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 state of the art survey gains themes with regards to the effect of multicore, manycore, and coprocessor applied sciences in technological know-how and large-scale purposes in an interdisciplinary surroundings. The papers integrated during this survey disguise study in mathematical modeling, layout of parallel algorithms, features 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 provided 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 essentially express the potential for rising applied sciences within the quarter of multicore and manycore processors which are paving the best way in the direction of own supercomputing and extremely most likely in the direction of exascale computing.

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

Example text

The performance results show that applications can gain a speedup by using fine-grained reduction since this implementation enables to hide the reduction between calculation while minimising the time waiting for synchronisation. 1 Introduction For applications of parallel scientific computing, reduction operations play an important role. In many cases, the performance of reduction operations, which aggregate data located on different processors to a common result datum using a specified operation, is crucial to the overall performance of the application.

Fig. 4. Buffered ping-pong communication strategy for the calculation of the DG operator 44 5 C. Altmann et al. Scale-Up Efficiency Besides the promising fundamental efficiency of the DGSEM method, its main advantages are based on its “high performance computing” capability. The DG algorithm is inherently parallel, since all elements do communicate only with their direct neighbors via solution and flux exchange.

For example, gradients in η-direction are already sent while the numerical flux in ξ-direction is yet to be calculated. This is important since all send-receive operations have to be buffered. 42 C. Altmann et al.                
            !  Fig. 3. Buffered ping-pong communication strategy for the calculation of BR1 lifting in ξ-direction          
   

       
       
  
    
 
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