Scheduling or distributing the computational workload over multiple threads is a critical and repeatedly performed activity in graph processing workloads. In a recent paper “Reducing the burden of parallel loop schedulers for many‐core processors” published in Concurrency & Computation: Practice & Experience, we investigated the overhead introduced by scheduling. This […]
GraphGrind
11 posts
https://doi.org/10.1145/3293883.3295703 This work proposes Vertex- and Edge-Balanced Ordering (VEBO): balance the number of edges and the number of unique destinations of those edges. VEBO balances edges and vertices for graphs with a power-law degree distribution, and ensures an equal degree distribution between partitions. Experimental evaluation on three shared-memory graph processing […]
https://manycore.org.uk/summerschool.html The Manycore Summer School gives researchers an opportunity to learn theory and practice in a range of emerging manycore technologies, from seven world-leading academic and industrial researchers. Participants engaged with cutting-edge material in lectures, hands-on labs, and interactive poster sessions. The Manycore Summer School was held from Monday 16th to Friday 20th […]
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 […]
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.
Thesis on QUB Pure PortalThesis in PDF Format Author: Jiawen Sun, https://www.linkedin.com/in/jiawen-sun-33b368103/ As shared memory systems support terabyte-sized main memory, they provide an opportunity to perform efficient graph analytics on a single machine. Graph analytics is characterised by frequent synchronisation, which is addressed in part by shared memory systems. However, […]
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 […]