Computational Gravitational Dynamics with Modern Numerical Accelerators

We review the recent optimizations of gravitational $N$-body kernels for running them on graphics processing units (GPUs), on single hosts and massive parallel platforms. For each of the two main $N$-body techniques, direct summation and tree-codes, we discuss the optimization strategy, which is different for each algorithm. Because both the accuracy as well as the performance characteristics differ, hybridizing the two algorithms is essential when simulating a large $N$-body system with high-density structures containing few particles, and with low-density structures containing many particles. We demonstrate how this can be realized by splitting the underlying Hamiltonian, and we subsequently demonstrate the efficiency and accuracy of the hybrid code by simulating a group of 11 merging galaxies with massive black holes in the nuclei.

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