Uri Silbershtein

dblp:08/5665 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—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
Operating systems · 50% Runtime systems and virtual machines · 50%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Operating systems › resource management
memory management
0.012004
An efficient parallel heap compaction algorithm · OOPSLA 2004
Runtime systems and virtual machines › garbage collection
parallel garbage collection
0.012004
An efficient parallel heap compaction algorithm · OOPSLA 2004
Parallel and multicore computing › multiprocessor system
shared-memory multiprocessor
0.012004
An efficient parallel heap compaction algorithm · OOPSLA 2004

Methods — techniques the papers use, named apart from their topics

parallel moving strategy · 0.1forwarding pointer · 0.1
YearPublicationVenuePosition
2004 An efficient parallel heap compaction algorithm
abstract
We propose a heap compaction algorithm appropriate for modern computing environments. Our algorithm is targeted at SMP platforms. It demonstrates high scalability when running in parallel but is also extremely efficient when running single-threaded on a uniprocessor. Instead of using the standard forwarding pointer mechanism for updating pointers to moved objects, the algorithm saves information for a pack of objects. It then does a small computation to process this information and determine each object's new location. In addition, using a smart parallel moving strategy, the algorithm achieves (almost) perfect compaction in the lower addresses of the heap, whereas previous algorithms achieved parallelism by compacting within several predetermined segments.
Diab Abuaiadh, Yoav Ossia, Erez Petrank, Uri Silbershtein
OOPSLA4