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Andrey A. Chusov

dblp:201/6213 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-7931-5368ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 62% Processor architecture and microarchitecture · 19% High-performance computing · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing
parallel algorithms
0.612022
Outperforming Sequential Full-Word Long Addition With Parallelization and Vectorization · IEEE Trans. Parallel Distributed Syst. 2022
Processor architecture and microarchitecture
instruction set architecture
0.212022
Outperforming Sequential Full-Word Long Addition With Parallelization and Vectorization · IEEE Trans. Parallel Distributed Syst. 2022
High-performance computing › code optimization
vectorization
0.212022
Outperforming Sequential Full-Word Long Addition With Parallelization and Vectorization · IEEE Trans. Parallel Distributed Syst. 2022

Methods — techniques the papers use, named apart from their topics

kogge-stone · 0.6carry-lookahead addition · 0.6SIMD · 0.6
YearPublicationVenuePosition
2022 Outperforming Sequential Full-Word Long Addition With Parallelization and Vectorization
abstract
The article presents algorithms for parallel and vectorized full-word addition of big unsigned integers with carry propagation. Because of the propagation, software parallelization and vectorization of non-polynomial addition of big integers have long been considered impractical due to data dependencies between digits of the operands. The presented algorithms are based upon parallel and vectorized detection of carry origins within elements of vector operands, masking bits which correspond to those elements and subsequent scalar addition of the resulting integers. The acquired bits can consequently be taken into account to adjust the sum using the proposed generalization of the Kogge-Stone method. Essentially, the article formalizes and experimentally verifies parallel and vectorized implementation of carry-lookahead adders applied at arbitrary granularity of data. This approach is noticeably beneficial for manycore, CUDA and vectorized implementation using AVX-512 with masked instructions. Experiments show that the parallel and vectorized implementations of the proposed algorithms can be multiple times faster compared to a sequential ripple-carry adder or adders based on redundant number systems such as one used in the GNU Multiple Precision library.
Andrey A. Chusov
IEEE Trans. Parallel Distributed Syst.1