Facing the Multicore-Challenge III: Aspects of New Paradigms by Michael Bader, Hans-Joachim Bungartz, Martin Schreiber

By Michael Bader, Hans-Joachim Bungartz, Martin Schreiber (auth.), Rainer Keller, David Kramer, Jan-Philipp Weiss (eds.)

This cutting-edge survey positive factors issues relating to the effect of multicore, manycore, and coprocessor applied sciences in technology and large-scale purposes in an interdisciplinary surroundings. The papers integrated during this survey hide examine in mathematical modeling, layout of parallel algorithms, facets of microprocessor structure, parallel programming languages, hardware-aware computing, heterogeneous systems, manycore applied sciences, functionality tuning, and necessities for large-scale purposes. The contributions offered during this quantity are an end result of an inspiring convention conceived and arranged by means of the editors on the collage of technologies (HfT) in Stuttgart, Germany, in September 2012. the ten revised complete papers chosen from 21 submissions are offered including the twelve poster abstracts and concentrate on mix of recent features of microprocessor applied sciences, parallel functions, numerical simulation, and software program improvement; hence they basically exhibit the potential for rising applied sciences within the zone of multicore and manycore processors which are paving the way in which in the direction of own supercomputing and intensely most likely in the direction of exascale computing.

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Additional info for Facing the Multicore-Challenge III: Aspects of New Paradigms and Technologies in Parallel Computing

Example text

One iteration of approximating the solution of (3), is shown in Alg. 1. The algorithm receives the element stiffness matrices Ael and the projectors Lel for all elements el as well as the approximation u for the solution of the linear system of equations (3) as input and returns the vector y as output. e. only accessible by the processor owning them. After setting y to zero (Line 1), the calculation of Formulas (6) to (8) is executed for each element (Lines 4 to 7) in a parallel section. The reduction operation at the end of the parallel section (Lines 8 to 10) adds the local solution vectors y ◦ to the overall solution vector according to (9).

The results of [8] are contrary to the results of this article, where the application benefits from atomic add instructions to shared arrays. One can assume that this is due to the memory access pattern of the particle simulation which has more frequent write operations to the same memory location than the reduction in the FEM. , presented in [5] and [7]. 36 7 M. Balg et al. Conclusion This article investigated several implementation variants of a reduction of arrays on shared-memory machines. A fine-grained reduction has been compared to existing implementations of coarse-grained reductions in OpenMP and MPI.

Thus, the condition mentioned above is fulfilled, and using fine-grained reduction improves the runtime of the adaptive FEM. The results of Sect. 3 show that, in contrast to other works which commonly suggest array privatisation, also writing directly to shared arrays can be efficient if fine-grained reduction operations are used. Acknowledgement. This work is supported by the cluster of excellence EnergyEfficient Product and Process Innovation in Production Engineering (eniPROD) funded by the European Union (European Regional Development Fund) and the Free State of Saxony.

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