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Compressed Symmetric Graphs for the simulation of Super Carbon Nanotubes 14

Compressed Symmetric Graphs for the Simulation of Super Carb...

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International Workshop on Parallel Optimization using/for Multi and Many-Core High Performance Computing (POMCO) in conjunction with 14th International Conference on High Performance Computing and simulation (hpcS)

作者： Burger, Michael Bischof, Christian Wackerfuss, Jens Tech Univ Darmstadt Grad Sch Computat Engn Dolivostr 15 D-64293 Darmstadt Germany Tech Univ Darmstadt Sci Comp Mornewegstr 30 D-64293 Darmstadt Germany Univ Kassel Struct Mech Monchebergstr 7 D-34109 Kassel Germany

ISBN: (纸本)9781509020881

In this paper, we present an extremely space-saving, yet parallelizable, data structure called Compressed Symmetric Graphs (CSGs) for the simulation of Super Carbon Nanotubes (SCNTs) modeled by a graph algebra. CSGs can drastically reduce the amount of data to represent a SCNT by exploiting inherent symmetry and hierarchy to dynamically reconstruct symmetric parts from base elements as needed. This new graph structure is integrated in an existing matrix-free solving approach for simulating the mechanical behavior of SCNTs. We extend previous investigations on structural symmetry in SCNTs to now simultaneously exploit translational and rotational symmetry, thus multiplying their effects. As a result, we can reach compression ratios of over 100 for some SCNT configurations. The memory demand is further reduced by replacing the m-tuples identifying the nodes in the graph model via a structure-related compression by a serial index that can be unfolded on demand. Finally, we use still available RAM as a software-controlled cache for storing intermediate values, reducing recomputations. In this fashion, our code can represent very large configurations, but makes optimal use of the hardware at hand. We investigate for order 0 and 1 SCNTs the impact of the data access-time in CSGs on the total runtime and a suitable OpenMP parallelization strategy for minimizing this influence. We demonstrate that as a result, the new CSGs approach significantly reduces the runtime, by a factor between 1.3 and 12. While the graph algebra underlying this work was designed for the representation of SCNTs, we believe that the algorithmic principles with respect to the exploitation of structure and efficient software design are relevant to other graph settings, where hierarchy and replication play an important role in the graph design. In these cases, CSGs can help to overcome the per node/core memory-capacity limitation of current hpc systems.

关键词： modeling and simulation using hpc systems Load Balancing and Sharing

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Enabling a Quantum Monte Carlo Application for the DEEP architecture 13

Enabling a Quantum Monte Carlo Application for the DEEP arch...

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International Conference on High Performance Computing and simulation (hpcS)

作者： Emerson, Andrew Affinito, Fabio Cineca Supercomp Applicat & Innovat SCAI I-40033 Casalecchio Di Reno Italy

ISBN: (纸本)9781467378130

In the DEEP project a prototype Exascale system consisting of a standard Intel Xeon cluster linked to a "Booster" part containing Intel Xeon Phi nodes connected in a high-speed network, is being designed and constructed. In order to evaluate this novel architecture, expected to be available in the second half of 2015, a number of grand challenge applications in computational science and engineering are being modified and optimised. In this study we report on the efforts made by the Cineca project partner and DEEP support staff to enable one of these applications, the TurboRVB Quantum Monte Carlo simulation program, which can be used to study complex phenomena in materials such as superconductivity. The modified code, based on an implementation of the OmpSs offload task model, has been successfully tested on the MareNostrum supercomputer at the Barcelona Supercomputing Center.

关键词： Exascale systems modeling and simulation using hpc systems Parallelization of simulation

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