Scalable Parallel Numerical Constraint Solver Using Global Load Balancing

TL;DR

This paper introduces a scalable parallel solver for numerical constraint satisfaction problems that leverages global load balancing, achieving significant speedups on supercomputers through efficient parallelization.

Contribution

It presents a novel parallelization scheme using global load balancing implemented in X10, enabling efficient solving of NCSPs on large-scale supercomputers.

Findings

Achieved up to 516-fold speedup with 600 cores

Demonstrated scalability on supercomputing infrastructure

Solved several benchmark NCSPs efficiently

Abstract

We present a scalable parallel solver for numerical constraint satisfaction problems (NCSPs). Our parallelization scheme consists of homogeneous worker solvers, each of which runs on an available core and communicates with others via the global load balancing (GLB) method. The parallel solver is implemented with X10 that provides an implementation of GLB as a library. In experiments, several NCSPs from the literature were solved and attained up to 516-fold speedup using 600 cores of the TSUBAME2.5 supercomputer.