Toward efficient GPU-accelerated N-body simulations
N-body algorithms are applicable to a number of common problems in computational physics including gravitation, electrostatics, and fluid dynamics. Fast algorithms (those with better than O(N2) performance) exist, but have not been successfully implemented on GPU hardware for practical problems. In the present work, we introduce not only best-in-class performance for a multipole-accelerated treecode method, but a series of improvements that support implementation of this solver on highly-data-parallel graphics processing units (GPUs). The greatly reduced computation times suggest that this problem is ideally suited for the current and next generations of single and cluster CPU-GPU architectures. We believe that this is an ideal method for practical computation of large-scale turbulent flows on future supercomputing hardware using parallel vortex particle methods.