Angen Zheng

Learn More
—Graph partitioning and repartitioning have been widely used by scientists to parallelize compute-and data-intensive 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)
—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)
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)
  • 1