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John Byrne

dblp:62/936 · DBLP profile ↗
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4ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · unresolved

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

Databases, data management, data science and information retrieval · 3Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 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
Performance modeling and evaluation · 77% Processor architecture and microarchitecture · 23%
Software engineering, system software, and programming languages
1 paper
Program analysis · 100%

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

TopicWeightPapersLastEvidence papers
Program analysis
dynamic analysis
0.312018
Watching for Software Inefficiencies with Witch · ASPLOS 2018
Processor architecture and microarchitecture
performance monitoring unit
0.112018
Watching for Software Inefficiencies with Witch · ASPLOS 2018

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

sampling · 0.7hardware performance monitoring · 0.7debug registers · 0.7
YearPublicationVenuePosition
2018 Watching for Software Inefficiencies with Witch
abstract
Inefficiencies abound in complex, layered software. A variety of inefficiencies show up as wasteful memory operations. Many existing tools instrument every load and store instruction to monitor memory, which significantly slows execution and consumes enormously extra memory. Our lightweight framework, Witch, samples consecutive accesses to the same memory location by exploiting two ubiquitous hardware features: the performance monitoring units (PMU) and debug registers. Witch performs no instrumentation. Hence, witchcraft---tools built atop Witch---can detect a variety of software inefficiencies while introducing negligible slowdown and insignificant memory consumption and yet maintaining accuracy comparable to exhaustive instrumentation tools. Witch allowed us to scale our analysis to a large number of code bases. Guided by witchcraft, we detected several performance problems in important code bases; eliminating these inefficiencies resulted in significant speedups.
Shasha Wen, Xu Liu 0001, John Byrne, Milind Chabbi
ASPLOS3
2007 Antecedents and consequences of team potency in software development projects
Ali E. Akgün, Halit Keskin, John Byrne, Salih Zeki Imamoglu
Inf. Manag.3
2005 Knowledge networks in new product development projects: A transactive memory perspective
Ali E. Akgün, John Byrne, Halit Keskin, Gary S. Lynn, Salih Zeki Imamoglu
Inf. Manag.2
1998 Neural Networks for Language Identification: A Comparative Study
Shane MacNamara, Padraig Cunningham, John Byrne
Inf. Process. Manag.3