Solving Large Multicommodity Network Flow Problems on GPUs

TL;DR

This paper introduces a GPU-accelerated primal-dual hybrid gradient algorithm for large-scale multicommodity network flow problems, achieving 100 to 1000 times faster solutions than existing methods and enabling scaling to billion-variable problems.

Contribution

It presents a novel GPU-compatible algorithm that efficiently solves large multicommodity network flow problems by exploiting problem-specific features and aggregation techniques.

Findings

Achieves 100x to 1000x speedup over commercial solvers.

Scales to problems with up to a billion variables.

Provides an open-source implementation for broader use.

Abstract

We consider the all-pairs multicommodity network flow problem on a network with capacitated edges. The usual treatment keeps track of a separate flow for each source-destination pair on each edge; we rely on a more efficient formulation in which flows with the same destination are aggregated, reducing the number of variables by a factor equal to the size of the network. Problems with hundreds of nodes, with a total number of variables on the order of a million, can be solved using standard generic interior-point methods on CPUs; we focus on GPU-compatible algorithms that can solve such problems much faster, and in addition scale to much larger problems, with up to a billion variables. Our method relies on the primal-dual hybrid gradient algorithm, and exploits several specific features of the problem for efficient GPU computation. Numerical experiments show that our primal-dual multicommodity network flow method accelerates state of the art generic commercial solvers by $100\times$ to $1000\times$, and scales to problems that are much larger. We provide an open source implementation of our method.