Demo Data from EDBT 2026 / reviewers in the wild / expert
Qifan Chang
dblp:252/4636
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—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.
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 50% Embedded and real-time systems · 50% | |
| Theoretical computer science
1 paper |
Algorithms and data structures · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization › parallelization
automatic parallelization |
0.4 | 1 | 2019 | D2P: from recursive formulations to distributed-memory codes · SC 2019 |
Compilers and program optimization › code generation › parallel code generation
distributed-memory code generation |
0.4 | 1 | 2019 | D2P: from recursive formulations to distributed-memory codes · SC 2019 |
Embedded and real-time systems › model-based design
code generation |
0.4 | 1 | 2019 | D2P: from recursive formulations to distributed-memory codes · SC 2019 |
Parallel and multicore computing › parallel programming models
distributed memory programming |
0.4 | 1 | 2019 | D2P: from recursive formulations to distributed-memory codes · SC 2019 |
Algorithms and data structures
dynamic programming |
0.1 | 1 | 2019 | D2P: from recursive formulations to distributed-memory codes · SC 2019 |
Methods — techniques the papers use, named apart from their topics
recursive divide-and-conquer · 1.1MPI · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | D2P: from recursive formulations to distributed-memory codesabstractRecursive formulations of programs are straightforward to reason about and write, often have good locality properties, and readily expose parallelism. We observe that it is easier to automatically generate distributed-memory codes for recursive formulations with certain properties: i) inclusive---a recursive method's parameters summarize the data access done within the method body. ii) Intersection---data-set intersection tests among method invocations can be computed efficiently. In this paper we present D2P, a system that automatically generates distributed-memory codes for recursive divide-conquer algorithms with these properties. D2P produces MPI-based implementations starting from shared-memory specifications of the recursive algorithms. We evaluate D2P with recursive Dynamic Programming (DP) algorithms, since these algorithms have the desired properties and are well known. We show that the generated implementations are scalable and efficient: D2P-generated implementations execute faster than implementations generated by recent distributed DP frameworks, and are competitive with (and often faster than) hand-written implementations. Nikhil Hegde, Qifan Chang, Milind Kulkarni 0001 |
SC | 2 |