Skip to Main Content
Parallel computing systems have been based on multicore CPUs and specialized coprocessors, like GPUs. Work-stealing is a scheduling technique that has been used to distribute and redistribute the workload among resources in an efficient way. This work aims to propose, implement and validate a scheduling approach based on work stealing in parallel systems with CPUs and GPUs simultaneously. Results show that our approach, called WORMS, presents competitive performance when compared to reference tool for multicore CPUs (Cilk). In hybrid scenario, WORMS with multicore+GPU outperforms WORMS and Cilk with multicore only and also the GPU reference tool (Thrust).