This session will introduce a novel, optimization-based workload distribution algorithm that exploits heterogeneous systems to accelerate various proximity queries. To represent complicated performance relationships between computing resources and different computations of proximity queries, we propose a simple model that measures the expected running time of these computations. Based on this model, we formulate an optimization problem that minimizes the largest time spent on computing resources, and propose a novel, iterative LP-based scheduling algorithm. We apply our method into various proximity queries used in five different applications that have different characteristics. Our method achieves an order of magnitude performance improvement by using four different GPUs and two hexa-core CPUs over using a hexa-core CPU only. In addition, we integrate our expected running time model with a work stealing method and achieve 16% performance improvement on average over the basicl work stealing method.