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This is a miniapp for the Energy Banding Monte Carlo (EBMC) neutron transportation simulation code. It is adapted from a similar miniapp provided by Andrew Siegel, whose algorithm is described in , where only one process in a compute node is used, and the compute nodes are divided into memory nodes and tracking nodes. Memory nodes do not participate in particle tracking. Obviously, there is a lot of resource waste in this design. To improve problems stated above, we developed this miniapp with optimizations made possible by MPI-3.0. In this miniapp, there is no distinction between memory nodes and tracking nodes. All cores on all nodes are tracking particles. In the code, r nodes comprise a memory group, such that there are np/(r*ppn) memory groups in total. Here np is total number of processes and ppn is the number processes per node. Cross section data is distributed on r nodes per memory group. Within a compute node, we use MPI-3.0 shared memory windows to shared band data between MPI processes. Between compute nodes, we use non-blocking calls to fetch band data on remote nodes. With non-blocking calls, we can start fetching data for band k+1 when we begin to track particles in band k, in effect achieving computation and communication overlapping. We have two methods to fetch remote band data. In the first version of the miniapp (ebmc-iallgather), we use MPI_Iallgather, the nonblocking version of MPI_Allgather. In ebmc-iallgather, nodes of a memory group work in lockstep from the first band to the last band. Working in lockstep is a constraint. However, the benefit is that MPI runtimes have global information of the communication pattern and might do optimized communication scheduling. In the second version of the miniapp (ebmc-rget), we use MPI_Rget, a one-sided RMA operation newly defined by MPI-3.0. In ebmc-rget, nodes of a memory group work independently. The drawback is that the communication scheduling might be suboptimal. In our experiments on Blues of LCRC at ANL, ebmc-iallgather had the best performance, giving 15x speedup over the miniapp in . Questions: Please contact Junchao Zhang <email@example.com>  Felker, Kyle G., Andrew R. Siegel, Kord S. Smith, Paul K. Romano, and Benoit Forget. "The energy band memory server algorithm for parallel Monte Carlo transport calculations." In SNA+ MC 2013-Joint International Conference on Supercomputing in Nuclear Applications+ Monte Carlo, p. 04207. EDP Sciences, 2014.