Uniting Control and Data Parallelism: Towards Scalable Memory-Driven Dynamic Graph Processing
Abstract
Control parallelism and data parallelism is mostly reasoned and optimized as separate functions. Because of this, workloads that are irregular, fine-grain and dynamic such as dynamic graph processing become very hard to scale. An experimental research approach to computer architecture that synthesizes prior techniques of parallel computing along with new innovations is proposed in this paper. We establish the background and motivation of the research undertaking and provide a detailed description of the proposed omputing system that is highly parallel non-von Neumann, memory-centric and memory-driven. We also present a message-driven (or even-driven) programming model called "diffusive computation" and provide insights into its properties using SSSP and Triangle Counting problems as examples.
Cite
@article{arxiv.2202.09418,
title = {Uniting Control and Data Parallelism: Towards Scalable Memory-Driven Dynamic Graph Processing},
author = {Bibrak Qamar Chandio and Thomas Sterling and Prateek Srivastava},
journal= {arXiv preprint arXiv:2202.09418},
year = {2023}
}
Comments
The paper did not publish and we are working on a new paper that is very different than this one but contains some information that is in this paper