Demo Data from EDBT 2026 / reviewers in the wild / expert
Albert Noll
dblp:17/2404
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 1 first-authorSoftware 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
3 papers |
Compilers and program optimization · 57% Debugging and program repair · 36% Runtime systems and virtual machines · 6% | |
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
Parallel and multicore computing · 100% |
Topics — the 8 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Debugging and program repair
record and replay |
0.3 | 1 | 2017 | Engineering Record and Replay for Deployability · USENIX ATC 2017 |
Compilers and program optimization › dynamic optimization
profile-guided optimization |
0.2 | 1 | 2013 | Online feedback-directed optimizations for parallel Java code · OOPSLA 2013 |
Parallel and multicore computing
parallel programming runtimes |
0.2 | 1 | 2013 | Online feedback-directed optimizations for parallel Java code · OOPSLA 2013 |
Parallel and multicore computing
task granularity |
0.2 | 1 | 2013 | Online feedback-directed optimizations for parallel Java code · OOPSLA 2013 |
Compilers and program optimization
dynamic optimization |
0.1 | 1 | 2012 | An infrastructure for dynamic optimization of parallel programs · PPoPP 2012 |
Compilers and program optimization
parallel program optimization |
0.1 | 1 | 2012 | An infrastructure for dynamic optimization of parallel programs · PPoPP 2012 |
Runtime systems and virtual machines
online optimization |
0.0 | 1 | 2013 | Online feedback-directed optimizations for parallel Java code · OOPSLA 2013 |
Parallel and multicore computing
parallel programming models |
0.0 | 1 | 2012 | An infrastructure for dynamic optimization of parallel programs · PPoPP 2012 |
Methods — techniques the papers use, named apart from their topics
feedback-directed optimization · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Engineering Record and Replay for Deployability
Robert O'Callahan, Nathan Froyd, Kyle Huey, Albert Noll, Nimrod Partush |
USENIX ATC | 5 |
| 2013 | Online feedback-directed optimizations for parallel Java codeabstractThe performance of parallel code significantly depends on the parallel task granularity (PTG). If the PTG is too coarse, performance suffers due to load imbalance; if the PTG is too fine, performance suffers from the overhead that is induced by parallel task creation and scheduling. Albert Noll, Thomas R. Gross |
OOPSLA | 1 |
| 2012 | An infrastructure for dynamic optimization of parallel programsabstractObject-oriented programming languages like Java provide only low-level constructs (e.g., starting a thread) to describe concurrency. High-level abstractions (e.g., thread pools) are merely provided as a library. As a result, a compiler is not aware of the high-level semantics of a parallel library and therefore misses important optimization opportunities. This paper presents a simple source language extension based on which a compiler is provided with the opportunity to perform new optimizations that are particularly effective for parallel code. Albert Noll, Thomas R. Gross |
PPoPP | 1 |