Evolution of Network Architecture in a Granular Material Under Compression

As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. Force chains are a prime example of such structures, and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, capturing and characterizing the evolving nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend the current approaches by utilizing multilayer networks as a framework for directly quantifying the progression of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and interparticle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the changes in this structure throughout the compression process. We separately consider the network of normal and tangential forces, and find that they display a different progression throughout compression. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be achieved by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than a purely local measure of interparticle forces alone. The results discussed throughout this study suggest that these network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup

Enabling GPU Support for the COMPSs-Mobile Framework

Using the GPUs embedded in mobile devices allows for increasing the performance of the applications running on them while reducing the energy consumption of their execution. This article presents a task-based solution for adaptative, collaborative heterogeneous computing on mobile cloud environments. To implement our proposal, we extend the COMPSs-Mobile framework – an implementation of the COMPSs programming model for building mobile applications that offload part of the computation to the Cloud – to support offloading computation to GPUs through OpenCL. To evaluate our solution, we subject the prototype to three benchmark applications representing different application patterns.This work is partially supported by the Joint-Laboratory on Extreme Scale Computing (JLESC), by the European Union through the Horizon 2020 research and innovation programme under contract 687584 (TANGO Project), by the Spanish Goverment (TIN2015-65316-P, BES-2013-067167, EEBB-2016-11272, SEV-2011-00067) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft