized. In this paper, we describe two implementations of List Ranking, a […]
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Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

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  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
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