Demo Data from EDBT 2026 / reviewers in the wild / expert
Monirul Sharif
dblp:81/7749
· 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 · 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 |
Embedded and real-time systems · 77% Parallel and multicore computing · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Embedded and real-time systems
streaming applications |
0.1 | 1 | 2010 | Input-driven dynamic execution prediction of streaming applications · PPoPP 2010 |
Parallel and multicore computing
pipeline parallelism |
0.0 | 1 | 2010 | Input-driven dynamic execution prediction of streaming applications · PPoPP 2010 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Input-driven dynamic execution prediction of streaming applicationsabstractStreaming applications are promising targets for effectively utilizing multicores because of their inherent amenability to pipelined parallelism. While existing methods of orchestrating streaming programs on multicores have mostly been static, real-world applications show ample variations in execution time that may cause the achieved speedup and throughput to be sub-optimal. One of the principle challenges for moving towards dynamic orchestration has been the lack of approaches that can predict or accurately estimate upcoming dynamic variations in execution efficiently, well before they occur. Farhana Aleen, Monirul Sharif, Santosh Pande |
PPoPP | 2 |