SGORP: A Subgradient-based Method for d-Dimensional Rectilinear Partitioning
Abstract
Partitioning for load balancing is a crucial first step to parallelize any type of computation. In this work, we propose SGORP, a new spatial partitioning method based on Subgradient Optimization, to solve the -dimensional Rectilinear Partitioning Problem (RPP). Our proposed method allows the use of customizable objective functions as well as some user-specific constraints, such as symmetric partitioning on selected dimensions. Extensive experimental evaluation using over 600 test matrices shows that our algorithm achieves favorable performance against the state-of-the-art RPP and Symmetric RPP algorithms. Additionally, we show the effectiveness of our algorithm to do application-specific load balancing using two applications as motivation: Triangle Counting and Sparse Matrix Multiplication (SpGEMM), where we model their load-balancing problems as -dimensional RPPs.
Cite
@article{arxiv.2310.02470,
title = {SGORP: A Subgradient-based Method for d-Dimensional Rectilinear Partitioning},
author = {Muhammed Fatih Balin and Xiaojing An and Abdurrahman Yaşar and Ümit V. Çatalyürek},
journal= {arXiv preprint arXiv:2310.02470},
year = {2023}
}