Graphics processing units (GPUs) have a strong floating-point capability and a high memory bandwidth in data parallelism and have been widely used in high-performance computing (HPC). Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for the GPU to reduce the complexity of programming. The programmable GPUs are becoming popular in computational fluid dynamics (CFD) applications. In this work, we propose a hybrid parallel algorithm of the message passing interface and CUDA for CFD applications on multi-GPU HPC clusters. The AUSM + UP upwind scheme and the three-step Runge–Kutta method are used for spatial discretization and time discretization, respectively. The turbulent solution is solved by the SST two-equation model. The CPU only manages the execution of the GPU and communication, and the GPU is responsible for data processing. Parallel execution and memory access optimizations are used to optimize the GPU-based CFD codes. We propose a nonblocking communication method to fully overlap GPU computing, CPU_CPU communication, and CPU_GPU data transfer by creating two CUDA streams. Furthermore, the one-dimensional domain decomposition method is used to balance the workload among GPUs. Finally, we evaluate the hybrid parallel algorithm with the compressible turbulent flow over a flat plate. The performance of a single GPU implementation and the scalability of multi-GPU clusters are discussed. Performance measurements show that multi-GPU parallelization can achieve a speedup of more than 36 times with respect to CPU-based parallel computing, and the parallel algorithm has good scalability.

/pdf/hybrid-mpi-and-cuda-parallelization-for-cfd-applications-on-q4ezmbkur9.pdf

Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

The present study sheds light on geometric nonlinear static analysis of prismatic shells using the semi-analytical finite strip method. A new computational model, which includes a fully nonlinear compound strip with a longitudinal and transverse stiffener, has been presented. Furthermore, strips with non-uniform characteristics in the longitudinal direction have been used in nonlinear analysis. This has, to the best knowledge of authors, never been reported. Also, this paper describes the design and implementation of eighteen ideal boundary conditions using three different longitudinal and six well-known transverse displacement interpolation functions. The results of the presented study were obtained using an open-source software and multi-purpose software Abaqus. Moreover, the accuracy of the applied computational approach has been verified by comparison with results from the literature. An excellent agreement of displacement fields is achieved for large deflection analyses of plates with a hole and stiffeners as well as for shells with a stepped thickness in the longitudinal direction. Additionally, results from post-buckling analyses of thin-walled structures, a snap-through and snap-back behavior of shallow shells, are matched. The work presented here has profound implications for future studies of the finite strip deployment.

Geometric nonlinear analysis of prismatic shells using the semi-analytical finite strip method

https://eprints.ncl.ac.uk/file_store/production/232833/DE8AE445-2E3F-4777-872F-E040C7DB2BA7.pdf

An optimization approach for agent-based computational models of biological development

Hybrid MPI/OpenMP cloud parallelization of harmonic coupled finite strip method applied on reinforced concrete prismatic shell structure

As the computation power in desktops advances, parallel programming has emerged as one of the essential skills needed by next generation software engineers. However, programs written in popular parallel programming paradigms have a substantial amount of sequential code mixed with the parallel code. Several such versions supporting different platforms are necessary to find the optimum version of the program for the available resources and problem size. As revealed by our study on benchmark programs, sequential code is often duplicated in these versions. This can affect code comprehensibility and re-usability of the software. In this paper, we discuss a framework named PPModel, which is designed and implemented to free programmers from these scenarios. Using PPModel, a programmer can separate parallel blocks in a program, map these blocks to various platforms, and re-execute the entire program. We provide a graphical modeling tool (PPModel) intended for Eclipse users and a Domain-Specific Language (tPPModel) for non-Eclipse users to facilitate the separation, the mapping, and the re-execution. This is illustrated with a case study from a benchmark program, which involves re-targeting a parallel block to CUDA and another parallel block to OpenMP. The modified program gave almost 5× performance gain compared to the sequential counterpart, and 1.5× gain compared to the existing OpenMP version.

PPModel: a modeling tool for source code maintenance and optimization of parallel programs

MPI-CUDA parallelization of a finite-strip program for geometric nonlinear analysis: A hybrid approach

Large displacement stability analysis of thin plate structures: Scope of MPI/OpenMP parallelization in harmonic coupled finite strip analysis

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MPI-CUDA parallelization of a finite-strip program for geometric nonlinear analysis: A hybrid approach

Large displacement stability analysis of thin plate structures: Scope of MPI/OpenMP parallelization in harmonic coupled finite strip analysis