| | 2 | * Larger and larger clusters alone will not allow larger and larger simulations to be performed in the same wall time without either an increase in CPU speed or a decrease in the workload of each processor. Since CPU speeds are not expected to keep pace with the requirements of large simulations, the only option is to decrease the work load of each processor. This however requires highly parallelized and efficient algorithms for managing the AMR infrastructure and the necessary computations. Scrambler, unlike many other AMR codes, uses a distributed tree so that each processor is only aware of the AMR structure it needs to be aware of in order to carry out its computations. While currently, this additional memory is small compared to the resources typically available to a CPU, future clusters will have much less memory per processor similar to what is already seen in GPU's. Additionally Scrambler uses a distributed control structure that mirrors the nested AMR grid hierarchy. Processors have child processors just as AMR grids have child grids. These child processors receive new grids, and all necessary tree information from their parent. This eliminates the need for global sharing of tree data. Processors only need to communicate with parent processors(processors containing parent grids), neighbor processors (processors containing adjacent grids), overlapping processors (processors containing previous AMR grids that overlap with the processors current grids), and child processors (processors assigned to child grids). Additionally the allocation of resources among child grids is done using a Hilbert space filling curve. This allows neighboring processors to be physically close on the network (or on the same core) and allows Scrambler to take advantage of the network topology. |
