A bridging model for parallel computation
Communications of the ACM
A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Anthill: A Scalable Run-Time Environment for Data Mining Applications
SBAC-PAD '05 Proceedings of the 17th International Symposium on Computer Architecture on High Performance Computing
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Graph Twiddling in a MapReduce World
Computing in Science and Engineering
Evaluating use of data flow systems for large graph analysis
Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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With the increasing "data deluge" scientists face today, the analysis and processing of large datasets of structured data is a daring task. Among such data, large graphs are gaining particular importance with the growing interest on social networks and other complex networks. Given the dimensions considered, parallel processing is essential. However, users are generally not experts in writing parallel code to handle such structures. In this work we present Rendero, a middleware that makes it possible to easily describe graph algorithms in a form adequate for parallel execution. The system is based on the Bulk-Synchronous programming model and offers a vertex-based abstraction. Our current implementation offers good speed-up and scale-up results for large graphs ranging from tens of thousands to millions of vertices and edges in some cases.