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Zviad Metreveli

dblp:59/10954 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none

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

Systems, architecture and hardware · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 61% Memory systems · 30% Storage systems · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems › cache management
cache partitioning
0.112012
CPHASH: a cache-partitioned hash table · PPoPP 2012
Parallel and multicore computing
concurrent data structures
0.112012
CPHASH: a cache-partitioned hash table · PPoPP 2012
Parallel and multicore computing › concurrent data structures
concurrent hashtable
0.112012
CPHASH: a cache-partitioned hash table · PPoPP 2012
Storage systems
key-value storage
0.012012
CPHASH: a cache-partitioned hash table · PPoPP 2012

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

message passing · 0.1lock-free synchronization · 0.1
YearPublicationVenuePosition
2012 CPHASH: a cache-partitioned hash table
abstract
CPHash is a concurrent hash table for multicore processors. CPHash partitions its table across the caches of cores and uses message passing to transfer lookups/inserts to a partition. CPHash's message passing avoids the need for locks, pipelines batches of asynchronous messages, and packs multiple messages into a single cache line transfer. Experiments on a 80-core machine with 2 hardware threads per core show that CPHash has ~1.6x higher throughput than a hash table implemented using fine-grained locks. An analysis shows that CPHash wins because it experiences fewer cache misses and its cache misses are less expensive, because of less contention for the on-chip interconnect and DRAM. CPServer, a key/value cache server using CPHash, achieves ~5% higher throughput than a key/value cache server that uses a hash table with fine-grained locks, but both achieve better throughput and scalability than memcached. The throughput of CPHash and CPServer also scale near-linearly with the number of cores.
Zviad Metreveli, Nickolai Zeldovich, M. Frans Kaashoek
PPoPP1