https://puma.ub.uni-stuttgart.de/tag/ParallelisationPUMA RSS feed for /tag/Parallelisation2026-04-23T06:25:11+02:00https://puma.ub.uni-stuttgart.de/bibtex/20ace774f7a6793874363bb34e9e65a7b/bastiankollerbastiankoller2017-03-14T06:31:25+01:00Data-as-a-Service MPI OMPIJava Parallelisation Performance <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexey Cheptsov" itemprop="url" href="/person/1921f863d6d1f0846708c323a613df400/author/0"><span itemprop="name">A. Cheptsov</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bastian Koller" itemprop="url" href="/person/1921f863d6d1f0846708c323a613df400/author/1"><span itemprop="name">B. Koller</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"></span>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)</span>Tue Mar 14 06:31:25 CET 2017Leveraging High-Performance Computing Infrastructures to Web Data Analytic Applications by Means of Message-Passing Interface2015Data-as-a-Service MPI OMPIJava Parallelisation Performance https://puma.ub.uni-stuttgart.de/bibtex/2a4a5c9728db0683dda1b2b3318fa0c9d/amerwafaiamerwafai2016-01-29T09:34:55+01:00Back CUDA GPGPU GPU HLRS Network Neural Parallelisation Propagation SCOPE myown <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mhd. Amer Wafai" itemprop="url" href="/person/10f4f1b3c77004231f7c17f1c80480dfe/author/0"><span itemprop="name">M. Wafai</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Zaheer Ahmed" itemprop="url" href="/person/10f4f1b3c77004231f7c17f1c80480dfe/author/1"><span itemprop="name">Z. Ahmed</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rainer Keller" itemprop="url" href="/person/10f4f1b3c77004231f7c17f1c80480dfe/author/2"><span itemprop="name">R. Keller</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sven Holzmann" itemprop="url" href="/person/10f4f1b3c77004231f7c17f1c80480dfe/author/3"><span itemprop="name">S. Holzmann</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Björn Sander" itemprop="url" href="/person/10f4f1b3c77004231f7c17f1c80480dfe/author/4"><span itemprop="name">B. Sander</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael Resch" itemprop="url" href="/person/10f4f1b3c77004231f7c17f1c80480dfe/author/5"><span itemprop="name">M. Resch</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">New Horizons in Web Based Learning</span>, </em></span><em>volume 7697 of LNCS, </em><em>page <span itemprop="pagination">21-29</span>. </em><em><span itemprop="publisher">Springer Berlin Heidelberg</span>, </em>(<em><span>December 2011<meta content="December 2011" itemprop="datePublished"/></span></em>)</span>Fri Jan 29 09:34:55 CET 2016New Horizons in Web Based LearningDecember21-29LNCSOptimization of industrial Neural Network simulators for GPGPUs76972011Back CUDA GPGPU GPU HLRS Network Neural Parallelisation Propagation SCOPE myown This paper introduces the porting of an industrial neural network simulator onto GPUs used in a tool-chain to sort massive amounts of E-mails and other textual data. Compared to other previous work, all steps are being executed on the GPU, achieving overall up to 33× speedup without using any cuBLAS functionality. All the time-consuming routines have been ported onto the GPU, i.e. the training-, the simulation- and the verification-phases, the training being the most time-consuming. It is planned to include these GPU-kernels into the product for special costumer's demands.