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  1. Proceedings of the 5th Workshop on Irregular Applications: Architectures and Algorithms (IA3 '15)
  2. PL2AP: fast parallel cosine similarity search
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How to match in parallel: approximation algorithms and multicore machines
A scalable architecture for ordered irregular parallelism
Data-centric GPU-based adaptive mesh refinement
Scalable task-based algorithm for multiplication of block-rank-sparse matrices
Tensor-matrix products with a compressed sparse tensor
Generalised vectorisation for sparse matrix: vector multiplication
Hybrid memory cube performance characterization on data-centric workloads
PL2AP: fast parallel cosine similarity search
PathFinder: a signature-search miniapp and its runtime characteristics
A GPU-parallel construction of volumetric tree
Dynamic parallelism for simple and efficient GPU graph algorithms
Betweenness centrality on Multi-GPU systems
GAIL: the graph algorithm iron law
Improving graph partitioning for modern graphs and architectures

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PL2AP: fast parallel cosine similarity search

Content Provider ACM Digital Library
Author Anastasiu, David C. Karypis, George
Abstract Solving the AllPairs similarity search problem entails finding all pairs of vectors in a high dimensional sparse dataset that have a similarity value higher than a given threshold. The output form this problem is a crucial component in many real-world applications, such as clustering, online advertising, recommender systems, near-duplicate document detection, and query refinement. A number of serial algorithms have been proposed that solve the problem by pruning many of the possible similarity candidates for each query object, after accessing only a few of their non-zero values. The pruning process results in unpredictable memory access patterns that can reduce search efficiency. In this context, we introduce pL2AP, which efficiently solves the AllPairs cosine similarity search problem in a multi-core environment. Our method uses a number of cache-tiling optimizations, combined with fine-grained dynamically balanced parallel tasks, to solve the problem 1.5x-238x faster than existing parallel baselines on datasets with hundreds of millions of non-zeros.
Starting Page 1
Ending Page 8
Page Count 8
File Format PDF
ISBN 9781450340014
DOI 10.1145/2833179.2833182
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-11-15
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Similarity join Cosine similarity Bounded cosine similarity graph Similarity search
Content Type Text
Resource Type Article
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