Christopher M. Dee

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dblp:175/6605 · 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.

Databases, data mining, and information retrieval 1 paper		Information retrieval · 100%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Hardware accelerators and domain-specific architectures · 77% GPUs and heterogeneous computing · 23%

Topics — the 3 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Information retrieval › similarity search high-dimensional similarity search	0.2	1	2016	Exploiting accelerators for efficient high dimensional similarity search · PPoPP 2016
Information retrieval similarity search	0.2	1	2016	Exploiting accelerators for efficient high dimensional similarity search · PPoPP 2016
Hardware accelerators and domain-specific architectures › domain-specific accelerator vector search accelerator	0.2	1	2016	Exploiting accelerators for efficient high dimensional similarity search · PPoPP 2016

Year	Publication	Venue	Position
2016	Exploiting accelerators for efficient high dimensional similarity search abstract Similarity search finds the most similar matches in an object collection for a given query; making it an important problem across a wide range of disciplines such as web search, image recognition and protein sequencing. Practical implementations of High Dimensional Similarity Search (HDSS) search across billions of possible solutions for multiple queries in real time, making its performance and efficiency a significant challenge. Existing clusters and datacenters use commercial multicore hardware to perform search, which may not provide the optimal performance and performance per Watt. Sandeep R. Agrawal, Christopher M. Dee, Alvin R. Lebeck	PPoPP	2