Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Michael P. Ferguson

dblp:33/11532 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
0since 2021 · last 2018
0009-0003-7031-0549ORCID · corroborated

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

Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 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
Parallel and multicore computing · 50% Memory systems · 50%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

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

TopicWeightPapersLastEvidence papers
Compilers and program optimization
compiler optimization
0.312018
LAPPS: Locality-Aware Productive Prefetching Support for PGAS · ACM Trans. Archit. Code Optim. 2018
Parallel and multicore computing › parallel programming models › distributed memory programming models
partitioned global address space
0.312018
LAPPS: Locality-Aware Productive Prefetching Support for PGAS · ACM Trans. Archit. Code Optim. 2018
Memory systems › cache
prefetching
0.312018
LAPPS: Locality-Aware Productive Prefetching Support for PGAS · ACM Trans. Archit. Code Optim. 2018

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

user-driven prefetching · 0.7
YearPublicationVenuePosition
2018 LAPPS: Locality-Aware Productive Prefetching Support for PGAS
abstract
Prefetching is a well-known technique to mitigate scalability challenges in the Partitioned Global Address Space (PGAS) model. It has been studied as either an automated compiler optimization or a manual programmer optimization. Using the PGAS locality awareness, we define a hybrid tradeoff. Specifically, we introduce locality-aware productive prefetching support for PGAS. Our novel, user-driven approach strikes a balance between the ease-of-use of compiler-based automated prefetching and the high performance of the laborious manual prefetching. Our prototype implementation in Chapel shows that significant scalability and performance improvements can be achieved with minimal effort in common applications.
Engin Kayraklioglu, Michael P. Ferguson, Tarek A. El-Ghazawi
ACM Trans. Archit. Code Optim.2
2012 FEMTO: Fast Search of Large Sequence Collections
Michael P. Ferguson
CPM1