Fast Modular Arithmetic on the Kalray MPPA-256 Processor for an Energy-Efficient Implementation of ECM

The Kalray MPPA-256 processor is based on a recent low-energy manycore architecture. In this article, we investigate its performance in multiprecision arithmetic for number-theoretic applications. We have developed a library for modular arithmetic that takes full advantage of the particularities of...

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Vydané v:IEEE Transactions on Computers Ročník 66; číslo 12; s. 2019 - 2030
Hlavní autori: Ishii, Masahiro, Detrey, Jeremie, Gaudry, Pierrick, Inomata, Atsuo, Fujikawa, Kazutoshi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.12.2017
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Predmet:
[INFO.INFO-AO] Computer Science [cs]/Computer Arithmetic
[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]
Arithmetic
Computer architecture
Computer Arithmetic
Computer Science
Context awareness
Cryptography and Security
Curves
Electronic countermeasures
Elliptic curve cyrptography
Elliptic curve method
Graphics processing units
Integer factorization
Kalray MPPA-256 manycore processor
Microprocessors
Multiprecision modular arithmetic
Noise measurement
State of the art
VLIW
Elliptic curve method
Multiprecision modular arithmetic
Kalray MPPA-256 manycore processor
Integer factorization
ISSN:0018-9340, 1557-9956
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Popis
Shrnutí:The Kalray MPPA-256 processor is based on a recent low-energy manycore architecture. In this article, we investigate its performance in multiprecision arithmetic for number-theoretic applications. We have developed a library for modular arithmetic that takes full advantage of the particularities of this architecture. This is in turn used in an implementation of the ECM, an algorithm for integer factorization using elliptic curves. For parameters corresponding to a cryptanalytic context, our implementation compares well to state-of-the-art implementations on GPU, while using much less energy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2017.2704082