HPC on Sun Today and Tomorrow VIRACOCHA: An Efficient Parallelization Framework Processing in Virtual Environments. Andreas Gerndt

Transcription

1 HPC on Sun Today and Tomorrow VIRACOCHA: An Efficient Parallelization Framework for Large-Scale CFD Post-Processing Processing in Virtual Environments Andreas Gerndt Aachen University (RWTH), Germany Center of Computing and Communication Virtual Reality Group Members of Virtual Reality Center Aachen (VRCA)

2 Outline CFD Post-Processing and Virtual Reality Parallelization Framework Viracocha Data Management System Streaming and Multi-Resolution Conclusion and Future Work

3 CFD Post-Processing Processing and Virtual Reality

4 CFD Post-Processing and Virtual Reality Large-Scale Datasets Data Set Year Grid Points Time Steps Size Tapered Cylinder MB McDonnell Douglas F/A MB Descending Delta Wing MB Bell-Boing V22 Tilrotor MB Bell-Boing V22 Tilrotor MB K. Reinders, Feature-Based Visualization of Time-Dependent Data, PhD Thesis, 2001

5 CFD Post-Processing and Virtual Reality Interaction Criteria Virtual Reality Real-Time Interaction Cannot handle large datasets simultaneously Minimum Frame Rate An application is not allowed to work with a frame rate lower than a threshold Bryson: 10 Hz Kreylos: 30 Hz Maximum System Response Time The response time of a Virtual Reality system after a user input was applied Bryson: 100 ms Kreylos: 100 ms

6 Parallelization Approaches

7 Parallelization Viracocha ViSTA FlowLib Viracocha VTK VTK Extraction Manager Scheduler Worker Worker TCP/IP Message Passing Visualization / VR (Client) Relieved from Extraction Requests via TCP/IP Preparation of Received Data Rendering Supercomputer (Server) Scheduler Receives Requests Workers Compute in Parallel Message Passing Algorithmic Layer Makes Use of VTK

8 Parallelization Viracocha ViSTA FlowLib Viracocha VTK VTK Extraction Manager Scheduler Worker Worker TCP/IP Message Passing System Design Constrains Platform Independence Arbitrary Main Memory Systems SMPs as well as Distributed Memory Systems CFD Demands Large Scale Datasets Unsteady Flow Fields

9 Parallelization Visualization Pipeline Input: Raw Datasets Filtering Produces More Comprehensive Data Converting Raw Datasets Appending / Cutting Data Resampling Data Extraction Connected in Series Multiple Input Mapping Creates Visualization Objects Rendering Dataset Filtering Extracted Data Mapping Visual Primitive Rendering Image Data

10 Parallelization Visualization Pipeline In General, Heavy Work at the Beginning of the Pipeline

11 Parallelization Data Parallelization Determine heavy work and duplicate these pipeline sections Split data and distribute them to several processes Combine partial results and execute remaining steps

12 Viracocha Data Management System

13 Viracocha Data Management System Data Data Data VDMS ViSTA FlowLib Extraction Manager Server Scheduler Proxy Viracocha Worker Proxy Worker TCP/IP Message Passing VDMS Server Part of the Scheduler, Non-blocking Statistical Unit / Name Service Can provide VisHost with data VDMS Status Information Already computed / cached data Upload additional command data Central Unit to coordinate VDMS VDMS Proxies Interface between Worker and VDMS If Worker needs data, it sends a request to the Proxy Proxy is a Black Box Usage of Proxy is optional Loading Strategies controlled by Server

14 Viracocha Data Management System Data Data Data VDMS ViSTA FlowLib Extraction Manager Server Scheduler Proxy Viracocha Worker Proxy Worker TCP/IP Message Passing Main Approaches Caching (Primary / Secondary) Once loaded, used several Times Prefetching Load probable data in advanced Optimized Load Strategies Data Access VDMS can handle arbitrary data Has no specific Information about actually used data types Algorithm Layer has to implement an optimized DataAccess-Object Methods of its interface are invoked by the VDMS

15 Viracocha Data Management System Caching Replacement Strategies Least Recently Used (LRU) Least Frequently Used (LFU) Frequency Based Replacement (FBR) >>> PFBR Set a Priority Value as Ref. Counter for new Elements At Start-up: High Priority Runtime: Decreasing Priority Shows Better Behavior with Activated Prefetching SIZE Remove Largest Block RAND Upper Bound for Others FBR MRU New Partition Middle Partition Old Partition LRU 3 Partitions with fixed lengths Put the referenced element at the beginning (LRU) Reference Counter If it was in New Partition: do not increase Ref. Counter Prevents fast increasing counters of frequently accessed elements Else: increase counter by one Replacement Remove Element with lowest Ref. Counter in Old Partition

16 Viracocha Data Management System Prefetching Code Prefetching Non-blocking Fetch-Command System Prefetching Automatic Prefetching controlled by the VDMS Sequential Strategies One Block Look-ahead (OBL) Use (b), Prefetch (b+1) Prefetch on Miss Prefetch (b+1) if (b) not already in Cache Markov Prefetching >>> Important for Particle Tracing Markov + OBL Already Efficient in Learning Phase Markov a 1/3 1/3 2/3 3/3 2/3 c b Probability Graph, 1. Order I am coming from (a) It is highly probable (67%) that the next block is (b) Prefetch (b) The next block is actually (b) The new probability from (a) to (b) is now 75% Higher Order possible I am coming from (c,a) a 1/4 1/3 3/4 3/3 2/3 c b

17 Viracocha Data Management System Loading Strategies Load Local Condition: File on File System or in Sec. Cache Calls LoadData() of DataAccess Object Application Developer Dependent Load Manual Condition: as for Load Local Load as Byte Sequence DataAccess-Object has to interpret Byte Sequence Assesses Loading Cost Transfer Data If available, get data from neighbor caches Else, from File System Collective Load All Workers in Group load the same Data Block Platform Dependent ROMIO

18 Streaming and Multiresolution

19 Streaming and Multiresolution Decoupling VisHost and WorkHost Improvement of Interactivity within Virtual Environments However: Extraction Still Needs Time Solution: Data Streaming Motivation: Progressive JPG >>> Immediate Feedback / Impression Exploit First Approximate Results Data Exploration can be started early Possibly abort running Computation and restart it with new Parameters Problem Additional Communication and Computation Time Appropriate CFD-Algorithms Progressive Approaches hardly available Source:

20 Streaming and Multiresolution Block-wise Streaming Fig.: Vortex Extraction on a Propfan Multi-Block Dataset