Scalable Parallel Numerical Constraint Solver Using Global Load Balancing
This work addresses scalability issues in numerical constraint solving for researchers and practitioners, though it is incremental as it applies an existing method to parallelization.
The authors tackled the challenge of solving numerical constraint satisfaction problems efficiently by developing a scalable parallel solver using global load balancing, achieving up to 516-fold speedup on 600 cores.
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.