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High Performance Computing at UCF Brian Goldiez, Ph.D. September, 2008.

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Presentation on theme: "High Performance Computing at UCF Brian Goldiez, Ph.D. September, 2008."— Presentation transcript:

1 High Performance Computing at UCF Brian Goldiez, Ph.D. September, 2008

2 Background Directed Federal Program –Funded for 2 years Maximize Campus Participation Competitive Procurement (7 Bids) –IBM Selected –Machine Named STOKES After Sir George Gabriel Stokes (Mathematician & Physicist) Participate in the HPC Community –SURAgrid –Supercomputer Conference

3 UCF Objectives Support Scientific Exploration & Interaction –Science Based M&S –Human Centered M&S –Synergies Between the Above Build a Diverse Community of Users Increase System Capabilities Increase Research Scope & Funding Attract External Faculty & Users Become Self Sufficient in 2010

4 Current Management Approach Research Computing is a Specialize Field Research Computing Needs to be Professionally Managed (e.g. GSU, Purdue) We Have Some Unique Opportunities –Interaction in HPC –Real Time Storm Effects on Coastal Areas –Crowd Modeling –Games (Serious & Entertainment) UCF Can Become a Major Player and Be Viable for Funding Recommendations: –UCF Centrally Facilitate/Manage Research Computing for Improved Efficiency & Use of Resources –UCF Designate a Person to Become Active in SURA HPC Group & Work With Campus Entities on Rsch Computing –Use Existing Grant Resources to Fund the Initial Effort –Plan for University & External Support and Growth over the Next 3 Years

5 Stokes Current Capabilities Current System (90% Utilized) –Processor, Xeon 3 GHz, 64b –~2.2 Tflops –240 Cores 4 Visualization Nodes –528 GB Memory –22+ TB Storage –O/S RHEL 5.0 –Interconnect IB 20Gbps GigE –NFS ~220MB/s Expanded System –Processor, Xeon 3 GHz, 64b –~6.4 Tflops –648 Cores 4 Visualization Nodes –1.424 TB Memory –42+ TB Storage –O/S RHEL 5.1 –Interconnect IB 20Gbps GigE –GPFS w/RDMA ~500 MB/s

6 Usage Groupings Science Based M&S Usage –Nano Technology –Civil Engineering –Physics Batch Processing Existing Programs (e.g., MatLab) New Data Large Runs Segue to Larger Systems Human Centered M&S Usage –IST –Army –Partnering Industry Interactive –Human in the Loop –Modeling Human Activity Multi-modal I/O Multi-user No Existing HPC Programs or Data

7 Interactive Simulation Needs –Real time capability using fast processors and high-speed interconnects –High fidelity –Low latency/High bandwidth interconnects –Real time I/O –Connection to real world assets –Fixed frame rates (some apps) Strategies –Message Passing Interface (MPI) or Scalable Link Interface (SLI) –Ltd shared memory processing (SMP) or distributed processing Interfaces with sensory processors (e.g., interactive visualization, haptics, …) Scalability in terms of HPC architecture and simulation entities

8 Other Considerations Lets remember the human factor –How will a user interact with an HPC? –How will multiple users interact with an HPC & maintain coherence of I/O? –How will interim results be gathered? –How can timely and relevant HF experiments be developed to influence the design? Get developers involved…

9 Current Users IST Physics Mathematics Chemistry Nanoscience Civil Engr Mech. Engr Industrial Engr Electrical & Computer Engr CREOL SAIC Forterra

10 Current Human Centered M&S Research Apparent Parallelizable Systems (SAF/Games) –Approaches to Parallelization –Spatial & Temporal Coherency –Performance Assessment & Optimization Interactive & Visualization –Review Lit in Sci Vis & Comp Steering –Leverage Existing Software (e.g., OLIVE, DCV) –Consider & Baseline Different Approaches LVC Modeling

11 Possible Areas of Future Research Multi-core Programming for M&S Applications –Tight Timing Constraints –Low Latency –I/O Bound Use of Cell Processor for M&S Multi-World Systems LVC Implementations/Experimentation Terrain Correlation Granular Propagation Mitigation Methods Multi-scale Simulations Benchmarks De-coupling SAF Models ????

12 Getting Involved (Notional for Discussion) Relevance to UCF Interests –UCF M&S (Fully Supported) –Other UCF (Partially Supported) –Other Entities (Profit and Non-Profit) With UCF M&S (Fully Supported) With Other UCF (Partially Supported) Other Users (Lower Queue Priority) –University/Non-Profits (Case by Case) –For Profit Proprietary Provide Funds for Staff Constrained Use of Software Joint Proposals

13 Issues Facilities –Power & Cooling Infrastructure Obsolescence Parallel Programming Long Term Support –State Funding? –Other Sources?

14 Back Up Charts

15 High Performance Computing for Simulation Training Systems Purpose 1.Enhance the Universitys facilities in the area of HPCC systems 2.Support faculty research for parallel simulation of complex scientific data in the areas of Physics, Chemistry, Civil and Nano-technology 3.Study large scale interactive simulations that require real- time processing of hundreds of entities on complex terrain databases 4.Support RDECOM research on gaming and training system development such as OneSAF Benefits to the Army 1.Establish a capability to address M&S relevant issues in Multi-scale simulation, interactivity and visualization. 2.Offer a unique opportunity to synthesize the research efforts of the various departments at the University by facilitating a shared high performance computing infrastructure Federal and Private Endorsements 1.Project funded and supported by RDECOM and PEO-STRI 2.Association with national super-computing grids such as Southeastern Universities Research Association (SURA) 3.Collaboration with private companies like Forterra systems Deliverables 1.HPCC computing platform with quad-core processors, 4GB memory, 10 TB storage, high-speed interconnect and graphics capabilities. 2.Scientific studies on using HPC in interactive M&S

16 The Top 10 Machines November 2007 Rmax is in TeraFLOPS = One Trillion (10 12 ) Floating Point Operations per second

17 Projected Top 500 computing power

18 Some perspective: Computing Power and Capabilities The Hans Moravec vision

19 Areas for Investigation Extents of single image environments –Terrain/Environment –Interacting entities Live, virtual, constructive experimentation –Scalable simulations –Multi-scale simulations –Control of propagating granularity HPC architectures for interaction –Map HPC types to applications Techniques for porting interactive applications to HPC platforms Tools for interaction

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