Demo Data from EDBT 2026 / reviewers in the wild / expert
Stephanie C. Tsao
dblp:30/614
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2
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 · 50% Processor architecture and microarchitecture · 50% | |
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Processor architecture and microarchitecture
many-core architecture |
0.1 | 1 | 2007 | Implicitly Parallel Programming Models for Thousand-Core Microprocessors · DAC 2007 |
Parallel and multicore computing
parallel programming models |
0.1 | 1 | 2007 | Implicitly Parallel Programming Models for Thousand-Core Microprocessors · DAC 2007 |
Compilers and program optimization › parallelization
automatic parallelization |
0.0 | 1 | 2007 | Implicitly Parallel Programming Models for Thousand-Core Microprocessors · DAC 2007 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Accelerating advanced MRI reconstructions on GPUs
Sam S. Stone, Justin P. Haldar, Stephanie C. Tsao, Wen-Mei W. Hwu, Bradley P. Sutton, Zhi-Pei Liang |
J. Parallel Distributed Comput. | 3 |
| 2007 | Implicitly Parallel Programming Models for Thousand-Core MicroprocessorsabstractThis paper argues for an implicitly parallel programming model for many-core microprocessors, and provides initial technical approaches towards this goal. In an implicitly parallel programming model, programmers maximize algorithm-level parallelism, express their parallel algorithms by asserting high-level properties on top of a traditional sequential programming language, and rely on parallelizing compilers and hardware support to perform parallel execution under the hood. In such a model, compilers and related tools require much more advanced program analysis capabilities and programmer assertions than what are currently available so that a comprehensive understanding of the input program's concurrency can be derived. Such an understanding is then used to drive automatic or interactive parallel code generation tools for a diverse set of parallel hardware organizations. The chip-level architecture and hardware should maintain parallel execution state in such a way that a strictly sequential execution state can always be derived for the purpose of verifying and debugging the program. We argue that implicitly parallel programming models are critical for addressing the software development crises and software scalability challenges for many-core microprocessors. Wen-Mei W. Hwu, Shane Ryoo, Sain-Zee Ueng, John H. Kelm, Isaac Gelado, Sam S. Stone, Robert E. Kidd, Sara S. Baghsorkhi, Aqeel Mahesri, Stephanie C. Tsao, Nacho Navarro, Steven S. Lumetta, Matthew I. Frank, Sanjay J. Patel |
DAC | 10 |