Speeding Up GPU Graph Processing Using Structural Graph
Properties
SessionDoctoral Showcase Session 3
Presenter
Event Type
Doctoral Showcase
Accelerators
Applications
Architectures
Graph Algorithms
Heterogeneous Systems
TimeTuesday, November 14th4:06pm -
4:24pm
Location210-212
DescriptionDue to its wide applicability and flexibility, graph
processing is an increasingly important part of data
science. To scale complex graph analytics computations
to large datasets it is becoming popular to utilise
accelerator-based architectures, such as Graphical
Processing Units (GPUs).
Mapping irregular graph algorithms to hardware designed for highly regular parallelism is a complex task. There are often multiple ways to parallelise the same operation on the GPU. Which of these parallelisation strategies is the fastest is dependent on the structure of the input graph. Performance differences can be an order of magnitude or more, and the optimal strategy varies from graph to graph.
The goal of my PhD research is to identify how structural properties impact the performance of different strategies and use this information to speed-up GPU graph processing by predicting the fastest parallelisation of an algorithm for a specific input graph.
Mapping irregular graph algorithms to hardware designed for highly regular parallelism is a complex task. There are often multiple ways to parallelise the same operation on the GPU. Which of these parallelisation strategies is the fastest is dependent on the structure of the input graph. Performance differences can be an order of magnitude or more, and the optimal strategy varies from graph to graph.
The goal of my PhD research is to identify how structural properties impact the performance of different strategies and use this information to speed-up GPU graph processing by predicting the fastest parallelisation of an algorithm for a specific input graph.
