In this paper, parallel Relaxed and Extrapolated algorithms based on the Power method for accelerating the PageRank computation are presented. Different parallel implementations of the Power method and the proposed variants are analyzed using different data distribution strategies. The reported experiments show the behavior and effectiveness of the designed algorithms for realistic test data using either OpenMP, MPI or an hybrid OpenMP/MPI approach to exploit the benefits of shared memory inside the nodes of current SMP supercomputers.This research was partially supported by the Spanish Ministry of Science and Innovation under Grant Number TIN2011-26254
PageRank algorithm is a benchmark for many graph analytics and is the underlying kernel for link pre...
AbstractWe observe that the convergence patterns of pages in the PageRank algorithm have a nonunifor...
PageRank kernel is a standard benchmark addressing various graph processing and analytical problems....
The PageRank algorithm for determining the importance of Web pages has become a central technique in...
In this work we present parallel algorithms based on the use of two-stage methods for solving the Pa...
In this work, a non-stationary technique based on the Power method for accelerating the parallel com...
The PageRank algorithm is an important component in effective web search. At the core of this algori...
Cataloged from PDF version of article.The PageRank algorithm is an important component in effective ...
The PageRank method is an important and basic component in effective web search to compute the rank ...
Abstract. We present a novel technique for speeding up the computation of PageRank, a hyperlink-base...
For computing PageRank problems, a Power–Arnoldi algorithm is presented by periodically knitting the...
PageRank is the measure of importance of a node within a set of nodes. It was originally developed f...
A power method formulation, which efficiently handles the problem of dangling pages, is investigated...
This paper presents different parallel implementations of Google’s PageRank algorithm. The purpose i...
PageRank becomes the most well-known re-ranking technique of the search results. By its iterative co...
PageRank algorithm is a benchmark for many graph analytics and is the underlying kernel for link pre...
AbstractWe observe that the convergence patterns of pages in the PageRank algorithm have a nonunifor...
PageRank kernel is a standard benchmark addressing various graph processing and analytical problems....
The PageRank algorithm for determining the importance of Web pages has become a central technique in...
In this work we present parallel algorithms based on the use of two-stage methods for solving the Pa...
In this work, a non-stationary technique based on the Power method for accelerating the parallel com...
The PageRank algorithm is an important component in effective web search. At the core of this algori...
Cataloged from PDF version of article.The PageRank algorithm is an important component in effective ...
The PageRank method is an important and basic component in effective web search to compute the rank ...
Abstract. We present a novel technique for speeding up the computation of PageRank, a hyperlink-base...
For computing PageRank problems, a Power–Arnoldi algorithm is presented by periodically knitting the...
PageRank is the measure of importance of a node within a set of nodes. It was originally developed f...
A power method formulation, which efficiently handles the problem of dangling pages, is investigated...
This paper presents different parallel implementations of Google’s PageRank algorithm. The purpose i...
PageRank becomes the most well-known re-ranking technique of the search results. By its iterative co...
PageRank algorithm is a benchmark for many graph analytics and is the underlying kernel for link pre...
AbstractWe observe that the convergence patterns of pages in the PageRank algorithm have a nonunifor...
PageRank kernel is a standard benchmark addressing various graph processing and analytical problems....