Parallel Adaptive Data Structures
While GPUs and other highly parallel devices excel in processing of regularly structured data their large SIMD width and high number of cores quickly leads to inefficiencies in fine-granular branches and complex synchronization. However, adaptive data structures that cause such problems are indispensable to capture multi-scale phenomena. We must rethink our data arrangement in order to reconcile parallel and adaptive requirements.
Adaptive Data Structures
GPUs process data of the same resolution very quickly with massive data parallel execution. But even the massive parallelism cannot compete with adaptive methods when the data size grows cubically under uniform refinement. This project develops parallel refinement strategies with grids and particles that allow to introduce higher resolution in only parts of the computational domain.