Communication Schemes of a Parallel Fluid Solver for Multi-scale Environmental Simulations

Frisch, Jérôme; Mundani, Ralf-Peter; Rank, Ernst

doi:10.1109/synasc.2011.7

Cited by 9 publications

(16 citation statements)

References 8 publications

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“…In [22], the basic communication routines were introduced, and they are displayed in Figure 2. The parallelization strategy is based on a distribution of logical grids to different processes, where the data grids can use their internal finite difference stencil and the ghost halos are exchanged at specific times.…”

Section: Data Exchangementioning

confidence: 99%

Engineering-Based Thermal CFD Simulations on Massive Parallel Systems

et al. 2015

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The development of parallel Computational Fluid Dynamics (CFD) codes is a challenging task that entails efficient parallelization concepts and strategies in order to achieve good scalability values when running those codes on modern supercomputers with several thousands to millions of cores. In this paper, we present a hierarchical data structure for massive parallel computations that supports the coupling of a Navier-Stokes-based fluid flow code with the Boussinesq approximation in order to address complex thermal scenarios for energy-related assessments. The newly designed data structure is specifically designed with the idea of interactive data exploration and visualization during runtime of the simulation code; a major shortcoming of traditional high-performance computing (HPC) simulation codes. We further show and discuss speed-up values obtained on one of Germany's top-ranked supercomputers with up to 140,000 processes and present simulation results for different engineering-based thermal problems.

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Section: Data Exchangementioning

confidence: 99%

Engineering-Based Thermal CFD Simulations on Massive Parallel Systems

et al. 2015

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“…Next experiments addressed a full time step update, i. e. an iterative solution of the pressure Poisson equation until convergence was reached. The setup was exactly the same as in the previous experiment, namely a fully refined 3D domain using l-grid refinement levels of (2,2,2) and d-grid sizes of (16,16,16) for different depths (5)(6)(7)(8), leading to around 20 million l-grids with more than 78.5 billion d-grid cells and over 707 billion variables on depth 8. It is worth notifying that this setup also requires 28 TByte of combined main memory for storing all relevant data.…”

Section: Scaling Resultsmentioning

confidence: 99%

Measuring and Comparing the Scaling Behaviour of a High-Performance CFD Code on Different Supercomputing Infrastructures

Frisch

Mundani

2015

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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Parallel code design is a challenging task especially when addressing petascale systems for massive parallel processing (MPP), i. e. parallel computations on several hundreds of thousands of cores. An in-house computational fluid dynamics code, developed by our group, was designed for such high-fidelity runs in order to exhibit excellent scalability values. Basis for this code is an adaptive hierarchical data structure together with an efficient communication and (numerical) computation scheme that supports MPP. For a detailled scalability analysis, we performed several experiments on two of Germany's national supercomputers up to 140,000 processes. In this paper, we will show the results of those experiments and discuss any bottlenecks that could be observed while solving engineering-based problems such as porous media flows or thermal comfort assessments for problem sizes up to several hundred billion degrees of freedom.

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“…The communication phase consists of 3 sequential steps as described in Frisch et al First of all, all d‐grids that have not yet been updated during the computation phase are set to the averaged values of their corresponding children d‐grids in a bottom‐up step. In a second, horizontal step, all adjacent d‐grids update their ghost layers before all resulting ghost layers of d‐grids are set properly on different levels (due to an adaptive grid refinement) in a final top‐down step.The l‐grid management hereby has to pay attention to flux conservation across d‐grid boundaries to guarantee data integrity and consistency.…”

Section: Fluid Flow Simulationsmentioning

confidence: 99%

Design and optimisation of an efficient HDF5 I/O Kernel for massive parallel fluid flow simulations

Ertl

Frisch

Mundani

2017

Concurrency and Computation

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Summary More and more massive parallel codes running on several hundreds of thousands of cores are entering the computational science and engineering domain, allowing high‐fidelity computations on up to trillions of unknowns for very detailed analyses of the underlying problems. Such runs typically produce gigabytes of data, hindering both efficient storage and (interactive) data exploration. Advanced approaches based on inherently distributed data formats such as hierarchical data format version 5 become necessary here to avoid long latencies when storing the data and to support fast (random) access when retrieving the data for visual processing. This paper shows considerations and implementation aspects of an I/O kernel based on hierarchical data format version 5 that supports fast checkpointing, restarting, and selective visualisation using a single shared output file for an existing computational fluid dynamics framework. This functionality is achieved by including the framework's hierarchical data structure in the file, which also opens the door for additional steering functionality. Finally, the performance of the kernel's write routines are presented. Bandwidths close to the theoretical peak on modern supercomputing clusters were achieved by avoiding file‐locking and using collective buffering.

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Communication Schemes of a Parallel Fluid Solver for Multi-scale Environmental Simulations

Cited by 9 publications

References 8 publications

Engineering-Based Thermal CFD Simulations on Massive Parallel Systems

Engineering-Based Thermal CFD Simulations on Massive Parallel Systems

Measuring and Comparing the Scaling Behaviour of a High-Performance CFD Code on Different Supercomputing Infrastructures

Design and optimisation of an efficient HDF5 I/O Kernel for massive parallel fluid flow simulations

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