Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/71398

TitleHeterogeneous implementation of a Voronoi cell-based SVP solver
Author(s)Falcão, Gabriel
Cabeleira, Filipe
Mariano, Artur
Santos, Luís Paulo
Keywordslattice-based cryptanalysis
parallel computing
Lattices
Voronoi-cell
algorithms
high performance computing
parallelism
multi-threading
multicores
graphics processing units
multi-GPU
CUDA
OpenMP
StarPU
Issue dateSep-2019
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
JournalIEEE Access
CitationG. Falcao, F. Cabeleira, A. Mariano and L. Paulo Santos, "Heterogeneous Implementation of a Voronoi Cell-Based SVP Solver," in IEEE Access, vol. 7, pp. 127012-127023, 2019, doi: 10.1109/ACCESS.2019.2939142.
Abstract(s)This paper presents a new, heterogeneous CPU+GPU attacks against lattice-based (postquantum) cryptosystems based on the Shortest Vector Problem (SVP), a central problem in lattice-based cryptanalysis. To the best of our knowledge, this is the first SVP-attack against lattice-based cryptosystems using CPUs and GPUs simultaneously. We show that Voronoi-cell based CPU+GPU attacks, algorithmically improved in previous work, are suitable for the proposed massively parallel platforms. Results show that 1) heterogeneous platforms are useful in this scenario, as they increment the overall memory available in the system (as GPU's memory can be used effectively), a typical bottleneck for Voronoi-cell algorithms, and we have also been able to increase the performance of the algorithm on such a platform, by successfully using the GPU as a co-processor, 2) this attack can be successfully accelerated using conventional GPUs and 3) we can take advantage of multiple GPUs to attack lattice-based cryptosystems. Experimental results show a speedup up to 7.6× for 2 GPUs hosted by an Intel Xeon E5-2695 v2 CPU (12 cores ×2 sockets) using only 1 core and gains in the order of 20% for 2 GPUs hosted by the same machine using all 22 CPU threads (2 are reserved for orchestrating the GPUs), compared to single-CPU execution using the entire 24 threads available.
TypeArticle
URIhttp://hdl.handle.net/1822/71398
DOI10.1109/ACCESS.2019.2939142
ISSN2169-3536
e-ISSN2169-3536
Publisher versionhttps://ieeexplore.ieee.org/document/8822970
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CCTC - Artigos em revistas internacionais

Files in This Item:
File Description SizeFormat 
08822970.pdf5,84 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID