Virginia Chiu

dblp:191/7819 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 67% Memory systems · 33%
Software engineering, system software, and programming languages
1 paper
Concurrent programming · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
cache coherence
0.212016
Exploiting semantic commutativity in hardware speculation · MICRO 2016
Parallel and multicore computing › transactional memory
conflict detection
0.212016
Exploiting semantic commutativity in hardware speculation · MICRO 2016
Parallel and multicore computing › transactional memory
hardware transactional memory
0.212016
Exploiting semantic commutativity in hardware speculation · MICRO 2016
Concurrent programming
speculative execution
0.112016
Exploiting semantic commutativity in hardware speculation · MICRO 2016
Concurrent programming
transactional memory
0.112016
Exploiting semantic commutativity in hardware speculation · MICRO 2016

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

semantic commutativity · 0.5coherence protocol extension · 0.5
YearPublicationVenuePosition
2016 Exploiting semantic commutativity in hardware speculation
abstract
Hardware speculative execution schemes such as hardware transactional memory (HTM) enjoy low run-time overheads but suffer from limited concurrency because they rely on reads and writes to detect conflicts. By contrast, software speculation schemes can exploit semantic knowledge of concurrent operations to reduce conflicts. In particular, they often exploit that many operations on shared data, like insertions into sets, are semantically commutative: they produce semantically equivalent results when reordered. However, software techniques often incur unacceptable run-time overheads. To solve this dichotomy, we present COMMTM, an HTM that exploits semantic commutativity. CommTM extends the coherence protocol and conflict detection scheme to support user-defined commutative operations. Multiple cores can perform commutative operations to the same data concurrently and without conflicts. CommTM preserves transactional guarantees and can be applied to arbitrary HTMs. CommTM scales on many operations that serialize in conventional HTMs, like set insertions, reference counting, and top-K insertions, and retains the low overhead of HTMs. As a result, at 128 cores, CommTM outperforms a conventional eager-lazy HTM by up to 3.4 χ and reduces or eliminates aborts.
Guowei Zhang 0002, Virginia Chiu, Daniel Sánchez 0003
MICRO2