Year: 2020
Type: preprint
Abstract: Extensive prior research has focused on alleviating the characteristic poor cache locality of graph analytics workloads. However, graph pre-processing tasks remain relatively unexplored. In many important scenarios, graph pre-processing tasks can be as expensive as the downstream graph analytics kernel. We observe that Propagation Blocking (PB), a software optimization designed for SpMV kernels, generalizes to many graph analytics kernels as well as common pre-processing tasks. In this work, we identify the lingering inefficiencies of a PB execution on conventional multicores and propose architecture support to eliminate PB's bottlenecks, further improving the performance gains from PB. Our proposed architecture -- COBRA -- optimizes the PB execution of both graph processing and pre-processing alike to provide end-to-end speedups of up to 4.6x (3.5x on average).
Source: arXiv (Cornell University)
Authors: Balaji, Vignesh, Lucia, Brandon
Institution: Carnegie Mellon University
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3105028439