Headline in Arial Bold 30pt. Visualisation using the Grid Jeff Adie Principal Systems Engineer, SAPK July 2008

Transcription

1 Headline in Arial Bold 30pt Visualisation using the Grid Jeff Adie Principal Systems Engineer, SAPK July 2008

2 Agenda Visualisation Today User Trends Technology Trends Grid Viz Nodes Software Ecosystem Remote Visualisation Computational Steering Q & A 17-Sep-08 Slide 2

3 The Visualization Landscape Textures Pixel Fill Rate Shading Visualization Gaming/Movies Model Size Polygon Count Compute Power Sci Viz Image courtesy 20 th Century Fox 17-Sep-08 Slide 3

4 Market Forces Fragmented Market LAN / WAN Clusters Sci Viz Fragmented Market Visualization GPU GP/GPU Gaming/Movies Complex Workflows 17-Sep-08 Slide 4

5 17-Sep-08 Slide 5

6 Visualization User Trends Increasingly Complex Data Sets Scope: Larger Geographic and Physical Models Precision: More polygons per model Depth: More complex interactions Need for Speed Run multiple iterations of a problem Time to solution remains critical, even with very large models Ability to interact in real-time with very large models Display & Interactivity Requirements Very large display environments Computational Steering and Interactivity Multiple remote displays Remote group collaboration 17-Sep-08 Slide 6

7 Example: Manufacturing CAE 17-Sep-08 Slide 7

8 Example: NASTRAN job surge> ls -l total rw-r--r-- 1 jeffadie users :08 INPUT2_with_ACMS.dat -rwxr-xr-x 1 jeffadie users :20 ana64 -rw-r--r-- 1 jeffadie users :39 input2_64core.f04 -rw-r--r-- 1 jeffadie users :39 input2_64core.f06 -rw-r--r-- 1 jeffadie users :39 input2_64core.log -rw-r--r-- 1 jeffadie users :39 input2_64core.pch -rwx jeffadie users :19 mkl.cfg -rwxr-xr-x 1 jeffadie users :14 run64 surge> 17-Sep-08 Slide 8

9 Example: NASTRAN Job Input File : 41MB Output File: 83.5GB Network (Singapore USA): 1Mb/s measured Upload model: 410s ~ 7 minutes Solution (1000 steps, 64 cores): ~ 1 hour Download results: 835,000s ~ 232 hours ~ 10 days NO WAY TO REASONABLY GET RESULTS 17-Sep-08 Slide 9

10 Visualization Technology Trends Increasing COTS GPU Performance Price/Performance Advantage Use as Application Accelerators Cluster Architectures Distributed Compute & Render Expandability High Speed Data I/O Necessary to handle large data transfers Resource Optimization Identify and utilize available compute & render resources Grid computing and Virtualization Smart Workflow Management Increase user and system productivity 17-Sep-08 Slide 10

11 Implementing Visualization on Clusters Leverage Cluster Architecture and Advantages Clusters dominate mindshare and marketshare Scalability, Costs, Selection, Performance, Open Source Reduce Cluster Complexity Integration, Q/A, Time-to-Productivity Leverage Graphics (Gaming) Technology Provide Textures, Pixel Fill Rate, Shading Graphics Cards don t directly address Visual Supercomputing Demands The Challenge of Visualization on Clusters Scaling Visualization Applications Maintaining Data I/O Throughput Software Stack Complexity Resource Management Maintaining Interactivity in a Batch Environment 17-Sep-08 Slide 11

12 SGI Virtu VN200 Visualization Node 8 FB-DIMMs 8 to 32 GB RAM DDR 4x IB interconnect Two (2.5 ) SATA Drives NVIDIA Quadro FX High Performance GPU Two (2) Intel Quad core Xeon CPUs Redundant (2) Power Supplies 17-Sep-08 Slide 12

13 SYSTEM MANAGEMENT STORAGE MANAGEMENT FILE SYSTEM COMPILERS LIBRARIES PROFILERS, DEBUGGERS Software Ecosystem JOB SCHEDULER LINUX OPERATING SYSTEM 17-Sep-08 Slide 13

14 REMOTE VISUALIZATION SYSTEM MANAGEMENT STORAGE MANAGEMENT FILE SYSTEM COMPILERS LIBRARIES PROFILERS, DEBUGGERS GRAPHICS TOOLKITS Software Ecosystem WORKFLOW ORCHESTRATION BATCH SCHEDULER INTERACTIVE SCHEDULER JOB SCHEDULER LINUX OPERATING SYSTEM 17-Sep-08 Slide 14

15 The Market Demand for Grid Visualization Global workflow is reality in today s business Visualization must fit into the global workflow model Greater demands on security, data sharing, and resource management Reduce costs for applications and client systems Grid virtualization Laptop/desktop limitations Without Remote Grid Visualization, the only method to share data is to physically copy the data to a remote location, with duplicate applications and processing power. 17-Sep-08 Slide 15

16 Remote Grid Visualization: Key Concepts Traditional Approach: Data Paradigm Download DATA to individual graphics workstations Multiple copies of the model/data across users Large data transmissions across networks, iteratively Store Compute Manage Render / Interact 2D, 3D, 4D non-visual model Compute Nodes Admin Service Node 2D, 3D, 4D visual model Desktop workstation Batch Interactive 17-Sep-08 Slide 16

17 Remote Grid Visualization The Silicon Graphics Difference Visual Information Paradigm Securely share visual information to all users no data transfer Real-time iterative analysis and updates Single data image secure, simpler management, consistent Store Compute Manage Interact 2D, 3D, 4D models Compute & Visualization Nodes Visualization & Admin Node Visualization Display System or Thin Client Batch Interactive 17-Sep-08 Slide 17

18 Visualisation across the Grid The Power of a Reality Center The Power of Fusion The Power of Many Applications The Power of Many Devices 17-Sep-08 Slide 18

19 Benefits of Grid Visualisation Remove the physical link between users, their data, and the visualization system Solve much more difficult problems than possible with desktop workstations but do it from your desktop Ability to reserve HPC resources to enable interactive applications Connect experts with users and their data, independent of their locations Enable computational steering from anywhere in the world 17-Sep-08 Slide 19

20 SGI High Performance Visualization Computational Steering Traditional Methods Interactive Discovery Prepare Prepare/Analysis Submit Jobs Analysis Simulation Interactively change the simulation Faster Time to Insight 17-Sep-08 Slide 20

21 Summary Remote Grid Visualisation benefits: Data size One copy of the data is maintained in the database. User has fast access to the data. Sharing data Parallel file systems allows multiply users to analysis the database. Model size Distributed rendering allows larger models to be visualized. Frame buffer limit of the GPU is no longer a limiting factor. Faster computation Render nodes are integrated in the Grid and can be used to for MPI jobs when not running visualization. License consolidation For end user application licenses can be shared on the Grid as opposed to each user having their own license. 17-Sep-08 Slide 21

22 Thank You! 17-Sep-08 Slide 22