Demo Data from EDBT 2026 / reviewers in the wild / expert
Mohammed Afraz
dblp:167/7856
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 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.
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program analysis
dynamic analysis |
0.2 | 1 | 2015 | P3: partitioned path profiling · ESEC/SIGSOFT FSE 2015 |
Program analysis › dynamic analysis › profiling
path profiling |
0.2 | 1 | 2015 | P3: partitioned path profiling · ESEC/SIGSOFT FSE 2015 |
Parallel and multicore computing › parallel computing › parallel program analysis
parallelism profiling |
0.1 | 1 | 2015 | P3: partitioned path profiling · ESEC/SIGSOFT FSE 2015 |
Parallel and multicore computing
parallel programming models |
0.1 | 1 | 2015 | P3: partitioned path profiling · ESEC/SIGSOFT FSE 2015 |
Methods — techniques the papers use, named apart from their topics
ball-larus algorithm · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | P3: partitioned path profilingabstractAcyclic path profile is an abstraction of dynamic control flow paths of procedures and has been found to be useful in a wide spectrum of activities. Unfortunately, the runtime overhead of obtaining such a profile can be high, limiting its use in practice. In this paper, we present partitioned path profiling (P3) which runs K copies of the program in parallel, each with the same input but on a separate core, and collects the profile only for a subset of intra-procedural paths in each copy, thereby, distributing the overhead of profiling. P3 identifies “profitable” procedures and assigns disjoint subsets of paths of a profitable procedure to different copies for profiling. To obtain exact execution frequencies of a subset of paths, we design a new algorithm, called PSPP. All paths of an unprofitable procedure are assigned to the same copy. P3 uses the classic Ball-Larus algorithm for profiling unprofitable procedures. Further, P3 attempts to evenly distribute the profiling overhead across the copies. To the best of our knowledge, P3 is the first algorithm for parallel path profiling. We have applied P3 to profile several programs in the SPEC 2006 benchmark. Compared to sequential profiling, P3 substantially reduced the runtime overhead on these programs averaged across all benchmarks. The reduction was 23%, 43% and 56% on average for 2, 4 and 8 cores respectively. P3 also performed better than a coarse-grained approach that treats all procedures as unprofitable and distributes them across available cores. For 2 cores, the profiling overhead of P3 was on average 5% less compared to the coarse-grained approach across these programs. For 4 and 8 cores, it was respectively 18% and 25% less. Mohammed Afraz, Diptikalyan Saha, Aditya Kanade 0001 |
ESEC/SIGSOFT FSE | 1 |