Regan, John M.; Yan, Hengjing(Advances in Photosynthesis and Respiration, Springer Nature, 2014)[Book Chapter]
Bioelectrochemical systems involve the use of exoelectrogenic (i.e., anode-reducing) microbes to produce current in conjunction with the oxidation of reduced compounds. This current can be used directly for power in a microbial fuel cell, but there are alternate uses of this current. One such alternative is the production of hydrogen in a microbial electrolysis cell (MEC), which accomplishes cathodic proton reduction with a slight applied potential by exploiting the low redox potential produced by exoelectrogens at the anode. As an indirect approach to biohydrogen production, these systems are not subject to the hydrogen yield constraints of fermentative processes and have been proven to work with virtually any biodegradable organic substrate. With continued advancements in reactor design to reduce the system internal resistance, increase the specific surface area for anode biofilm development, and decrease the material costs, MECs may emerge as a viable alternative technology for biohydrogen production. Moreover, these systems can also incorporate other value-added functionalities for applications in waste treatment, desalination, and bioremediation.
There has been a significant research in collective communication operations, in particular in MPI broadcast, on distributed memory platforms. Most of the research works are done to optimize the collective operations for particular architectures by taking into account either their topology or platform parameters. In this work we propose a very simple and at the same time general approach to optimize legacy MPI broadcast algorithms, which are widely used in MPICH and OpenMPI. Theoretical analysis and experimental results on IBM BlueGene/P and a cluster of Grid’5000 platform are presented.
Buse, Gerrit; Pflüger, Dirk; Jacob, Riko(Lecture Notes in Computational Science and Engineering, Springer Nature, 2014)[Book Chapter]
In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated grids, both with and without boundary grid points. Similar to the implicit data structures proposed in Feuersänger (Dünngitterverfahren für hochdimensionale elliptische partielle Differntialgleichungen. Diploma Thesis, Institut für Numerische Simulation, Universität Bonn, 2005) and Murarasu et al. (Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming. Cambridge University Press, New York, 2011, pp. 25–34) we also define a bijective mapping from the multi-dimensional space of grid points to a contiguous index, such that the grid data can be stored in a simple array without overhead. Our approach is especially well-suited to exploit all levels of current commodity hardware, including cache-levels and vector extensions. Furthermore, this kind of data structure is extremely attractive for today’s real-time applications, as it gives direct access to the hierarchical structure of the grids, while outperforming other common sparse grid structures (hash maps, etc.) which do not match with modern compute platforms that well. For dimensionality d ≤ 10 we achieve good speedups on a 12 core Intel Westmere-EP NUMA platform compared to the results presented in Murarasu et al. (Proceedings of the International Conference on Computational Science—ICCS 2012. Procedia Computer Science, 2012). As we show, this also holds for the results obtained on Nvidia Fermi GPUs, for which we observe speedups over our own CPU implementation of up to 4.5 when dealing with moderate dimensionality. In high-dimensional settings, in the order of tens to hundreds of dimensions, our sparse grid evaluation kernels on the CPU outperform any other known implementation.
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