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CS 194 Research Results

CS 194 Research Results. Paul Salzman Advisor: Professor Glenn Reinman Winter 2007 - Spring 2007. Outline. Motivation Initial Project: Polymorphism Idea Progress and Problems Final Project: Object Level Locality Idea Previous Work Methodology Results.

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CS 194 Research Results

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  1. CS 194 Research Results Paul Salzman Advisor: Professor Glenn Reinman Winter 2007 - Spring 2007

  2. Outline • Motivation • Initial Project: Polymorphism • Idea • Progress and Problems • Final Project: Object Level Locality • Idea • Previous Work • Methodology • Results

  3. Motivation: Interactive Entertainment • Simulates virtual worlds with objects and characters that interact. • Large computational requirements with the increasing demand for realism. • Interactions previously relied on predefined animations. • Real time physics engines currently used to dynamically calculate interactions. • These systems can use all the performance boosts possible to make them more feasible.

  4. Initial Project: Polymorphism • Explore a currently proposed dedicated physics architecture, ParallAX. • Explore how different facets of interactive entertainment can be applied to a dedicated architecture.

  5. Initial Project: Progress • Investigating SESC as a viable architectural simulator. • Creating an architecture with heterogeneous cores in SESC. • Hard coding in the ParallAX topology into SESC.

  6. Initial Project: Difficulties • Lack of up to date simulator documentation. • Key out-of-the-box simulator feature, network communication latency modeler, removed without mention. • Simulator construction occupying far too much time.

  7. Initial Project: Experience • Working in a large, open-source code base with bad documentation. • Academic and industry research projects do not always end successfully. • Learn when to pursue a new idea.

  8. Object Level Locality in Real-Time Physics Applications • Idea: Objects in motion stay in motion. • Can this lend to locality at the object level in physics simulation? • If so, how can this be harness to speed up real-time physics simulation?

  9. Value Prediction in Physics Simulation • We will observing load values pertaining to physical objects in the simulator. • Loads are long latency instructions. • Accurately predicting loads can increase instruction level parallelism and in turn performance.

  10. Instruction Level Parallelism (ILP) • Independent instructions can be executed simultaneously. • Data dependencies prevent the processor from working on the chain of dependent instructions. • Predictions allow the processor to attempt useful work past data dependencies.

  11. Previous Work • Value Prediction has been shown as a viable option for performance enhancement. • Various implementations of value predictors have been explored. • Methods to improve which instructions to predict and how to predict with confidence. • Correlations between High-Level information and lower level locality have been found

  12. Methodology • Profile object info in a real-time physics simulator • Observe locality in values associated with physical objects • Construct predictors based on the locality information • Observe the performance of these predictors

  13. Open Dynamics Engine (ODE) • Open source physics engine. • Used commercially on the PC, XBOX 360, and other IE platforms. • Large code library, hone in on hotspot functions using gprof. • Use a complex benchmark to exercise the physics engine’s functionality.

  14. Benchmark • Many entities in the enviornment. • Collisions between multitudes of stacked boxes, rigid bodies, and rag doll constraint humanoid objects. • Used with permission from Dr. Thomas Yeh.

  15. Profiling ODE • gprof utility • Place trace code in the hotspot function • Track each object’s values • Associate values with the objects id (address) • Maintain an index via the PC as well as with the (PC XOR object id) • Maintain chronological order

  16. Observing and Analyzing Locality • Parse through the trace code observing locality with respect to the two indexing methods. • Check for adjacent values • Stride values • Trivial values (0,1,-1)

  17. Constructing/Analyzing Predictors • Use the locality data to construct pertinent predictors. • Run the various predictors through the trace data to observe their performance.

  18. ODE Hotspots • 43%: dBoxBox - Bounding Box collision function • 18%: collideAABBs – General geometric collision function • These two functions were chosen to profile. • The load instructions observed scale up to ~40% (rough value)

  19. Locality Results • Charts

  20. Locality Results • Charts

  21. Locality Results • Adjacent values appear very often. • Stride values do not appear (intuitively runs with the idea of a physical object) • Trivial values appear in varying forms across the functions. • Different trivial values may appear for different physics engines.

  22. Predictor Construction • Adjacent Values • Last Value Predictor • Simple Implementation, • Finite Context Method Predictor (FCM) • Maintains small table of previous values • Tracks appearance of tables values • Chooses most likely candidate • Trivial Values • 0 and -1 predictors. • Extremely simply implementation.

  23. Predictor Construction (cont’d) • Same two functions will be profiled. • The predictors will be indexed in the same fashion as the locality data • By PC • By (PC XOR object ID) • No limit will be placed on the size of the predictor tables • Avoids constructive and destructive aliasing.

  24. Predictor Results

  25. Predictor Results

  26. Predictor Results • FCM2 appears to function most accurately. • Predictors indexed by (PC XOR object ID) act exactly as zero value predictors • By convention, a predictor that has not seen a value will guess zero. • As expected, trivial value predictors have hit rates equal to the appearance of their trivial values in the locality data.

  27. Summary – Objects in Motion… • Object level locality does appear in real-time physics simulators. • This data can be leveraged by further research to increase ILP in IE architecture. • Next Step: Pursuing the connection between the high-level data to architecture.

  28. References • [1] Brad Calder, Glenn Reinman, and Dean Tullsen. Selective Value Prediction. In 26th International Symposium on Computer Architecture, May 1999 • [2] Mikko Lipasti, Christopher Wilkerson, and John Shen. Value locality and load value prediction. In Seventh International Conference on Architectural Support for Programming Languages and Operating Systems, 1996. • [3] Open Dynamics Engine. http://ode.org/. • [4] Yiannak Sazeides and James E. Smith. The predictability of data values. In 30th International Symposium on Microarchitecture, pages 248-258, December1997. • [5] Thomas Y. Yeh, Petros Faloutsos, and Glenn Reinman. Accelerating Real-Time Physics Simulation by Leveraging High-Level Information. In UCLA CSD-TR 060023, 2006.

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