Demo Data from VLDB 2026 / reviewers in the wild / expert
Andrey A. Chusov
dblp:201/6213
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing
parallel algorithms |
0.6 | 1 | 2022 | Outperforming Sequential Full-Word Long Addition With Parallelization and Vectorization · IEEE Trans. Parallel Distributed Syst. 2022 |
Processor architecture and microarchitecture
instruction set architecture |
0.2 | 1 | 2022 | Outperforming Sequential Full-Word Long Addition With Parallelization and Vectorization · IEEE Trans. Parallel Distributed Syst. 2022 |
High-performance computing › code optimization
vectorization |
0.2 | 1 | 2022 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Outperforming Sequential Full-Word Long Addition With Parallelization and VectorizationabstractThe 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 |