Scalable parallel numerical constraint solver using global load balancing

By Daisuke Ishii, Kazuki Yoshizoe, Toyotaro Suzumura. 2015

3 citations recorded by the source corpus.

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 search tree of the branch and prune algorithm is split and distributed through the two phases of GLB: a random workload stealing phase and a workload distribution and termination phase based on a hyper-cube-shaped graph called lifeline. The parallel solver is simply 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. Optimal GLB configurations are analyzed.

Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering

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In Computer Networks and Communications

Citations in the corpus

Formal Security Analysis of Neural Networks using Symbolic Intervals (2018, positive): “Due to its capabilities in providing rigorous enclosures of solutions in modeling equations, many numerical optimizations [17, 43] leveraged interval analysis to achieve a near-precise approximation of models and constraints.”