Demo Data from EDBT 2026 / reviewers in the wild / expert
Michael P. Ferguson
dblp:33/11532
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization
compiler optimization |
0.3 | 1 | 2018 | 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 |
0.3 | 1 | 2018 | LAPPS: Locality-Aware Productive Prefetching Support for PGAS · ACM Trans. Archit. Code Optim. 2018 |
Memory systems › cache
prefetching |
0.3 | 1 | 2018 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | LAPPS: Locality-Aware Productive Prefetching Support for PGASabstractPrefetching 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 |
CPM | 1 |