We present a parallel framework for simulating incompressible fluids with predictive-corrective incompressible smoothed particle\nhydrodynamics (PCISPH) on the GPU in real time. To this end, we propose an efficient GPU streaming pipeline to map the entire\ncomputational task onto the GPU, fully exploiting the massive computational power of state-of-the-art GPUs. In PCISPH-based\nsimulations, neighbor search is the major performance obstacle because this process is performed several times at each time step.\nTo eliminate this bottleneck, an efficient parallel sorting method for this time-consuming step is introduced.Moreover, we discuss\nseveral optimization techniques including using fast on-chip shared memory to avoid global memory bandwidth limitations and\nthus further improve performance on modern GPU hardware. With our framework, the realism of real-time fluid simulation\nis significantly improved since our method enforces incompressibility constraint which is typically ignored due to efficiency\nreason in previous GPU-based SPH methods. The performance results illustrate that our approach can efficiently simulate realistic\nincompressible fluid in real time and results in a speed-up factor of up to 23 on a high-end NVIDIA GPU in comparison to singlethreaded\nCPU-based implementation.
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