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| ||Previous years: ||[[ScientificSeminar/2017|2017]] ||[[ScientificSeminar/2016|2016]] ||[[ScientificSeminar/2015|2015]] ||[[ScientificSeminar/2014|2014]] ||[[ScientificSeminar/2013|2013]] ||[[ScientificSeminar/2012|2012]] ||[[ScientificSeminar/2011|2011]] ||[[ScientificSeminar/2010|2010]] ||[[ScientificSeminar/2009|2009]] ||[[ScientificSeminar/2008|2008]] ||[[ScientificSeminar/2007|2007]] ||[[ScientificSeminar/2006|2006]] ||[[ScientificSeminar/2005|2005]] || | We present a novel parallel algorithm to solve fractional-order systems involving Caputo-types derivative. The numerical method used for finding the solution is Adams-Bashforth-Moulton (ABM) predictor-corrector scheme. Using MPI and OPENMP frameworks the algorithm was implemented to run on a BlueGene/P supercomputer. The same algorithm that ran on the BlueGene/P cluster was adapted to run on GPU and exploit the GPU's capabilities. A comparison between these approaches is presented by running numerical simulations of the same three-dimensional fractional-order systems. |
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---- '''2019''' ---- [[AbstractBrankaVucetic|''Research and PhD Scholarships at the Sydney University Centre for IoT and Telecommunications'']] Branka Vucetic, Timisoara Deep Learning Meetup . January 16, 2019, 12:00, room 045C ---- ---- '''2018''' ---- [[AbstractDeepLearning|''Deep Learning made in TM'']] [[attachment:DeepLearningMeetup_21nov2018.pdf]] Virgil Petcu and Robert Maria, Timisoara Deep Learning Meetup . November 21, 2018, 18:00, room 045C ---- ''Algebraic approach for post-quantum cryptography'' Vlad Dragoi, PhD, postdoctoral researcher Aurel Vlaicu University Arad . October 24, 2018, 18:00, room 045C ---- ''Terrorism and Cyberspace - Ancient ideology modern body'' Tal Pavel, PhD, CEO & Founder, Middleeasternet, Head of Cyber studies, The Academic College of Tel Aviv-Yaffo . October 10, 2018, 18:00, room 045C ---- ''Inferring the road network from GPS trajectories'' (slides and datasets: http://cs.uef.fi/~radum/RO/) Radu Mariescu Istodor, PhD, University of Eastern Finland . October 10, 2018, 19:00, room 045C ---- [[AbstractDoruRotovei|Analytical CRM – Enhancing the customer experience during the initial complex sale using machine learning techniques]] Doru Rotovei, West University of Timisoara . May 2, 2018, 18:00, room 045C ---- [[AbstractStefanBalint|Objectivity Lost when Riemann-Liouville or Caputo Fractional Order Derivatives Are Used]] Stefan Balint, West University of Timisoara . April 25, 2018, 18:00, room 045C ---- [[AbstractTeodoraSeleaML|Machine Learning for Processing Satellite Images]] Teodora Selea, West University of Timisoara . April 18, 2018, 18:00, room 045C ---- [[AbstractCiprianPungila|Understanding the Science Behind GPGPU Computational Models: Overcoming Challenges and Debunking Myths.]] Ciprian Pungila, West University of Timisoara . April 4, 2018, 18:00, room 045C ---- [[AbstractCiprianJichici|Quantum computing – The potential impact on Machine Learning]] Ciprian Jichici, CEO Genisoft & Microsoft Regional Director in Timișoara . March 14, 2018, 18:00, room 045C ---- [[AbstractAdrianaDinis|Towards a Complex Evolutionary Agent-Based System for Medical Sensor Data]] Adriana Dinis, West University of Timisoara . March 7, 2018, 18:00, room 045C |
The algorithm implemented in CUDA that approximates the solution using the ABM method was also adapted to use Diethelm's method. Having the same algorithm that runs on the same system, enables an accurate and practical comparison between the numerical methods. |
We present a novel parallel algorithm to solve fractional-order systems involving Caputo-types derivative. The numerical method used for finding the solution is Adams-Bashforth-Moulton (ABM) predictor-corrector scheme. Using MPI and OPENMP frameworks the algorithm was implemented to run on a BlueGene/P supercomputer. The same algorithm that ran on the BlueGene/P cluster was adapted to run on GPU and exploit the GPU's capabilities. A comparison between these approaches is presented by running numerical simulations of the same three-dimensional fractional-order systems.
The algorithm implemented in CUDA that approximates the solution using the ABM method was also adapted to use Diethelm's method. Having the same algorithm that runs on the same system, enables an accurate and practical comparison between the numerical methods.