TY - GEN
T1 - POSTER: Accelerating High-Precision Integer Multiplication used in Cryptosystems with GPUs
AU - Zhang, Zhaorui
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/3/2
Y1 - 2024/3/2
N2 - High-precision integer multiplication is crucial in privacy-preserving computational techniques but poses acceleration challenges on GPUs due to its complexity and the diverse bit lengths in cryptosystems. This paper introduces GIM, an efficient high-precision integer multiplication algorithm accelerated with GPUs. It employs a novel segmented integer multiplication algorithm that separates implementation details from bit length, facilitating code optimizations. We also present a computation diagram to analyze parallelization strategies, leading to a series of enhancements. Experiments demonstrate that this approach achieves a 4.47× speedup over the commonly used baseline.
AB - High-precision integer multiplication is crucial in privacy-preserving computational techniques but poses acceleration challenges on GPUs due to its complexity and the diverse bit lengths in cryptosystems. This paper introduces GIM, an efficient high-precision integer multiplication algorithm accelerated with GPUs. It employs a novel segmented integer multiplication algorithm that separates implementation details from bit length, facilitating code optimizations. We also present a computation diagram to analyze parallelization strategies, leading to a series of enhancements. Experiments demonstrate that this approach achieves a 4.47× speedup over the commonly used baseline.
KW - GPU computing
KW - big integer multiplication
UR - http://www.scopus.com/inward/record.url?scp=85187204916&partnerID=8YFLogxK
U2 - 10.1145/3627535.3638495
DO - 10.1145/3627535.3638495
M3 - Conference article published in proceeding or book
T3 - Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP
SP - 445
EP - 447
BT - PPoPP 2024 - Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
ER -