https://puma.ub.uni-stuttgart.de/tag/SimTech%20algorithm-based%20abftPUMA RSS feed for /tag/SimTech%20algorithm-based%20abft2026-04-05T08:57:14+02:00https://puma.ub.uni-stuttgart.de/bibtex/278feed56c1636b8fcbfd657450c145bd/clausbraunclausbraun2018-03-19T16:42:05+01:00ABFT GPGPU GPU SimTech algebra algorithm-based error error-detection fault fault-tolerance linear matrix-operations myown simulation <meta content="thesis" itemprop="educationalUse"/><span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Claus Braun" itemprop="url" href="/person/1f4aa6bff08e99d1685a2218270cadc80/author/0"><span itemprop="name">C. Braun</span></a></span></span>. </span><span class="additional-entrytype-information"><em>University of Stuttgart, </em>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)</span>Mon Mar 19 16:42:05 CET 2018Algorithm-based fault tolerance for matrix operations on graphics processing units: analysis and extension to autonomous operation.2015ABFT GPGPU GPU SimTech algebra algorithm-based error error-detection fault fault-tolerance linear matrix-operations myown simulation https://puma.ub.uni-stuttgart.de/bibtex/2a6dcd392b900956871dd3cfde89cd481/clausbraunclausbraun2018-03-19T16:15:07+01:00ABFT GPGPU GPU SimTech adaptivity algebra algorithm-based autonompous error error-correction error-detection fault-tolerance linear matrix matrix-multiplication metric myown rounding rounding-error <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Claus Braun" itemprop="url" href="/person/1f672f35af5f6b825bda005ca703be294/author/0"><span itemprop="name">C. Braun</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sebastian Halder" itemprop="url" href="/person/1f672f35af5f6b825bda005ca703be294/author/1"><span itemprop="name">S. Halder</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hans-Joachim Wunderlich" itemprop="url" href="/person/1f672f35af5f6b825bda005ca703be294/author/2"><span itemprop="name">H. Wunderlich</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN&#039;14)</span>, </em></span><em>page <span itemprop="pagination">443--454</span>. </em>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)</span>Mon Mar 19 16:15:07 CET 2018Proceedings of the 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'14)443--454{A-ABFT: Autonomous Algorithm-Based Fault Tolerance for Matrix Multiplications on Graphics Processing Units}2014ABFT GPGPU GPU SimTech adaptivity algebra algorithm-based autonompous error error-correction error-detection fault-tolerance linear matrix matrix-multiplication metric myown rounding rounding-error Graphics processing units (GPUs) enable large-scale scientific applications and simulations on the desktop. To allow scientific computing on GPUs with high performance and reliability requirements, the application of software-based fault tolerance is attractive. Algorithm-Based Fault Tolerance (ABFT) protects important scientific operations like matrix multiplications. However, the application to floating-point operations necessitates the runtime classification of errors into inevitable rounding errors, allowed compute errors in the magnitude of such rounding errors, and into critical errors that are larger than those and not tolerable. Hence, an ABFT scheme needs suitable rounding error bounds to detect errors reliably. The determination of such error bounds is a highly challenging task, especially since it has to be integrated tightly into the algorithm and executed autonomously with low performance overhead. In this work, A-ABFT for matrix multiplications on GPUs is introduced, which is a new, parallel ABFT scheme that determines rounding error bounds autonomously at runtime with low performance overhead and high error coverage.https://puma.ub.uni-stuttgart.de/bibtex/292cad6c6d7a90044e7289f504f6f4cf7/clausbraunclausbraun2018-03-19T16:15:07+01:00ABFT GPGPU SimTech algorithm-based computing errors fault fault-tolerance myown scientific simulation <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hans-Joachim Wunderlich" itemprop="url" href="/person/1852ec5b9e00df1c4437700418d91759c/author/0"><span itemprop="name">H. Wunderlich</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Claus Braun" itemprop="url" href="/person/1852ec5b9e00df1c4437700418d91759c/author/1"><span itemprop="name">C. Braun</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sebastian Halder" itemprop="url" href="/person/1852ec5b9e00df1c4437700418d91759c/author/2"><span itemprop="name">S. Halder</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the IEEE International On-Line Testing Symposium (IOLTS&#039;13)</span>, </em></span><em>page <span itemprop="pagination">240--243</span>. </em>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)</span>Mon Mar 19 16:15:07 CET 2018Proceedings of the IEEE International On-Line Testing Symposium (IOLTS'13)240--243{Efficacy and Efficiency of Algorithm-Based Fault Tolerance on GPUs}2013ABFT GPGPU SimTech algorithm-based computing errors fault fault-tolerance myown scientific simulation Computer simulations drive innovations in science and industry, and they are gaining more and more importance. However, their high computational demand generates extraordinary challenges for computing systems. Typical highperformance computing systems, which provide sufficient performance and high reliability, are extremly expensive. Modern GPUs offer high performance at very low costs, and they enable simulation applications on the desktop. However, they are increasingly prone to transient effects and other reliability threats. To fulfill the strict reliability requirements in scientific computing and simulation technology, appropriate fault tolerance measures have to be integrated into simulation applications for GPUs. Algorithm-Based Fault Tolerance on GPUs has the potential to meet these requirements. In this work we investigate the efficiency and the efficacy of ABFT for matrix operations on GPUs. We compare ABFT against fault tolerance schemes that are based on redundant computations and we evaluate its error detection capabilities