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Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
cuIBM -- A GPU-accelerated Immersed Boundary Method
A projection-based immersed boundary method is dominated by sparse linear
algebra routines. Using the open-source Cusp library, we observe a speedup
(with respect to a single CPU core) which reflects the constraints of a
bandwidth-dominated problem on the GPU. Nevertheless, GPUs offer the capacity
to solve large problems on commodity hardware. This work includes validation
and a convergence study of the GPU-accelerated IBM, and various optimizations.Comment: Extended paper post-conference, presented at the 23rd International
Conference on Parallel Computational Fluid Dynamics (http://www.parcfd.org),
ParCFD 2011, Barcelona (unpublished
Scalable partitioning for parallel position based dynamics
We introduce a practical partitioning technique designed for parallelizing Position Based Dynamics, and exploiting
the ubiquitous multi-core processors present in current commodity GPUs. The input is a set of particles whose
dynamics is influenced by spatial constraints. In the initialization phase, we build a graph in which each node
corresponds to a constraint and two constraints are connected by an edge if they influence at least one common
particle. We introduce a novel greedy algorithm for inserting additional constraints (phantoms) in the graph
such that the resulting topology is q-colourable, where ˆ qˆ ≥ 2 is an arbitrary number. We color the graph, and
the constraints with the same color are assigned to the same partition. Then, the set of constraints belonging to
each partition is solved in parallel during the animation phase. We demonstrate this by using our partitioning
technique; the performance hit caused by the GPU kernel calls is significantly decreased, leaving unaffected the
visual quality, robustness and speed of serial position based dynamics
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