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One of the major challenges in parallelization is the difficulty of improving application scalability with conventional techniques. HPX provides efficient scalable parallelism by significantly reducing node starvation and effective latencies while controlling the overheads. In this paper, we present a new highly scalable parallel distributed N-Body application using a future-based algorithm, which is implemented with HPX. The main difference between this algorithm and prior art is that a future-based request buffer is used between different nodes and along each spatial direction to send/receive data to/from the remote nodes, which helps removing synchronization barriers. HPX provides an asynchronous programming model which results in improving the parallel performance. The results of using HPX for parallelizing Octree construction on one node and the force computation on the distributed nodes show the scalability improvement on an average by about 45% compared to an equivalent OpenMP implementation and 28% compared to a hybrid implementation (MPI+OpenMP) [1] respectively for one billion particles running on up to 128 nodes with 20 cores per each. 

We present a highly scalable demonstration of a portable asynchronous many-task programming model and runtime system applied to a grid-based adaptive mesh refinement hydrodynamic simulation of a double white dwarf merger with 14 levels of refinement that spans 17 orders of magnitude in astrophysical densities. The code uses the portable C++ parallel programming model that is embodied in the HPX library and being incorporated into the ISO C++ standard. The model represents a significant shift from existing bulk synchronous parallel programming models under consideration for exascale systems. Through the use of the Futurization technique, seemingly sequential code is transformed into wait-free asynchronous tasks. We demonstrate the potential of our model by showing results from strong scaling runs on National Energy Research Scientific Computing Center’s Cori system (658,784 Intel Knight’s Landing cores) that achieve a parallel efficiency of 96.8% using billions of asynchronous tasks.