CS 194 Research Results

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CS 194 Research Results

• Paul Salzman
• Advisor Professor Glenn Reinman
• Winter 2007 - Spring 2007

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Outline

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

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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.

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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.

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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.

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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.

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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.

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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?

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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.

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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.

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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

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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

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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
  engines functionality.

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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.

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Profiling ODE

• gprof utility
• Place trace code in the hotspot function
• Track each objects 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

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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)

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Constructing/Analyzing Predictors

• Use the locality data to construct pertinent
  predictors.
• Run the various predictors through the trace data
  to observe their performance.

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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)

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Locality Results

• Charts

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Locality Results

• Charts

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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.

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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.

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Predictor Construction (contd)

• 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.

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Predictor Results
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Predictor Results
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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.

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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.

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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.