Demo Data from EDBT 2026 / reviewers in the wild / expert
Madhumitha Ravichandran
dblp:58/8277
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, 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 |
Parallel and multicore computing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing › parallel programming runtimes
thread management |
0.1 | 1 | 2010 | Thread tailor: dynamically weaving threads together for efficient, adaptive parallel applications · ISCA 2010 |
Parallel and multicore computing
parallel programming models |
0.0 | 1 | 2010 | Thread tailor: dynamically weaving threads together for efficient, adaptive parallel applications · ISCA 2010 |
Parallel and multicore computing
thread-level parallelism |
0.0 | 1 | 2010 | Thread tailor: dynamically weaving threads together for efficient, adaptive parallel applications · ISCA 2010 |
Methods — techniques the papers use, named apart from their topics
dynamic adaptation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Thread tailor: dynamically weaving threads together for efficient, adaptive parallel applicationsabstractExtracting performance from modern parallel architectures requires that applications be divided into many different threads of execution. Unfortunately selecting the appropriate number of threads for an application is a daunting task. Having too many threads can quickly saturate shared resources, such as cache capacity or memory bandwidth, thus degrading performance. On the other hand, having too few threads makes inefficient use of the resources available. Beyond static resource assignment, the program inputs and dynamic system state (e.g., what other applications are executing in the system) can have a significant impact on the right number of threads to use for a particular application. Janghaeng Lee, Haicheng Wu, Madhumitha Ravichandran, Nathan Clark |
ISCA | 3 |