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Introduction Playstation 3 (PS3) Game Console Cell Processor - PowerPoint PPT Presentation

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PS3 GRID .net Building a distributed supercomputer using the Playstation 3 M. J. Harvey G. Giuppone J. Vill-Freixa G. De Fabritiis Presented by: Abadi Kurniawan Introduction Playstation 3 (PS3) Game Console Cell Processor

  1. PS3 GRID .net Building a distributed supercomputer using the Playstation 3 M. J. Harvey G. Giuppone J. Villà-Freixa G. De Fabritiis Presented by: Abadi Kurniawan

  2. Introduction • Playstation 3 (PS3) Game Console • Cell Processor • Molecular Dynamic (MD) • CellMD • Berkeley Open Infrastructure for Network Computing (BOINC)

  3. Playstation 3 • Sony’s game consoles launched in 2006 • Distinguished by technical capabilities and innovative design • Powered by Cell Processor • Cheap High-performance Computing • Grid of Playstation 3

  4. Problems • Reliability and Trust • No control to PS3s - all devices is transient • Error correction from incomplete simulation • Defective hardware or malicious users • Loose coupling • General-purpose ethernet network - bandwidth problem

  5. Cell Processor • Developed by Sony, Toshiba and IBM • 1 POWER TM processing element (PPE) • 8 Synergetic Processing Element (SPEs) • Main memory can be accessed only by PPE • SPE must use limited in-chip local memory of 256 KB. • Element Interconnect Bus (EIB): interconnecting 8 SPEs in high speed and memory-coherent • Integrated Memory Controller (MIC): connected to external RAMBUS XDR memory

  6. Cell Processor Figure: Cell Processor Block Diagram Courtesy of http://www.ibm.com/developerworks/library/pa-cellperf/

  7. Cell Processor • Each core (PPE or SPE) features Single Instruction Multiple Data (SIMD) • SPEs in total can performs 230 GFLOPS for single precision floating-point operation • Elements of SPE: • Synergetic Processing Unit: data processing core • Memory Flow Controller (MFC): handles communication between main and local memory • 1 SPU can handle 4 single precision floating point operation simultaneously

  8. Cell Processor Figure: SPE block diagram Courtesy of http://www.ibm.com/developerworks/library/pa-cellperf/

  9. Molecular Dynamic • Modeling very large molecular systems at an atomic level. • Each atom interacts with all the others within a certain radius. • Cut-off distance between 10-12 Å (10 -10 meters) • Each steps is 1 femtosecond (10 -15 seconds) • For PS3Grid, use simple model of a single Gramicidin-A pore in a biological cell

  10. CellMD • Cell Processor => codes do not automatically run faster. • CellMD => optimized for Cell processor • Vectorization of compute-intensive code • Work distribution using multi-threaded programming techniques. • Avoid brancing => no hardware for branch prediction

  11. CellMD • Comparing MD running on 2GHz Opteron PC with CellMD running on IBM Cell Blade server. • Speedup is approximately 19 times for many different atoms size. • Benchmark result for 30,000 atom Gramicidin-A model on 2Ghz Opteron PC, IBM Cell blade server, PS3 using 1, 2, 4 and 6 SPEs.

  12. PS3Grid Server • Berkeley Open Infrastructure Network Computing (BOINC) based • Provides end-to-end distributed computing infrastructure • Generic User Authentication • File transfer • Client-side : wrapper for the project application • Work-flow management function

  13. PS3Grid Client • Yellow Dog Linux (YDL) on Playstation 3 + BOINC Client • Steps: 1. Get Instructions 2. Download application and input data 3. Compute 4. Upload output files 5. Report results

  14. Results • Generate a computational power of 300 personal computers. • Sustained floating-point performance of 400 GFLOPS. • 5 GB of Data • 100 ns of meluclar dynamics trajectories • Over 6 years of computation by a single PC Figure: Simulation of Gramicidin A • All this in approximately 1 month!

  15. Conclusion • CellMD performs one order of magnitude faster than MD • CellMD and BOINC can compete with expensive multiprocessor high performance computers. • Opening possibility of High Performance Network Computing.

  16. Next Implementation • GPU Grid • Using Nvidia Graphics Card • Implementing CUDA

  17. Question?

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