We present new algorithms for the parallelization of Eulerian-Lagrangian interaction operations in the immersed boundary method. Our algorithms rely on two well-studied parallel primitives: key-value sort and segmented reduce. The use of these parallel primitives allows us to implement our algorithms on both graphics processing units (GPUs) and on other shared memory architectures. We present strong and weak scaling tests on problems involving scattered points and elastic structures. Our tests show that our algorithms exhibit near-ideal scaling on both multicore CPUs and GPUs.

翻译：我们在浸入的边界法中提出了Eularian-Lagrangian互动操作平行化的新算法。 我们的算法依赖于两个研究周密的平行原始:关键值排序和分解的缩小。 这些平行原始法的使用使我们能够在图形处理器(GPUs)和其他共享的记忆结构上实施我们的算法。 我们对分散点和弹性结构的问题进行了强弱的缩放测试。 我们的算法显示,我们的算法在多核心CPUs和GPUs上都展示了接近理想的缩放。