Dynamic load balancing on heterogeneous multicore/multiGPU systems

Parallel computing in heterogeneous environments is drawing considerable attention due to the growing number of these kind of systems. Adapting existing code and libraries to such systems is a fundamental problem. The performance of this code is affected by the large interdependence between the code and these parallel architectures. We have developed a dynamic load balancing library that allows parallel code to be adapted to heterogeneous systems for a wide variety of problems. The overhead introduced by our system is minimal and the cost to the programmer negligible. The strategy was applied to a Dynamic Programming Algorithm, the Resource Allocation Problem. This code has been implemented on different heterogeneous architectures, including an heterogeneous cluster, a multicore system, a single GPU, and a multi-GPU system. The unbalance nature of the RAP algorithm shows the success of our load balancing library on such architectures.