Adaptive Mesh Refinement methods reduce computational requirements of problems by increasing resolution for only areas of interest. However, in practice, efficient AMR implementations are difficult considering that the mesh hierarchy management must be optimized for the underlying hardware. Architecture complexity of GPUs can render efficient AMR to be particularity challenging in GPU-accelerated supercomputers. This talk presents a compiler-based high-level framework that can automatically transform serial uniform mesh code annotated by the user into parallel adaptive mesh code optimized for GPU-accelerated supercomputers. We show experimental results on three production applications. The speedups of code generated by our framework are comparable to hand-written AMR code while achieving good strong and weak scaling up to 3600 GPUs.

Bio: Mohamed Wahib is currently a postdoctoral researcher in the “HPC Programming Framework Research Team” at RIKEN Advanced Institute for Computational Science (RIKEN AICS). He joined RIKEN AICS in 2012 after he received Ph.D. in Computer Science from Hokkaido University, Japan. Prior to his graduate studies, he worked as a researcher at Texas Instruments (TI) R&D for four years. Mohamed’s research is focused on accelerators and data-centric programming models.