Abstract: As energy consumption becomes a major design concern of supercomputers, three design trends emerge in supercomputer architectures: massive parallelism, deep memory and network hierarchy, and heterogeneous computing. Large scale computing on such supercomputers as Tianhe-2 requires the load balancing algorithms with three properties: fast, minimal data movement cost, and load balance among heterogeneous devices such as CPU cores and accelerators. On the other hand, multi-physics and multi-scale applications are becoming ubiquitous for many challenge scientific simulations, which results in non-uniform load distribution and demands powerful load balancing algorithms. In this paper, we propose a load balancing algorithm with the above properties by combining a nested partitioning scheme, a greedy partitioning algorithm and an inner-outer subdomain partitioning algorithm. Model experiment shows our algorithm can guarantee good load balance efficiency. Furthermore, experiment on Tianhe-2 with 32 nodes shows our algorithm is able to achieve low communication cost. Finally, experiments of 5 real applications on Tianhe-2 with 936 thousand CPU and MIC cores show that, our algorithm can support large scale simulations efficiently.