https://arxiv.org/abs/1806.06576 Graph partitioning drives graph processing in distributed, disk-based and NUMA-aware systems. A commonly used partitioning goal is to balance the number of edges per partition in conjunction with minimizing the edge or vertex cut. While this type of partitioning is computationally expensive, we observe that such topology-driven partitioning nonetheless […]
Hans Vandierendonck
36 posts
The CSIT Summit brings together researchers and industry with an interest in cyber security challenges. Hans spoke about high-performance graph processing and the relevance for addressing security problems.
The following git repository contains source code of GraphGrind https://github.com/DIPSA-QUB/GraphGrind
https://doi.org/10.1109/ICPP.2017.27 This paper investigates how to improve the memory locality of graph-structured analytics on large-scale shared memory systems. We demonstrate that a graph partitioning where all in-edges for a vertex are placed in the same partition improves memory locality. However, realising performance improvement through such graph partitioning poses several challenges […]
On July 11th, Hans gave a talk on “GraphGrind: Taming Irregular Memory Accesses in Graph Analytics Workloads” at the UK ManyCore workshop https://manycore.org.uk/ukmac2017.html The analysis of graph-structured data is gaining importance due to its relevance to social media and big data. Due to the interconnection patterns in social network graphs, the […]
https://doi.org/10.1145/3079079.3079097 We investigate how graph partitioning adversely affects the performance of graph analytics. We demonstrate that graph partitioning induces extra work during graph traversal and that graph partitions have markedly different connectivity than the original graph. By consequence, increasing the number of partitions reaches a tipping point after which overheads […]