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Albert Noll

dblp:17/2404 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Debugging and program repair
record and replay
0.312017
Engineering Record and Replay for Deployability · USENIX ATC 2017
Compilers and program optimization › dynamic optimization
profile-guided optimization
0.212013
Online feedback-directed optimizations for parallel Java code · OOPSLA 2013
Parallel and multicore computing
parallel programming runtimes
0.212013
Online feedback-directed optimizations for parallel Java code · OOPSLA 2013
Parallel and multicore computing
task granularity
0.212013
Online feedback-directed optimizations for parallel Java code · OOPSLA 2013
Compilers and program optimization
dynamic optimization
0.112012
An infrastructure for dynamic optimization of parallel programs · PPoPP 2012
Compilers and program optimization
parallel program optimization
0.112012
An infrastructure for dynamic optimization of parallel programs · PPoPP 2012
Runtime systems and virtual machines
online optimization
0.012013
Online feedback-directed optimizations for parallel Java code · OOPSLA 2013
Parallel and multicore computing
parallel programming models
0.012012
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
YearPublicationVenuePosition
2017 Engineering Record and Replay for Deployability
Robert O'Callahan, Nathan Froyd, Kyle Huey, Albert Noll, Nimrod Partush
USENIX ATC5
2013 Online feedback-directed optimizations for parallel Java code
abstract
The 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
OOPSLA1
2012 An infrastructure for dynamic optimization of parallel programs
abstract
Object-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
PPoPP1