Vulnerability analysis of GPU computing

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dc.contributor.advisor Joseph Zambreno
dc.contributor.author Patterson, Michael
dc.contributor.department Electrical and Computer Engineering
dc.date 2018-08-11T08:03:59.000
dc.date.accessioned 2020-06-30T02:47:27Z
dc.date.available 2020-06-30T02:47:27Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2013
dc.date.embargo 2015-07-30
dc.date.issued 2013-01-01
dc.description.abstract <p>In the past decade Graphics Processing Units (GPUs) have advanced from simple fixed function graphics accelerators to fully-programmable multi-core architectures capable of supporting thousand of concurrent threads. Their use has spread from the specialized field of graphics into more general processing domains ranging from biomedical imaging to stock market prediction. Despite their increased computational power and range of applications, the security implications of GPUs have not been carefully studied. It has been assumed that the use of a GPU as a coprocessor with physically separate memory space, minimal support for multi-user programming, and limited I/O capability inherently guarantees security. This research challenges this assumption by demonstrating multiple security vulnerabilities in the current GPU computing infrastructure. Specifically, it focuses on the following three areas:</p> <p>1. Denial-of-Service by overwhelming the capabilities of the GPU so it is unable to provide responsiveness to the host operating system.</p> <p>2. Information leakage due to the way that modern GPUs fail to randomize pointers and zero out memory.</p> <p>3. The use of GPUs to assist CPU-resident malware through obfuscation and unpacking or acceleration of computational intensive tasks such as password cracking or encryption.</p> <p>Through the use of WebGL and CUDA, we successfully developed a proof of concept attack for the first two vulnerabilities listed above. For the third, we considered several different types of attacks and their implications. In all cases we also suggest possible security measures to fix these vulnerabilities. While the impact of each of these particular exploits is currently hardware and OS specific, current trends in GPU architecture indicate that these problems are only going to rise in importance going forward.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/13115/
dc.identifier.articleid 4122
dc.identifier.contextkey 4250761
dc.identifier.doi https://doi.org/10.31274/etd-180810-3178
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/13115
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/27304
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/13115/0-Code.zip|||Fri Jan 14 19:45:03 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/etd/13115/Patterson_iastate_0097M_13439.pdf|||Fri Jan 14 19:45:05 UTC 2022
dc.subject.disciplines Computer Engineering
dc.subject.keywords DoS
dc.subject.keywords GPU
dc.subject.keywords security
dc.subject.keywords vulnerability
dc.supplemental.bitstream Code.zip
dc.title Vulnerability analysis of GPU computing
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.level thesis
thesis.degree.name Master of Science
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