EDBT 2026 Demo / reviewers in the wild / expert
Joshua Lopez
dblp:130/9914
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
—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 · 77% Performance modeling and evaluation · 23% | |
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing › parallel computing
parallel program analysis |
0.2 | 1 | 2013 | Parallel scaling properties from a basic block view · SIGMETRICS 2013 |
Performance modeling and evaluation
profiling |
0.0 | 1 | 2013 | Parallel scaling properties from a basic block view · SIGMETRICS 2013 |
Methods — techniques the papers use, named apart from their topics
parallel block vector profiling · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Parallel scaling properties from a basic block viewabstractAs software scalability lags behind hardware parallelism, understanding scaling behavior is more important than ever. This paper demonstrates how to use Parallel Block Vector (PBV) profiles to measure the scaling properties of multithreaded programs from a new perspective: the basic block's view. Through this lens, we guide users through quick and simple methods to produce high-resolution application scaling analyses. This method requires no manual program modification, new hardware, or lengthy simulations, and captures the impact of architecture, operating systems, threading models, and inputs. We apply these techniques to a set of parallel benchmarks, and, as an example, demonstrate that when it comes to scaling, functions in an application do not behave monolithically. Melanie Kambadur, Kui Tang, Joshua Lopez, Martha A. Kim |
SIGMETRICS | 3 |