The drinking philosophers problem
ACM Transactions on Programming Languages and Systems (TOPLAS) - Lecture notes in computer science Vol. 174
A bridging model for parallel computation
Communications of the ACM
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Transaction Processing: Concepts and Techniques
Transaction Processing: Concepts and Techniques
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
Streaming graph partitioning for large distributed graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PowerGraph: distributed graph-parallel computation on natural graphs
OSDI'12 Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation
GraphChi: large-scale graph computation on just a PC
OSDI'12 Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation
Improving large graph processing on partitioned graphs in the cloud
Proceedings of the Third ACM Symposium on Cloud Computing
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Bulk Synchronous Parallelism (BSP) provides a good model for parallel processing of many large-scale graph applications, however it is unsuitable/inefficient for graph applications that require coordination, such as graph-coloring, subcoloring, and clustering. To address this problem, we present an efficient modification to the BSP model to implement serializability (sequential consistency) without reducing the highly-parallel nature of BSP. Our modification bypasses the message queues in BSP and reads directly from the worker's memory for the internal vertex executions. To ensure serializability, coordination is performed--implemented via dining philosophers or token ring-- only for border vertices partitioned across workers. We implement our modifications to BSP on Giraph, an open-source clone of Google's Pregel. We show through a graph-coloring application that our modified framework, Giraphx, provides much better performance than implementing the application using dining-philosophers over Giraph. In fact, Giraphx outperforms Giraph even for embarrassingly parallel applications that do not require coordination, e.g., PageRank.