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Demo Data from SIGIR 2026 / reviewers in the wild / expert

Nazerke Turtayeva

dblp:342/8329 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

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

Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 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
GPUs and heterogeneous computing · 33% Hardware accelerators and domain-specific architectures · 33% Electronic design automation · 33%

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

TopicWeightPapersLastEvidence papers
Hardware accelerators and domain-specific architectures
accelerator programming models		 0.712023
Cohort: Software-Oriented Acceleration for Heterogeneous SoCs · ASPLOS (3) 2023
Electronic design automation
hardware/software co-design		 0.712023
Cohort: Software-Oriented Acceleration for Heterogeneous SoCs · ASPLOS (3) 2023
GPUs and heterogeneous computing › heterogeneous computing systems
heterogeneous acceleration		 0.712023
Cohort: Software-Oriented Acceleration for Heterogeneous SoCs · ASPLOS (3) 2023

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

software abstraction design · 0.7
YearPublicationVenuePosition
2023 Cohort: Software-Oriented Acceleration for Heterogeneous SoCs
abstract
Philosophically, our approaches to acceleration focus on the extreme. We must optimise accelerators to the maximum, leaving software to fix any hardware-software mismatches. Today’s software abstractions for programming accelerators leak hardware details, requiring changes to data formats and manual memory and coherence management, among other issues. This harms generality and requires deep hardware knowledge to efficiently program accelerators, a state which we consider hardware-oriented.
Tianrui Wei, Nazerke Turtayeva, Marcelo Orenes-Vera, Omkar Lonkar, Jonathan Balkind
 ASPLOS (3)2