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Parallel Direct Simulation Monte Carlo Computation Using CUDA on GPUs

机译:使用CUDA在GPU上的并行直接模拟Monte Carlo计算

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In this study computations of the two-dimensional Direct Simulation Monte Carlo (DSMC) method using Graphics Processing Units (GPUs) are presented. An all-device (GPU) computational approach is adopted-where the entire computation is performed on the GPU device, leaving the CPU idle-which includes particle moving, indexing, collisions between particles and state sampling. The subsequent application to GPU computation requires various changes to the original DSMC method to ensure efficient performance on the GPU device. Communications between the host (CPU) and device (GPU) occur only during problem initialization and simulation conclusion when results are only copied from the device to the host. Several multi-dimensional benchmark tests are employed to demonstrate the correctness of the DSMC implementation. We demonstrate here the application of DSMC using a single-GPU, with speedups of 3approx10 times as compared to a high-end Intel CPU (Intel Xeon X5472) depending upon the size and the level of rarefaction encountered in the simulation.
机译:在本研究中,呈现了使用图形处理单元(GPU)的二维直接仿真蒙特卡罗(DSMC)方法的计算。采用全设备(GPU)计算方法 - 在GPU设备上执行整个计算,使CPU空闲 - 这包括粒子和状态采样之间的粒子移动,索引,碰撞。后续应用于GPU计算需要对原始DSMC方法的各种更改,以确保GPU设备上的有效性能。当结果仅从设备复制到主机时,主机(CPU)和设备(GPU)之间的通信仅发生在问题初始化和仿真结束期间。采用几种多维基准测试来展示DSMC实现的正确性。我们在此证明DSMC使用单个GPU的应用,与高端英特尔CPU(Intel Xeon X5472)相比,使用3Appox10次的加速,具体取决于模拟中遇到的稀疏的稀疏等级。

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