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A Grid Parallel Application Framework

Jeremy Villalobos PhD student Department of Computer Science University of North Carolina Charlotte. A Grid Parallel Application Framework. Overview. Parallel Applications on the Grid Latency Hiding by Redundant Processing (LHRP) ‏ PGAFramework Related work Conclusion.

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A Grid Parallel Application Framework

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  1. Jeremy Villalobos PhD student Department of Computer Science University of North Carolina Charlotte A Grid Parallel Application Framework

  2. Overview • Parallel Applications on the Grid • Latency Hiding by Redundant Processing (LHRP)‏ • PGAFramework • Related work • Conclusion

  3. Parallel Applications on the Grid • Advantages • Access to more resources • Lower costs • Future profits from Grid Economy ? • Challenges • IO problem • Need for easy-to-use Interface • Heterogeneous hardware

  4. Latency Hiding by Redundant Processing • Latency Hiding problem • LHRP Algorithm • CPU type • CPU task assigned to each CPU type • Versioning system • Mathematical model to describe LHRP • Results

  5. LHRP • Latency Hiding • Latency Hiding by Redundantly Processing

  6. LHRP Algorithm • Internal: Only communicates with LAN CPUs. • Border: Communicates with LAN CPUs and one Buffer CPU • Buffer: Communicates with LAN Border CPU and receives data from WAN Border CPU

  7. Computation and Communication Stages • Internal: • Computes borders • Transfers borders (Non-blocking)‏ • Computes core matrix • Waits for transfer ACK

  8. Computation and Communication Stages • Border: • Computes borders • Transfers borders (Non-blocking)‏ • Sends far border • Computes core matrix • Waits for transfer ACK • Checks on far border transfer ACK (if it is the last iteration Wait)‏

  9. Computation and Communication Stages • Buffer: • Computes borders • Transfers borders (Non-blocking)‏ • Receives far border • Computes core matrix • Waits for transfer ACK • Checks on far border transfer ACK (if it is the last iteration Wait)‏

  10. Buffer Node Versioning Algorithm

  11. Buffer Node Versioning Algorithm

  12. Buffer Node Versioning Algorithm

  13. Buffer Node Versioning Algorithm

  14. Buffer Node Versioning Algorithm

  15. Buffer Node Versioning Algorithm

  16. Buffer Node Versioning Algorithm

  17. LHRP Algorithm Review • Node types: • Internal • Border • Buffer • Far Border transfer • Buffer Node Versioning system

  18. Estimated Algorithm Performance • G: Grid Latency • I: Internal Latency • B: Amount of data tuples used by the Buffer Node • W: Total amount of work for all CPUs • C: Amount of CPUs doing non-redundant work

  19. Estimated Algorithm Performance

  20. Estimated Algorithm Performance

  21. Experimental Result: Memory Footprint • 21% increase memory use over conventional form of Latency Hiding. • Causes: • Extra Matrix in Buffer Node to store old column versions • Extra far border buffers.

  22. Experimental Results: Performance

  23. Experimental Results: Performance

  24. PGAFramework • Objective • Design Requirements • Implementation technology choices • API Design • API Workpool Example • Other API features • Synchronization option • Recursive option

  25. PGAFramework Objective: To create an efficient parallel application framework for the grid that allows a user programmer easy interaction with the Grid resources.

  26. Design Requirements • Platform independence • Self Deployment • Easy-to-Use Interface • Provide the following services without requiring extra effort on the part of the user programmer: • Load Balancing • Scheduling • Fault tolerance • Latency/Bandwidth tolerance

  27. GPAFramework User's Application API (Interface)‏ User's Applications Load Balancing Scheduling Fault Tolerance Latency Bandwidth Tolerance GPAFramework Globus Job Scheduler (Condor)‏ Hardware Resources Design

  28. Desktop PCs Node Deployment Job Submit Node Scheduling Service Resource Discovery GridWay ? Globus Globus Globus Condor SGE PBS Cluster computer node Super computer

  29. Implementation • Java • Platform Independence • JXTA (JXSE)‏ • Peer-to-peer API • Provides tools to work-around NAT's and firewalls • Provides library and module runtime loading • API

  30. Motivation for API Design • Video Codecs • Codecs follow an interfaces • What happens inside the codec does not matter • The input and output for the codec needs to be specified Video Player Display a Gui Load File ... Mpeg endoded stream mpeg ogg h.264 Raw video Data Output video to screen

  31. PGAFramework API API • Schedule processes on Resource • Load user Data • There may be multiple “template” API's • Each API has Interfaces that the user implements • The user “Inserts” his module into the framework • Give data to framework • Create network • Determine topology and net behavior • Send user process to compute nodes • Get data from framework • Compute on data • Return processed data • Request sync (optional)‏ • Get Data from user class • Send to master node • Get data from framework • Store or pipe data • Repeat process in loop until done

  32. API Sample Code

  33. API API Sample Code

  34. API Sample Code

  35. Synchronization option • RemoteHandler provides an Interface to synchronize data • Data is synced non-blocking • User creates blocking procedures if needed

  36. Recursive Feature • Allows multiple level of parallelization (granularity)‏ Blur pictures Decode Video Cut Raw Video Into Pictures Blur portion of picture Pipeline Work pool Synchronous

  37. Related Work • MPI Implementation for the Grid • MPICH-G2 • GridMPI • MPICH-V2 (MPICH-V1)‏ • Peer-to-peer parallel frameworks • P2PMPI (for cluster computing)‏ • P3 (for cluster computing)‏ • Self deploying frameworks • Jojo

  38. Conclusions • Parallel Applications on the Grid • Latency Hiding by Redundant Processing (LHRP)‏ • PGAFramework • Related work

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