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- Angen Zheng, Alexandros Labrinidis, Panos K. Chrysanthis
- 2014 IEEE International Conference on Big Data…
- 2014

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)

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)

- Angen Zheng, Alexandros Labrinidis, Panos K. Chrysanthis
- 2016 IEEE 32nd International Conference on Data…
- 2016

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)

- Angen Zheng, Alexandros Labrinidis, Panos K. Chrysanthis, Jack Lange
- 2016 IEEE International Conference on Big Data…
- 2016

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)

- Angen Zheng
- 2017

Developed an efficient benchmark to measure the relative network communication costs among machines. Developed three architecture-aware graph (re)partitioners using MPI for vertex-centric distributed graph computation, achieving up to 12x speedups on three classic graph workloads: BFS, SSSP and PageRank. Developed a skew-resistant graph partitioner for… (More)

- Angen Zheng, Jack Lange
- 2014

Because of the increasing complexity of the applications running in Kitten, a lightweight HPC OS targeted for compute nodes of massively-parallel, distributed-memory supercom-puters, 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)

- Angen Zheng
- 2014

In modern parallel architectures, cores belonging to the same NUMA node usually content for the shared LLC, Front Side Bus, and memory controller. Thus, different workload placements may result in different performance impact on the workloads running on these NUMA nodes. This performance impact is known as cross-workload interference. The goal of this… (More)

- Angen Zheng, Alexandros Labrinidis, Christos Faloutsos
- 2017 IEEE 33rd International Conference on Data…
- 2017

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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