Abstract: A boundary element method (BEM) for large-scale acoustic analysis is accelerated efficiently and precisely with Graphics Processing Units (GPUs). Based on Burton-Miller boundary integral equation, an implementation scheme that can be handled efficiently in GPU is derived and applied to accelerate conventional BEM. Data caching techniques in GPU are introduced to improve efficiency of the prototype algorithm. A double-single precision algorithm implemented with single-precision floating-point numbers is employed to reduce numerical errors. It shows that the improved algorithm sustained a highest GPU efficiency of 89.8% for large-scale problems, and its accuracy was almost the same as that with double-precision numerals directly while costing only 1/28 in time and half in GPU memory consumption of the latter. The largest problem size up to 3 million unknowns was solved rapidly on a desktop PC (8GB RAM, NVIDIA GeForce 660 Ti) by the method. Its performance was better than the fast BEM algorithms in both time and memory consumption.

Key words: acoustics, large-scale problems, GPU computing, optimization algorithm