OpenMP parallelization of the SCIARA Cellular Automata lava flow model: Performance analysis on shared-memory computers

Parallel Computing represents a valid solution for reducing execution times in simulations of complex geological processes, such as lava flows, debris flows and, in general, of fluid-dynamic processes. In these cases, Cellular Automata (CA) models have proved to be effective when the behavior of the system to be modeled can be described in terms of local interactions among its constituent parts. Cellular Automata are parallel computing models, discrete in space and time; space is generally subdivided into cells of uniform size and the overall dynamics of the system emerges as the result of the simultaneous application, at discrete time steps, of proper local rules of evolution to each one of them. Due to their intrinsic parallelism, CA models are attractive since they are suitable to be effectively and naturally implemented on parallel computers achieving also high performance. In the recent past, CA models were efficiently executed on distributed memory architectures, such as Beowulf clusters and many-node Supercomputers, while fewer implementations are found regarding shared-memory computers, such as in multi-core machines. This paper shows performance results of the parallelization of a well-known CA model for simulating lava flows – the SCIARA model – in a shared memory environment, by means of OpenMP, an Application Programming Interface which supports multi-platform shared-memory parallel programming.