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Nitin Kunal

dblp:219/9684 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

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

Databases, data management, data science and information retrieval · 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
Query processing and optimization · 50% Indexing and storage engines · 50%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Hardware accelerators and domain-specific architectures · 50% Energy-efficient computing · 50%

Topics — the 1 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Indexing and storage engines
columnar storage
0.312018
RAPID: In-Memory Analytical Query Processing Engine with Extreme Performance per Watt · SIGMOD Conference 2018

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

hardware-software co-design · 0.7
YearPublicationVenuePosition
2018 RAPID: In-Memory Analytical Query Processing Engine with Extreme Performance per Watt
abstract
Today, an ever increasing amount of transistors are packed into processor designs with extra features to support a broad range of applications. As a consequence, processors are becoming more and more complex and power hungry. At the same time, they only sustain an average performance for a wide variety of applications while not providing the best performance for specific applications. In this paper, we demonstrate through a carefully designed modern data processing system called RAPID and a simple, low-power processor specially tailored for data processing that at least an order of magnitude performance/power improvement in SQL processing can be achieved over a modern system running on today's complex processors. RAPID is designed from the ground up with hardware/software co-design in mind to provide architecture-conscious extreme performance while consuming less power in comparison to the modern database systems. The paper presents in detail the design and implementation of RAPID, a relational, columnar, in-memory query processing engine supporting analytical query workloads.
Cagri Balkesen, Nitin Kunal, Georgios Giannikis, Pit Fender, Seema Sundara, Felix Schmidt, Jarod Wen, Sandeep R. Agrawal, Arun Raghavan, Venkatanathan Varadarajan, Anand Viswanathan, Balakrishnan Chandrasekaran 0003, Sam Idicula, Nipun Agarwal, Eric Sedlar
SIGMOD Conference2