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Richard Neill

dblp:97/8109 · DBLP profile ↗
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1ranked-venue papers
1as 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 · 1 first-author

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 · 100%

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

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation › performance monitoring
hardware performance counters
0.312017
Fuse: Accurate Multiplexing of Hardware Performance Counters Across Executions · ACM Trans. Archit. Code Optim. 2017
Performance modeling and evaluation
profiling
0.312017
Fuse: Accurate Multiplexing of Hardware Performance Counters Across Executions · ACM Trans. Archit. Code Optim. 2017
Performance modeling and evaluation
benchmarking
0.112017
Fuse: Accurate Multiplexing of Hardware Performance Counters Across Executions · ACM Trans. Archit. Code Optim. 2017

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

statistical distribution comparison · 0.3
YearPublicationVenuePosition
2017 Fuse: Accurate Multiplexing of Hardware Performance Counters Across Executions
abstract
Collecting hardware event counts is essential to understanding program execution behavior. Contemporary systems offer few Performance Monitoring Counters (PMCs), thus only a small fraction of hardware events can be monitored simultaneously. We present new techniques to acquire counts for all available hardware events with high accuracy by multiplexing PMCs across multiple executions of the same program, then carefully reconciling and merging the multiple profiles into a single, coherent profile. We present a new metric for assessing the similarity of statistical distributions of event counts and show that our execution profiling approach performs significantly better than Hardware Event Multiplexing.
Richard Neill, Andi Drebes, Antoniu Pop
ACM Trans. Archit. Code Optim.1