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Sandeep Sikka

dblp:85/478 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none

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

Artificial intelligence and machine learning · 1Computer networks · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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 networks
1 paper
Routing and switching · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 77% GPUs and heterogeneous computing · 23%
Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Routing and switching
IP lookup
0.012000
Memory-efficient state lookups with fast updates · SIGCOMM 2000
Routing and switching › IP lookup
longest prefix matching
0.012000
Memory-efficient state lookups with fast updates · SIGCOMM 2000
Routing and switching
packet forwarding
0.012000
Memory-efficient state lookups with fast updates · SIGCOMM 2000
Memory systems › memory management
memory allocation
0.012000
Memory-efficient state lookups with fast updates · SIGCOMM 2000
GPUs and heterogeneous computing
packet processing
0.012000
Memory-efficient state lookups with fast updates · SIGCOMM 2000

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

pipelining · 0.1memory allocation algorithms · 0.0memory allocation algorithm · 0.0game-theoretic optimization · 0.0
YearPublicationVenuePosition
2000 Memory-efficient state lookups with fast updates
abstract
Routers must do a best matching prefix lookup for every packet; solutions for Gigabit speeds are well known. As Internet link speeds higher, we seek a scalable solution whose speed scales with memory speeds while allowing large prefix databases. In this paper we show that providing such a solution requires careful attention to memory allocation and pipelining. This is because fast lookups require on-chip or off-chip SRAM which is limited by either expense or manufacturing process. We show that doing so while providing guarantees on the number of prefixes supported requires new algorithms and the breaking down of traditional abstraction boundaries between hardware and software. We introduce new problem-specific memory allocators that have provable memory utilization guarantees that can reach 100%; this is contrast to all standard allocators that can only guarantee 20% utilization when the requests can come in the range [1 ... 32]. An optimal version of our algorithm requires a new (but feasible) SRAM memory design that allows shifted access in addition to normal word access. Our techniques generalize to other IP lookup schemes and to other state lookups besides prefix lookup.
Sandeep Sikka, George Varghese
SIGCOMM1
1999 Algorithms for Optimizing Leveled Commitment Contracts
Tuomas Sandholm, Sandeep Sikka, Samphel Norden
IJCAI2