Parallelization Strategy for Hierarchical Run Length Encoded Data Structures

An efficient parallelization strategy is presented for a Hierarchical Run Length Encoded (HRLE) data structure, implemented for the Sparse Field Level Set method. In order to achieve high parallel efficiency, computational work must be distributed evenly over all available CPU threads. Since the Level Set surface must be allowed to deform and evolve, thereby increasing the simulation area, there must exist a way to increase the surface domain while keeping an efficient parallelization strategy in place. This is achieved by processing the same number of calculations across each available CPU. The addition of data to HRLE data structures is only permitted in a sequential or lexicographical order, making parallelization more complex. The presented solution uses as many HRLE data structures as there are CPUs available. Approximately 90% of operations can be performed in parallel when using the presented strategy, leading to an efficiency of up to 96% or 78.5% when using two or sixteen CPU cores of an AMD Opteron 8435 processor, clocked at 2.6GHz, respectively. Topographies with one and two moving interfaces were simulated using multi-threading, showing the speedup and efficiency for the presented strategy.