A novel dynamic load balancing library for cluster computing

In the last few years, research advances in dynamic scheduling at application and runtime system levels have contributed to improving the performance of scientific applications in heterogeneous environments. This paper presents the design and implementation of a library as a result of an integrated approach to dynamic load balancing. This approach combines the advantages of optimizing data migration via novel dynamic loop scheduling strategies with the advances in object migration mechanisms of parallel runtime systems. The performance improvements obtained by the use of this library have been investigated by its use in two scientific applications: the N-body simulations, and the profiling of automatic quadrature routines. The experimental results obtained underscore the significance of using such an integrated approach, as well as the benefits of using the library especially in cluster applications characterized by irregular and unpredictable behavior.