High Performance Computing on Windows. User Experiences: Dynamic optimization User Experiences: Matlab. Christian Terboven

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1 High Permance omputing on Windows User Experiences: Dynamic optimization User Experiences: Matlab hristian Terboven enter omputing and ommunication RWTH Aachen University 1 HP on Windows enter omputing and ommunication

2 Agenda Uwe Rütjes, WZL: Simulation of Bevel Gear utting Process T: Arndt Hartwich, LPT: DyOS Dynamic Optimization Software T: Jun.-Prof. Oliver Holtemöller, VWL: Optimization with Matlab 2 HP on Windows enter omputing and ommunication

3 Agenda Uwe Rütjes, WZL: Simulation of Bevel Gear utting Process T: Arndt Hartwich, LPT: DyOS Dynamic Optimization Software T: Jun.-Prof. Oliver Holtemöller, VWL: Optimization with Matlab 3 HP on Windows enter omputing and ommunication

4 Agenda Uwe Rütjes, WZL: Simulation of Bevel Gear utting Process T: Arndt Hartwich, LPT: DyOS Dynamic Optimization Software T: Jun.-Prof. Oliver Holtemöller, VWL: Optimization with Matlab 4 HP on Windows enter omputing and ommunication

5 DyOS: Dynamic Optimization Software What is dynamic optimization? Start Drive 250 m in minimal time! Start with v = 0, stop exactly at x = 250m! Maximal speed is v = 10 m/s! Maximal acceleration is a = 1 m/s²! Finish v( t) v max x( 0), v(0) = 0 MAX Acceleration a Time [sec] 5 HP on Windows enter MIN v( t f ) = 0, x( t f ) xt. f Speed v Distance x Time [sec] omputing and ommunication

6 DyOS: Dynamic Optimization Software Dynamic optimization in chemical industry Plastic A omposition of A und B => unfeasible material Plastic B Task: hanging the product specification (from A to B) of a plastic manufactory in ongoing business! Minimize the junk (composition of A and B)! Search economic and ecologic otimal operational mode! This task is solved by the DyOS (Dynamic Optimization Software) tool, developed at the chair process systems engineering (LPT: Lehrstuhl für Prozesstechnik) at RWTH Aachen University. ontact: Arndt Hartwich ( ): 6 HP on Windows enter omputing and ommunication

7 DyOS: Dynamic Software Optimization One simulation typically takes up to ten days and requires a significant amount of memory: Not suited desktop machines Parallelization necessary to speed up simulation process hallenges: DyOS requires commercial software packages (source available) DyOS is bound to Windows because of gproms modelling tool gproms is bound to Visual Studio 6 Solutions: Separate the gproms Interface from the rest of the software and source it out into a DLL Port the rest of the software to the new compiler: V 8, Intel HP on Windows enter omputing and ommunication

8 DyOS: Dynamic Software Optimization state integration requires 1 jacobian per step (1 Jacobian: 0.2 sec) sensitivity integration requires >>1 jacobians per step sensitivitiy equation systems i are independent of each other computational time of LUdecomposition small Dissertation: Martin Schlegel Numerical state and sensitivity integration ms the greatest part of computational eft (>99%) 30-40% of computational times required computation of jacobians (~ jacobians per sensitivity integration), the remaining part mostly depends of number of sensitivities Idea: Separation into different tasks (subprograms): 1. Integration of states 2. omputation of Jacobian matrix 3. Integration of only a part of the sensitivity equation system on different PUs 8 HP on Windows enter omputing and ommunication

9 DyOS: Dynamic Software Optimization hallenge parallelization: gproms part can not be parallelized Shared-Memory, as it is not thread-safe gproms part can not be executed on one machine more than once at a time Physical realisation: DyOS x A x/a gproms-interface 9 HP on Windows enter Tasks of MPI communicator (ROOT): storing data sending and receiving data control over entire software Future work: parallelization with OpenMP of the remaining parts omputing and ommunication

10 Agenda Uwe Rütjes, WZL: Simulation of Bevel Gear utting Process T: Arndt Hartwich, LPT: DyOS Dynamic Optimization Software T: Jun.-Prof. Oliver Holtemöller, VWL: Simulation with Matlab 10 HP on Windows enter omputing and ommunication

11 Simulation with Matlab Oliver Holtemöller, Juniorprofessur für Allgemeine VWL RWTH Aachen University Forschungsgebiete Geldtheorie und Geldpolitik Quantitative Makroökonomik Angewandte Ökonometrie und Zeitreihenanalyse Aussage: Die einem Desktop deutlich überlegene Rechenleistung ist ein klarer Wettbewerbsvorteil für die Forschung: Simulation kann von ca. 200 Unbekannten (~ 12h Rechenzeit) auf mehrere 1000 Unbekannte ausgeweitet werden. 11 HP on Windows enter omputing and ommunication

12 End of this part Questions? 12 HP on Windows enter omputing and ommunication

Experiences with HPC on Windows

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