4,380 research outputs found
Task-based adaptive multiresolution for time-space multi-scale reaction-diffusion systems on multi-core architectures
A new solver featuring time-space adaptation and error control has been
recently introduced to tackle the numerical solution of stiff
reaction-diffusion systems. Based on operator splitting, finite volume adaptive
multiresolution and high order time integrators with specific stability
properties for each operator, this strategy yields high computational
efficiency for large multidimensional computations on standard architectures
such as powerful workstations. However, the data structure of the original
implementation, based on trees of pointers, provides limited opportunities for
efficiency enhancements, while posing serious challenges in terms of parallel
programming and load balancing. The present contribution proposes a new
implementation of the whole set of numerical methods including Radau5 and
ROCK4, relying on a fully different data structure together with the use of a
specific library, TBB, for shared-memory, task-based parallelism with
work-stealing. The performance of our implementation is assessed in a series of
test-cases of increasing difficulty in two and three dimensions on multi-core
and many-core architectures, demonstrating high scalability
Hydra: A Parallel Adaptive Grid Code
We describe the first parallel implementation of an adaptive
particle-particle, particle-mesh code with smoothed particle hydrodynamics.
Parallelisation of the serial code, ``Hydra'', is achieved by using CRAFT, a
Cray proprietary language which allows rapid implementation of a serial code on
a parallel machine by allowing global addressing of distributed memory.
The collisionless variant of the code has already completed several 16.8
million particle cosmological simulations on a 128 processor Cray T3D whilst
the full hydrodynamic code has completed several 4.2 million particle combined
gas and dark matter runs. The efficiency of the code now allows parameter-space
explorations to be performed routinely using particles of each species.
A complete run including gas cooling, from high redshift to the present epoch
requires approximately 10 hours on 64 processors.
In this paper we present implementation details and results of the
performance and scalability of the CRAFT version of Hydra under varying degrees
of particle clustering.Comment: 23 pages, LaTex plus encapsulated figure
Adaptive Partitioning for Large-Scale Dynamic Graphs
Abstract—In the last years, large-scale graph processing has gained increasing attention, with most recent systems placing particular emphasis on latency. One possible technique to improve runtime performance in a distributed graph processing system is to reduce network communication. The most notable way to achieve this goal is to partition the graph by minimizing the num-ber of edges that connect vertices assigned to different machines, while keeping the load balanced. However, real-world graphs are highly dynamic, with vertices and edges being constantly added and removed. Carefully updating the partitioning of the graph to reflect these changes is necessary to avoid the introduction of an extensive number of cut edges, which would gradually worsen computation performance. In this paper we show that performance degradation in dynamic graph processing systems can be avoided by adapting continuously the graph partitions as the graph changes. We present a novel highly scalable adaptive partitioning strategy, and show a number of refinements that make it work under the constraints of a large-scale distributed system. The partitioning strategy is based on iterative vertex migrations, relying only on local information. We have implemented the technique in a graph processing system, and we show through three real-world scenarios how adapting graph partitioning reduces execution time by over 50 % when compared to commonly used hash-partitioning. I
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing
This work introduces an innovative parallel, fully-distributed finite element
framework for growing geometries and its application to metal additive
manufacturing. It is well-known that virtual part design and qualification in
additive manufacturing requires highly-accurate multiscale and multiphysics
analyses. Only high performance computing tools are able to handle such
complexity in time frames compatible with time-to-market. However, efficiency,
without loss of accuracy, has rarely held the centre stage in the numerical
community. Here, in contrast, the framework is designed to adequately exploit
the resources of high-end distributed-memory machines. It is grounded on three
building blocks: (1) Hierarchical adaptive mesh refinement with octree-based
meshes; (2) a parallel strategy to model the growth of the geometry; (3)
state-of-the-art parallel iterative linear solvers. Computational experiments
consider the heat transfer analysis at the part scale of the printing process
by powder-bed technologies. After verification against a 3D benchmark, a
strong-scaling analysis assesses performance and identifies major sources of
parallel overhead. A third numerical example examines the efficiency and
robustness of (2) in a curved 3D shape. Unprecedented parallelism and
scalability were achieved in this work. Hence, this framework contributes to
take on higher complexity and/or accuracy, not only of part-scale simulations
of metal or polymer additive manufacturing, but also in welding, sedimentation,
atherosclerosis, or any other physical problem where the physical domain of
interest grows in time
Design and Analysis of a Task-based Parallelization over a Runtime System of an Explicit Finite-Volume CFD Code with Adaptive Time Stepping
FLUSEPA (Registered trademark in France No. 134009261) is an advanced
simulation tool which performs a large panel of aerodynamic studies. It is the
unstructured finite-volume solver developed by Airbus Safran Launchers company
to calculate compressible, multidimensional, unsteady, viscous and reactive
flows around bodies in relative motion. The time integration in FLUSEPA is done
using an explicit temporal adaptive method. The current production version of
the code is based on MPI and OpenMP. This implementation leads to important
synchronizations that must be reduced. To tackle this problem, we present the
study of a task-based parallelization of the aerodynamic solver of FLUSEPA
using the runtime system StarPU and combining up to three levels of
parallelism. We validate our solution by the simulation (using a finite-volume
mesh with 80 million cells) of a take-off blast wave propagation for Ariane 5
launcher.Comment: Accepted manuscript of a paper in Journal of Computational Scienc
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