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Hardware Computation of the PageRank Eigenvector

Authors: Séamas McGettrick; Dermot Geraghty;

Hardware Computation of the PageRank Eigenvector

Abstract

A hardware solver, implemented on an FPGA, for calculating the Page Rank eigenvector using the Power method is described. The critical operation is a very large sparse matrix by vector multiplication (SMVM). However, we show that the SMVM can be replaced with a pattern adder (a term coined to denote SMVM without multiplication), and a dense element by element vector multiplication, thus simplifying the calculation and improving performance. The system was tested for correctness and performance using a set of Internet Adjacency matrices. Performance data for Virtex 2 and Virtex 5 implementations are presented and an average performance improvement of 180% over a state of the art general purpose processor was achieved by the Virtex 5 implementation.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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