Demo Data from VLDB 2026 / reviewers in the wild / expert
Shruthi Balakrishna
dblp:163/3112
· DBLP profile ↗
1ranked-venue papers
0as 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
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% | |
| Theoretical computer science
1 paper |
Algorithms and data structures · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization
dependence analysis |
0.2 | 1 | 2015 | Tree dependence analysis · PLDI 2015 |
Methods — techniques the papers use, named apart from their topics
path-dependent conditional analysis · 0.4dependence test · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Tree dependence analysisabstractWe develop a new framework for analyzing recursive methods that perform traversals over trees, called tree dependence analysis. This analysis translates dependence analysis techniques for regular programs to the irregular space, identifying the structure of dependences within a recursive method that traverses trees. We develop a dependence test that exploits the dependence structure of such programs, and can prove that several locality- and parallelism- enhancing transformations are legal. In addition, we extend our analysis with a novel path-dependent, conditional analysis to refine the dependence test and prove the legality of transformations for a wider range of algorithms. We then use these analyses to show that several common algorithms that manipulate trees recursively are amenable to several locality- and parallelism-enhancing transformations. This work shows that classical dependence analysis techniques, which have largely been confined to nested loops over array data structures, can be extended and translated to work for complex, recursive programs that operate over pointer-based data structures. Yusheng Weijiang, Shruthi Balakrishna, Jianqiao Liu, Milind Kulkarni 0001 |
PLDI | 2 |