Coastal ocean forecasting on the GPU using a two-dimensional finite-volume scheme

In this work, we take a modern high-resolution finite-volume scheme for solving the rotational shallow-water equations and extend it with features required to run real-world ocean simulations. Our contributions include a spatially varying north vector and Coriolis term required for large scale domains, moving wet-dry fronts, a static land mask, bottom shear stress, wind forcing, boundary conditions for nesting in a global model, and an efficient model reformulation that makes it well-suited for massively parallel implementations. Our model order is verified using a grid convergence test, and we show numerical experiments using three different sections along the coast of Norway based on data originating from operational forecasts run at the Norwegian Meteorological Institute. Our simulation framework shows perfect weak scaling on a modern P100 GPU, and is capable of providing tidal wave forecasts that are very close to the operational model at a fraction of the cost. All source code and data used in this work are publicly available under open licenses.publishedVersio

Simplified Ocean Models on GPUs

This paper describes the implementation of three different simplified ocean models on a GPU (graphics processing unit) using Python and PyOpenCL. The three models are all based on the solving the shallow water equations on Cartesian grids, and our work is motivated by the aim of running very large ensembles of forecast models for fully nonlinear data assimilation. The models are the linearized shallow water equations, the non-linear shallow water equations, and the two-layer non-linear shallow water equations, respectively, and they contain progressively more physical properties of the ocean dynamics. We show how these models are discretized to run efficiently on a GPU, discuss how to implement them, and show some simulation results. The implementation is available online under an open source license, and may serve as a starting point for others to implement similar oceanographic modelspublishedVersio

Agile supervision of bachelor, master, and PhD theses

Supervision of students can often require a significant mental effort from the supervisor to be effective for the students. When supervising one or two students it is relatively easy to remember the context for each students work from one meeting to the next. However, with a large number of students the mental complexity of the supervision becomes significant. This paper presents the authors experience with using planning boards for supervision of students on bachelor, master, and PhD level to tackle this problem

Simplified Ocean Models on the GPU

This paper describes the implementation of three different simplified ocean models on a GPU (graphics processing unit) using Python and PyOpenCL. The three models are all based on the solving the shallow water equations on Cartesian grids, and our work is motivated by the aim of running very large ensembles of forecast models for fully nonlinear data assimilation. The models are the linearized shallow water equations, the non-linear shallow water equations, and the two-layer non-linear shallow water equations, respectively, and they contain progressively more physical properties of the ocean dynamics. We show how these models are discretized to run efficiently on a GPU, discuss how to implement them, and show some simulation results. The implementation is available online under an open source license, and may serve as a starting point for others to implement similar oceanographic models.&nbsp

GPU Computing in Discrete Optimization Part II: Survey Focused on Routing Problems

In many cases there is still a large gap between the performance of current optimization technology and the requirements of real-world applications. As in the past, performance will improve through a combination of more powerful solution methods and a general performance increase of computers. These factors are not independent. Due to physical limits, hardware development no longer results in higher speed for sequential algorithms, but rather in increased parallelism. Modern commodity PCs include a multi-core CPU and at least one GPU, providing a low-cost, easily accessible heterogeneous environment for high-performance computing. New solution methods that combine task parallelization and stream processing are needed to fully exploit modern computer architectures and profit from future hardware developments. This paper is the second in a series of two. Part I gives a tutorial style introduction to modern PC architectures and GPU programming. Part II gives a broad survey of the literature on parallel computing in discrete optimization targeted at modern PCs, with special focus on routing problems. We assume that the reader is familiar with GPU programming, and refer the interested reader to Part I. We conclude with lessons learnt, directions for future research, and prospects.submittedVersionacceptedVersio

Coastal ocean forecasting on the GPU using a two-dimensional finite-volume scheme

In this work, we take a modern high-resolution finite-volume scheme for solving the rotational shallow-water equations and extend it with features required to run real-world ocean simulations. Our contributions include a spatially varying north vector and Coriolis term required for large scale domains, moving wet-dry fronts, a static land mask, bottom shear stress, wind forcing, boundary conditions for nesting in a global model, and an efficient model reformulation that makes it well-suited for massively parallel implementations. Our model order is verified using a grid convergence test, and we show numerical experiments using three different sections along the coast of Norway based on data originating from operational forecasts run at the Norwegian Meteorological Institute. Our simulation framework shows perfect weak scaling on a modern P100 GPU, and is capable of providing tidal wave forecasts that are very close to the operational model at a fraction of the cost. All source code and data used in this work are publicly available under open licenses

Coastal ocean forecasting on the GPU using a two-dimensional finite-volume scheme

In this work, we take a modern high-resolution finite-volume scheme for solving the rotational shallow-water equations and extend it with features required to run real-world ocean simulations. Our contributions include a spatially varying north vector and Coriolis term required for large scale domains, moving wet-dry fronts, a static land mask, bottom shear stress, wind forcing, boundary conditions for nesting in a global model, and an efficient model reformulation that makes it well-suited for massively parallel implementations. Our model order is verified using a grid convergence test, and we show numerical experiments using three different sections along the coast of Norway based on data originating from operational forecasts run at the Norwegian Meteorological Institute. Our simulation framework shows perfect weak scaling on a modern P100 GPU, and is capable of providing tidal wave forecasts that are very close to the operational model at a fraction of the cost. All source code and data used in this work are publicly available under open licenses

GPU Computing in Discrete Optimization Part I: Introduction to the GPU

In many cases there is still a large gap between the performance of current optimization technology and the requirements of real world applications. As in the past, performance will improve through a combination of more powerful solution methods and a general performance increase of computers. These factors are not independent. Due to physical limits, hardware development no longer results in higher speed for sequential algorithms, but rather in increased parallelism. Modern commodity PCs include a multi-core CPU and at least one GPU, providing a low cost, easily accessible heterogeneous environment for high performance computing. New solution methods that combine task parallelization and stream processing are needed to fully exploit modern computer architectures and profit from future hardware developments. This paper is the first part of a series of two, where the goal of this first part is to give a tutorial style introduction to modern PC architectures and GPU programming. We start with a short historical account of modern mainstream computer architectures, and a brief description of parallel computing. This is followed by the evolution of modern GPUs, before a GPU programming example is given. Strategies and guidelines for program development are also discussed. Part II gives a broad survey of the existing literature on parallel computing targeted at modern PCs in discrete optimization, with special focus on papers on routing problems. We conclude with lessons learnt, directions for future research, and prospects.acceptedVersio