VLDB 2026 Research / reviewers in the wild / expert

Wiktor Jakubiuk

dblp:195/6403 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

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

Systems, architecture and hardware · 1

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 · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › parallel algorithms
parallel algorithm design		 0.312017
A Multicore Path to Connectomics-on-Demand · PPoPP 2017
Bioinformatics and computational biology › computational neuroscience
connectomics		 0.112017
A Multicore Path to Connectomics-on-Demand · PPoPP 2017
YearPublicationVenuePosition
2017 A Multicore Path to Connectomics-on-Demand
abstract
The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led to believe that this type of horizontal scaling can reduce or even eliminate the need for traditional algorithm development, careful parallelization, and performance engineering. This paper is a case study showing the contrary: that the benefits of algorithms, parallelization, and performance engineering, can sometimes be so vast that it is possible to solve "cluster-scale" problems on a single commodity multicore machine.
Alexander Matveev, Yaron Meirovitch, Hayk Saribekyan, Wiktor Jakubiuk, Tim Kaler, Gergely Ódor, David M. Budden, Aleksandar Zlateski, Nir Shavit
 PPoPP4