| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
|---|
| Outline of Final Research Achievements |
Recently grid-based physical simulations with multiple GPUs require effective methods to adapt grid resolution to certain sensitive regions of simulations. In this research, we have developed a high-productivity framework for adaptive mesh refinement (AMR); the AMR method is one of the effective methods on GPU to compute certain local regions that demand higher accuracy with higher resolution. This framework allows us to apply AMR to various stencil-based applications on GPU supercomputers. We have developed and implemented a dynamic load balancing method for GPU supercomputers, communication reduction techniques, and optimization techniques for time integration computations to enhance the framework. The 3D compressive fluid simulation based on this proposed framework has achieved a high parallel efficiency and demonstrated the high productivity of the framework.
|
