115 research outputs found
High-Performance Computing for the simulation of particles with the Discrete Element Method
In this talk, we will give an overview of the main techniques used for the parallelization of numerical simulations on High-Performance Computing platforms, and provide a particular focus on the Discrete Element Method (DEM), a numerical method for the simulation of the motion of granular materials. We will cover the main parallelization paradigms and their implementations (shared memory with OpenMP and distributed memory with MPI), present the performance bottlenecks and introduce load-balancing techniques
Optimized Coordinated Checkpoint/Rollback Protocol using a Dataflow Graph Model
Fault-tolerance protocols play an important role in today long runtime scienti\ufb01c parallel applications. The probability of a failure may be important due to the number of unreliable components involved during an execution. We present our approach and preliminary results about a new checkpoint/rollback protocol based on a coordinated scheme. The application is described using a dataflow graph, which is an abstract representation of the execution. Thanks to this representation, the fault recovery in our protocol only requires a partial restart of other processes. Simulations on a domain decomposition application show that the amount of computations required to restart and the number of involved processes are reduced compared to the classical global rollback protocol
The XDEM Multi-physics and Multi-scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications
The XDEM multi-physics and multi-scale simulation platform roots in the Ex-
tended Discrete Element Method (XDEM) and is being developed at the In- stitute
of Computational Engineering at the University of Luxembourg. The platform is
an advanced multi- physics simulation technology that combines flexibility and
versatility to establish the next generation of multi-physics and multi-scale
simulation tools. For this purpose the simulation framework relies on coupling
various predictive tools based on both an Eulerian and Lagrangian approach.
Eulerian approaches represent the wide field of continuum models while the
Lagrange approach is perfectly suited to characterise discrete phases. Thus,
continuum models include classical simulation tools such as Computa- tional
Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended
configuration of the classical Discrete Element Method (DEM) addresses the
discrete e.g. particulate phase. Apart from predicting the trajectories of
individual particles, XDEM extends the application to estimating the thermo-
dynamic state of each particle by advanced and optimised algorithms. The
thermodynamic state may include temperature and species distributions due to
chemical reaction and external heat sources. Hence, coupling these extended
features with either CFD or FEA opens up a wide range of applications as
diverse as pharmaceutical industry e.g. drug production, agriculture food and
processing industry, mining, construction and agricultural machinery, metals
manufacturing, energy production and systems biology
A co-located partitions strategy for parallel CFD-DEM couplings
In this work, a new partition-collocation strategy for the parallel execution
of CFD--DEM couplings is investigated. Having a good parallel performance is a
key issue for an Eulerian-Lagrangian software that aims to be applied to solve
industrially significant problems, as the computational cost of these couplings
is one of their main drawback. The approach presented here consists in
co-locating the overlapping parts of the simulation domain of each software on
the same MPI process, in order to reduce the cost of the data exchanges. It is
shown how this strategy allows reducing memory consumption and inter-process
communication between CFD and DEM to a minimum and therefore to overcome an
important parallelization bottleneck identified in the literature. Three
benchmarks are proposed to assess the consistency and scalability of this
approach. A coupled execution on 280 cores shows that less than 0.1% of the
time is used to perform inter-physics data exchange
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