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SGI: Meeting Manufacturing's Need for Production Supercomputing
The document discusses how manufacturing companies are facing challenges related to increasing engineering productivity, reducing product development time, and efficiently using expensive simulation software licenses. It describes how SGI solutions like their Scale-up and Scale-out computing platforms and workload scheduling tools help address these challenges by enabling high performance computing across geographically distributed engineering facilities. As an example, SGI and ANSYS set a new record by running an ANSYS Fluent simulation on over 145,000 CPU cores, significantly reducing the simulation time.
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SGI: Meeting Manufacturing's Need for Production Supercomputing
- 1. 1 Meeting Manufacturing’s Needfor Production Supercomputing Tony DeVarco Director of Virtual Product Design Manufacturing Solutions
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- 3. 3©2016 SGI Challenges ManufacturingCompanies Are Facing Image Courtesy of ANSYS • The need to increase engineering productivity. • Speed up product development time. • Reduce physical prototyping to save time and costs. • Inefficient use of expensive ISV licenses. • Need to maintain multiple geographically distributed engineering facilities. • Distributed workstations cannot interact with computer resources and data in the corporate data centers. • Need to replace workstations with low-cost, remote clients.
- 4. 4©2016 SGI Pre/Post Model andMesh Creation and Visualization CEM Computational Electromagnetic s CFD Computational Fluid Dynamics CSM* Explicit Workload Scheduling SGI® Management Center and SGI® Performance Suite SGI® Scale-up and Scale-out Computing Solutions Workload Scheduling Tool Linux OS Nastran ANSYS® Mechanical ABAQUS/Standard ADINA Permas LS-DYNA PAM-CRASH RADIOSS ABAQUS Madymo ANSYS® FLUENT OpenFOAM® StarCCM+ PowerFLOW CFD++ Pam-Flow FEKO ANSYS® HFSS CST FMSlib ANSA Hypermesh Gambit Patran Altiar Hyperworks ANSYS EKM SimManager ABAQUS/CAE d3View SDM Simulation Data Management CSM* Implicit Nastran ANSYS® Mechanical ABAQUS/Standard ADINA Permas Altair RADIOSS LS-DYNA PAM-CRASH ABAQUS Madymo Altair AcuSolve ANSYS® FLUENT OpenFOAM® StarCCM+ PowerFLOW ANSYS® HFSS Altair FEKO CST ANSYS Maxwell 3D Altair Hypermesh ABAQUS/CAE ANSA Gambit Patran Altiar Hyperworks ANSYS EKM SimManager d3View SGI® Vizserver® SGI Hardware 3rd Party Software CAE Segments * CSM Is Computational Structural Mechanics SGI Software SGI Services SGIServices SGI® Solution Environment and CAE Application Segments
- 5. 5 SGI and ANSYS Scaling Fluent 17.2 to a New Record of 145,152 cores
- 6. 6©2016 SGI SGI ICEXA System Used SGI and ANSYS Scaling Fluent 17.2 to a New Record of 145,152 Cores • A great example using a balanced production supercomputer is illustrated in a recent setting of a new commercial CFD benchmark for the widely-used ANSYS application. • Specifically, ANSYS and SGI application engineers worked together to achieve a new world record, scaling ANSYS Fluent® on SGI® ICE™ XA, which is one of the world’s fastest commercial distributed-memory supercomputer platforms.
- 7. 7©2016 SGI 0 20000 40000 60000 80000 100000 120000 140000 160000 0 20000 40000 60000 80000 100000 120000 140000 160000 Speedup Cores Combustor 830M -- Fluent 17.2 ICE-XA -- E5-2697v4 -- 2.30 GHz Linear Scaling Gas Combustor 830M cell Model, Courtesy of ANSYS In the test, SGI ran Fluent on 145,152 CPU cores, which is over 16,000 more cores than the previous record. The benefit of scaling to this level is that the total wall clock time to complete a simulation can be significantly reduced. In this case, a single simulation was completed in 13 seconds. In contrast, the same simulation run on 1,296 cores took 20 minutes.
- 8. 8 An industry examplethat highlights the advantages of scaling CFD workloads on a SGI® UV™ System
- 9. 9©2016 SGI SGI® Origin2000, 1996 MIPS R10K SGI Origin 3000, 1999 MIPS R12K SGI® Altix® 3000, 2003 Intel Itanium SGI Altix 4700, 2006 Intel Itanium SGI UV 1000, 2010 Intel E7 SGI UV 300, 2014 Intel E7 SGI UV 2000/3000, 2012/2015 Intel E5 Seven Generations of Shared Memory Systems: 1996–2015
- 10. 10 The Best ofDMP and SMP in One Cache-Coherent System with One OS SGI® InfiniteStorage™ NVIDIA® K5200 or M6000 Multiple Users and Multiple Jobs Preprocessing, Mesh Generation and Model Decomposition Running Solvers Post Processing and Visualization UV rack layout for illustration purposes only. Consolidate Workloads into One Easy-to-Administer System
- 11. 11©2016 SGI Image courtesyLondon Computational Solutions • London Computational Solutions, headed by Mark Taylor, former Head of CFD at McLaren F1, is working with Elemental Cars, an advanced track car manufacturer. • The goal of this effort is to improve the cornering speed of its RP1 car by using design elements to create an aerodynamic downforce that increases the vertical force on the car’s tires creating more grip with the road. Elemental Cars
- 12. 12©2016 SGI • “WhenI was Principal Aerodynamicist for McLaren F1 Racing, and now as CEO of London Computational Solutions (LCS),” states Mark Taylor, “I relied on the SGI® UV™ shared memory platform to deliver the robustness, reliability, and efficiency to scale our CFD simulations to meet a tight manufacturing deadline and our aerodynamic performance targets.” • “When LCS was presented with the aerodynamic challenge to improve the cornering speeds for a new British road-legal track car called Elemental RP1, I knew I could meet their requirements of a quick turnaround because the running of our CFD software on SGI UV would perform as advertised and just work.” Image courtesy London Computational Solutions Elemental Cars
- 13. 13©2016 SGI Examples ofa fifth order accurate simulation of a full automotive car geometry. Please have insideHPC put this Top View animaRon here Please have insideHPC put this Bo*om View animaRon here Elemental Cars
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