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  1. Proceedings of the 11th International Workshop on Data Management on New Hardware (DaMoN'15)
  2. Ultra-Fast Similarity Search Using Ternary Content Addressable Memory
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Energy-Efficient In-Memory Data Stores on Hybrid Memory Hierarchies
Beyond the Wall: Near-Data Processing for Databases
Scaling the Memory Power Wall With DRAM-Aware Data Management
NUMA obliviousness through memory mapping
By their fruits shall ye know them: A Data Analyst's Perspective on Massively Parallel System Design
TLB misses: The Missing Issue of Adaptive Radix Tree?
Applying HTM to an OLTP System: No Free Lunch
The Serial Safety Net: Efficient Concurrency Control on Modern Hardware
Efficient Lightweight Compression Alongside Fast Scans
Energy-Efficient Query Processing on Embedded CPU-GPU Architectures
Toward GPUs being mainstream in analytic processing: An initial argument using simple scan-aggregate queries
Ultra-Fast Similarity Search Using Ternary Content Addressable Memory

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Ultra-Fast Similarity Search Using Ternary Content Addressable Memory

Content Provider ACM Digital Library
Author Hel-Or, Yacov Bremler-Barr, Anat Harchol, Yotam Hay, David
Abstract Similarity search, and specifically the nearest-neighbor search (NN) problem is widely used in many fields of computer science such as machine learning, computer vision and databases. However, in many settings such searches are known to suffer from the notorious curse of dimensionality, where running time grows exponentially with d. This causes severe performance degradation when working in high-dimensional spaces. Approximate techniques such as locality-sensitive hashing [2] improve the performance of the search, but are still computationally intensive. In this paper we propose a new way to solve this problem using a special hardware device called ternary content addressable memory (TCAM). TCAM is an associative memory, which is a special type of computer memory that is widely used in switches and routers for very high speed search applications. We show that the TCAM computational model can be leveraged and adjusted to solve NN search problems in a single TCAM lookup cycle, and with linear space. This concept does not suffer from the curse of dimensionality and is shown to improve the best known approaches for NN by more than four orders of magnitude. Simulation results demonstrate dramatic improvement over the best known approaches for NN, and suggest that TCAM devices may play a critical role in future large-scale databases and cloud applications.
Starting Page 1
Ending Page 10
Page Count 10
File Format PDF
ISBN 9781450336383
DOI 10.1145/2771937.2771938
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-05-31
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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