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GPGPU Computing for Cloud Auditing

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Abstract

With the increasing computational complexity of cloud auditing and other data-intensive analysis applications, there is a growing need for computing platforms that can handle massive data sets and perform rapid analysis. These needs are met by systems with accelerators, such as Graphics Processing Units (GPUs), that can perform data analysis with a high level of parallelism employing tools like Hadoop MapReduce to handle massively parallel computing jobs. Applying GPUs to general purpose processing is known as GPGPU. This chapter uses an introductory approach to cover the basics of GPUs and GPGPU computing and their application to cloud computing and handling of large data sets. The main aim is to give the reader a broad background on how GPGPUs are used and their contribution to advances in cloud auditing.

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Acknowledgements

This material is based upon work partially supported by the National Science Foundation (NSF) Engineering Research Centers (ERC) Innovations Program grant EEC-0946463, Mathworks, the Air Force Office of Scientific Research (AFOSR)/the Air Force Research Laboratory (AFRL) LRIR 11RI01COR. The authors would like to thank James Brock for developing the OpenCL code examples, and Devon Yablonski for writing the conjugate gradient GPU code and analyzing the performance of GPU computing. This work was completed while both James and Devon were graduate students, and had worked under the supervision of Miriam Leeser at Northeastern University.

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  1. Air Force Research Laboratory, WPAFB, OH, USA

    Virginia W. Ross

  2. Northeastern University, Boston, MA, USA

    Miriam E. Leeser

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  1. Virginia W. Ross
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Correspondence to Virginia W. Ross .

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  1. Air Force Research Laboratory, Rome, New York, USA

    Keesook J. Han

  2. School of Computing and Engineering, University of Missouri - Kansas City, Kansas City, Missouri, USA

    Baek-Young Choi

  3. Department of Engineering Technology The Dwight Look College of Engineering, Texas A&M University, College Station, Texas, USA

    Sejun Song

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Ross, V.W., Leeser, M.E. (2014). GPGPU Computing for Cloud Auditing. In: Han, K., Choi, BY., Song, S. (eds) High Performance Cloud Auditing and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3296-8_10

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