Angen Zheng

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With the explosion of large, dynamic graph datasets from various fields, graph partitioning and repartitioning are becoming more and more critical to the performance of many graph-based Big Data applications, such as social analysis, web search, and recommender systems. However, well-studied graph (re)partitioners usually assume a homogeneous and(More)
Graph partitioning and repartitioning have been widely used by scientists to parallelize compute- and dataintensive simulations. However, existing graph (re)partitioning algorithms usually assume homogeneous communication costs among partitions, which contradicts the increasing heterogeneity in inter-core communication in modern parallel architectures and(More)
Graph partitioning is an essential preprocessing step in distributed graph computation and scientific simulations. Existing well-studied graph partitioners are designed for static graphs, but real-world graphs, such as social networks and Web networks, keep changing dynamically. In fact, the communication and computation patterns of some graph algorithms(More)
The increasing popularity and ubiquity of various large graph datasets has caused renewed interest for graph partitioning. Existing graph partitioners either scale poorly against large graphs or disregard the impact of the underlying hardware topology. A few solutions have shown that the nonuniform network communication costs may affect the performance(More)
Because of the increasing complexity of the applications running in Kitten, a lightweight HPC OS targeted for compute nodes of massively-parallel, distributed-memory supercomputers, and the complex hardware that Kitten is running on, bugs are becoming more difficult to find. As a result, the need for Kitten to support user-level application debugging(More)
Large graph datasets have caused renewed interest for graph partitioning. However, existing well-studied graph partitioners often assume that vertices of the graph are always active during the computation, which may lead to time-varying skewness for traversal-style graph workloads, like Breadth First Search, since they only explore part of the graph in each(More)
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