Research – Numerics – Atomistic-Continuum Simulations of Liquids

@article{rossinelli2015a,
author = {Diego Rossinelli and Babak Hejazialhosseini and Wim van Rees and Mattia Gazzola and Michael Bergdorf and Petros Koumoutsakos},
doi = {10.1016/j.jcp.2015.01.035},
journal = {{J. Comput. Phys.}},
month = {may},
pages = {1–18},
publisher = {Elsevier {BV}},
title = {{MRAG}-I2D: Multi-resolution adapted grids for remeshed vortex methods on multicore architectures},
url = {https://drive.google.com/file/d/1pKnaq8Ixc0X_RWG9dju7sqK6agnyT2hO/view?usp=drive_link},
volume = {288},
year = {2015}
}

We present MRAG-I2D, an open source software framework, for multiresolution simulations of two-dimensional, incompressible, viscous flows on multicore architectures. The spatiotemporal scales of the flow field are captured by remeshed vortex methods enhanced by high order average-interpolating wavelets and local time-stepping. The multiresolution solver of the Poisson equation relies on the development of a novel, tree-based multipole method. MRAG-I2D implements a number of HPC strategies to map efficiently the irregular computational workload of wavelet-adapted grids on multicore nodes. The capabilities of the present software are compared to the current state-of-the-art in terms of accuracy, compression rates and time-to-solution. Benchmarks include the inviscid evolution of an elliptical vortex, flow past an impulsively started cylinder at Re = 40 – 40 000 and simulations of self-propelled anguilliform swimmers. The results indicate that the present software has the same or better accuracy than state-of-the-art solvers while it exhibits unprecedented performance in terms of time-to-solution.

@article{rossinelli2011b,
author = {Diego Rossinelli and Babak Hejazialhosseini and Daniele G. Spampinato and Petros Koumoutsakos},
doi = {10.1137/100795930},
journal = {{SIAM J. Sci. Comput.}},
month = {jan},
number = {2},
pages = {512–540},
publisher = {Society for Industrial {\&} Applied Mathematics ({SIAM})},
title = {Multicore/Multi-{GPU} Accelerated Simulations of Multiphase Compressible Flows Using Wavelet Adapted Grids},
url = {https://drive.google.com/file/d/1DJUe8eygpJYx65jP8SCQfGkYY2fTWkjm/view?usp=drive_link},
volume = {33},
year = {2011}
}

We present a computational method of coupling average interpolating wavelets with high-order finite volume schemes and its implementation on heterogeneous computer architectures for the simulation of multiphase compressible flows. The method is implemented to take advantage of the parallel computing capabilities of emerging heterogeneous multicore/multi-GPU architectures. A highly efficient parallel implementation is achieved by introducing the concept of wavelet blocks, exploiting the task-based parallelism for CPU cores, and by managing asynchronously an array of GPUs by means of OpenCL. We investigate the comparative accuracy of the GPU and CPU based simulations and analyze their discrepancy for two-dimensional simulations of shock-bubble interaction and Richtmeyer{–}Meshkov instability. The results indicate that the accuracy of the GPU/CPU heterogeneous solver is competitive with the one that uses exclusively the CPU cores. We report the performance improvements by employing up to 12 cores and 6 GPUs compared to the single-core execution. For the simulation of the shock-bubble interaction at Mach 3 with two million grid points, we observe a 100-fold speedup for the heterogeneous part and an overall speedup of 34.

@article{hejazialhosseini2010a,
author = {Babak Hejazialhosseini and Diego Rossinelli and Michael Bergdorf and Petros Koumoutsakos},
doi = {10.1016/j.jcp.2010.07.021},
journal = {{J. Comput. Phys.}},
month = {nov},
number = {22},
pages = {8364–8383},
publisher = {Elsevier {BV}},
title = {High order finite volume methods on wavelet-adapted grids with local time-stepping on multicore architectures for the simulation of shock-bubble interactions},
url = {https://drive.google.com/file/d/1ObySlMbGfrPbHB2szo_pET_UikmLgDQg/view?usp=drive_link},
volume = {229},
year = {2010}
}

We present a space{–}time adaptive solver for single- and multi-phase compressible flows that couples average interpolating wavelets with high-order finite volume schemes. The solver introduces the concept of wavelet blocks, handles large jumps in resolution and employs local time-stepping for efficient time integration. We demonstrate that the inherently sequential wavelet-based adaptivity can be implemented efficiently in multicore computer architectures using task-based parallelism and introducing the concept of wavelet blocks. We validate our computational method on a number of benchmark problems and we present simulations of shock-bubble interaction at different Mach numbers, demonstrating the accuracy and computational performance of the method.

@article{rossinelli2010b,
author = {Diego Rossinelli and Babak Hejazialhosseini and Michael Bergdorf and Petros Koumoutsakos},
doi = {10.1002/cpe.1639},
journal = {{Concurr. Comp.-Prat. E.}},
month = {aug},
number = {2},
pages = {172–186},
publisher = {Wiley-Blackwell},
title = {Wavelet-adaptive solvers on multi-core architectures for the simulation of complex systems},
url = {https://drive.google.com/file/d/1LJz40d9MQ02bvMNSuj8jzaZlKzg8C5HX/view?usp=drive_link},
volume = {23},
year = {2010}
}

We build wavelet-based adaptive numerical methods for the simulation of advection-dominated flows that develop multiple spatial scales, with an emphasis on fluid mechanics problems. Wavelet-based adaptivity is inherently sequential and in this work we demonstrate that these numerical methods can be implemented in software that is capable of harnessing the capabilities of multi-core architectures while maintaining their computational efficiency. Recent designs in frameworks for multi-core software development allow us to rethink parallelism as task-based, where parallel tasks are specified and automatically mapped onto physical threads. This way of exposing parallelism enables the parallelization of algorithms that were considered inherently sequential, such as wavelet-based adaptive simulations. In this paper we present a framework that combines wavelet-based adaptivity with the task-based parallelism. We demonstrate the promising performance obtained by simulating various physical systems on different multi-core architectures using up to 16 cores.