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Cuda Implementation Of A Navier-Stokes Solver On Multi-Gpu Desktop Platforms For Incompressible Flows, Julien C. Thibault, Inanc Senocak
Cuda Implementation Of A Navier-Stokes Solver On Multi-Gpu Desktop Platforms For Incompressible Flows, Julien C. Thibault, Inanc Senocak
Inanc Senocak
Graphics processor units (GPU) that are traditionally designed for graphics rendering have emerged as massively-parallel "co-processors" to the central processing unit (CPU). Small-footprint desktop supercomputers with hundreds of cores that can deliver teraflops peak performance at the price of conventional workstations have been realized. A computational fluid dynamics (CFD) simulation capability with rapid computational turnaround time has the potential to transform engineering analysis and design optimization procedures. We describe the implementation of a Navier-Stokes solver for incompressible fluid flow using desktop platforms equipped with multi-GPUs. Specifically, NVIDIA’s Compute Unified Device Architecture (CUDA) programming model is used to implement the discretized …
An Mpi-Cuda Implementation For Massively Parallel Incompressible Flow Computations On Multi-Gpu Clusters, Dana A. Jacobsen, Julien C. Thibault, Inanc Senocak
An Mpi-Cuda Implementation For Massively Parallel Incompressible Flow Computations On Multi-Gpu Clusters, Dana A. Jacobsen, Julien C. Thibault, Inanc Senocak
Inanc Senocak
Modern graphics processing units (GPUs) with many-core architectures have emerged as general-purpose parallel computing platforms that can accelerate simulation science applications tremendously. While multi-GPU workstations with several TeraFLOPS of peak computing power are available to accelerate computational problems, larger problems require even more resources. Conventional clusters of central processing units (CPU) are now being augmented with multiple GPUs in each compute-node to tackle large problems. The heterogeneous architecture of a multi-GPU cluster with a deep memory hierarchy creates unique challenges in developing scalable and efficient simulation codes. In this study, we pursue mixed MPI-CUDA implementations and investigate three strategies to …
Stochastic Event Reconstruction Of Atmospheric Contaminant Dispersion Using Bayesian Inference, Inanc Senocak, Nicolas W. Hengartner, Margaret B. Short, W. Brent Daniel
Stochastic Event Reconstruction Of Atmospheric Contaminant Dispersion Using Bayesian Inference, Inanc Senocak, Nicolas W. Hengartner, Margaret B. Short, W. Brent Daniel
Inanc Senocak
Environmental sensors have been deployed in various cities for early detection of contaminant releases into the atmosphere. Event reconstruction and improved dispersion modeling capabilities are needed to estimate the extent of contamination, which is required to implement effective strategies in emergency management. To this end, a stochastic event reconstruction capability that can process information from an environmental sensor network is developed. A probability model is proposed to take into account both zero and non-zero concentration measurements that can be available from a sensor network because of a sensor’s specified limit of detection. The inference is based on the Bayesian paradigm …