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arXiv · 2310.02470

SGORP: A Subgradient-based Method for d-Dimensional Rectilinear Partitioning

Muhammed Fatih Balin·Xiaojing An·Abdurrahman Yaşar·Ümit V. Çatalyürek
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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 $d$-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 $3$-dimensional RPPs.

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Muhammed Fatih Balin, Xiaojing An, Abdurrahman Yaşar, Ümit V. Çatalyürek. 2023-10-03. SGORP: A Subgradient-based Method for d-Dimensional Rectilinear Partitioning. https://arxiv.org/abs/2310.02470

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