Parallelization of a 3-Dimensional Hydrodynamics Model Using a Hybrid Method with MPI and OpenMP

Abstract

Process-based numerical models developed to perform hydraulic/hydrologic/water quality analysis of watersheds and rivers have become highly sophisticated, with a corresponding increase in their computation time. However, for incidents such as water pollution, rapid analysis and decision-making are critical. This paper proposes an optimized parallelization scheme to reduce the computation time of the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model, which has been continuously developed for water pollution or algal bloom prediction in rivers. An existing source code and a parallel computational code with open multi-processing (OpenMP) and a message passing interface (MPI) were optimized, and their computation times compared. Subsequently, the simulation results for the existing EFDC model and the model with the parallel computation code were compared. Furthermore, the optimal parallel combination for hybrid parallel computation was evaluated by comparing the simulation time based on the number of cores and threads. When code parallelization was applied, the performance improved by a factor of approximately five compared to the existing source code. Thus, if the parallel computational source code applied in this study is used, urgent decision-making will be easier for events such as water pollution incidents.

A Hybrid MPI-OpenMP Parallel Algorithm for the Assessment of the Multifractal Spectrum of River Networks

“…As we will show below, indeed, the improvements obtained with the adoption of the OpenMP paradigm are relevant with respect to the pure MPI case. Moreover, the application of both the paradigms (MPI and OpenMP) is not at all new in hydraulics applications (see, for instance, [14][15][16] for relevant hydraulic applications, albeit in different contexts).…”

Fast and Accurate Solution of Integral Formulations of Large MQS Problems Based on Hybrid OpenMP–MPI Parallelization

“…Therefore, the actual available memory at each node, M AV , is reduced with respect to M N , and must be larger than the requested chunk memory, M LOC . These conditions are summarized in (11), that sets the limit for the pure MPI approach by imposing a maximum value to N MPI :…”

“…In order to speed up the inter-thread computation and to fully gain from the modern CPU architecture, M DT should match the local core cache size (cache coherence, e.g., [27]). Let us now define as N T the number of threads per node: if we use one MPI process per node, and after considering that M D is allocated only once, Condition (11) on the available memory for the pure MPI approach is replaced for the hybrid OpenMP-MPI one by the following:…”

“…In view of comparing the two approaches, it is convenient to introduce the following speed-up parameters: S MPI = N MPI (for pure MPI approach) and S H = N T N N (for hybrid OpenMP-MPI). They can be expressed from (11) and (12), as follows:…”

“…More recently, this problem has been addressed by using hybrid parallel programming paradigms, like those that adopt both a parametrized communication paradigm, such as the message passing interface, MPI [7], and an application programming interface, such as OpenMP [8,9]. Such a hybrid programming approach has been used in several fields, such as computational fluid dynamics [10,11], chemical simulations [12,13], geotechnics [14], high efficiency video coding [15], and electromagnetic simulations [16][17][18], due to its very interesting performance [19]. As pointed out in [19,20], the advantages of using a hybrid OpenMP-MPI programming approach are many, as follows: (i) suitability to the architecture of modern supercomputers, with interconnected shared memory nodes; (ii) improved scalability; (iii) optimization of the total consumed memory; (iv) reduction in the number of MPI processes.…”