Accelerating solid–fluid interaction based on the immersed boundary method on multicore and GPU architectures

This work proposes several approaches to accelerate the solid–fluid interaction through the use of the Immersed Boundary method on multicore and GPU architectures. Different optimizations on both architectures have been proposed, focusing on memory management and workload mapping. We have chosen two different test scenarios which consist of single-solid and multiple-solid simulations. The performance analysis has been carried out on an intensive set of test cases to analyze the proposed optimizations using multiple CPUs (2) and GPUs (4). An effective performance is obtained for single-solid executions using one CPU (Intel Xeon E5520) achieving a speedup peak equal to 5.5. It is reached a higher benefit on multiple solids obtaining a top speedup of approximately 5.9 and 9 using one CPU (8 cores) and two CPUs (16 cores), respectively. On GPU (Kepler K20c) architecture, two different approaches are presented as the best alternative: one for single-solid executions and one for multiple-solid executions. The best approach obtained for one solid executions achieves a speedup of approximately 17 with respect the sequential counterpart. In contrast, for multiple-solid executions the benefit is much higher, being this type of problems much more suitable for GPU and reaching a peak speedup of 68, 115 and 162 using 1, 2 and 4 GPUs, respectively.