Dynamo: A Transparent Dynamic Optimization System

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Dynamo A Transparent Dynamic Optimization System

• Bala, Dueterwald, and Banerjia
• www.hpl.hp.com/cambridge/
• projects/Dynamo

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What is Dynamo?

• Dynamic translation quick dirty
  virtual-to-native translation focused
  native-to-native optimization of the most
  frequently executed portions of the translated
  code
• Dynamo focuses on the latter component

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Motivation for Dynamo?

• Programs typically spend most of their execution
  time in a small fraction of program code
• Optimizing code at translation-time can be
  counterproductive
• Dynamo allows for implementation of a quick
  virtual-to-native optimizer while letting the
  native-to-native optimizer clean up the
  translated code hot at runtime.

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Dynamo Implementation

• The native-to-native optimizer is implemented as
  a software layer tightly coupled to the CPU
  hardware

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How does Dynamo work?

• Dynamo operates at runtime
• Interprets an applications instructions via a
  native instruction interpreter
• Activates counter for application addresses
  satisfying a start of trace conditionan address
  reached by taking a backward branch in the
  application

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Establishing Hot traces

• If a start of trace is referred to again in the
  program, Dynamo goes into the fragment cache thus
  suspending itself and resuming native execution
  of the program.
• Key when an address becomes hot, it is
  statistically likely that the very next sequence
  of interpreted instructions will also be hot.
• When a counter is hot, the interpreter changes
  state and goes into trace selection mode.

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What is done with hot traces?

• Once selected the instructions are converted into
  a low-level IR (intermediate representation).
• Next, a lightweight optimizer processes the IR to
  create a fragmenta single-entry, multi-exit
  sequence of instructions laid out contiguously in
  memory.
• Finally, a linker emits the fragment code to the
  fragment cache and links it with others in the
  cache

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Continued

• The linker also gives a linker stub at the bottom
  of each fragment for each taken branch in the
  fragment.
• If the branch cannot be found in the fragment
  cache then it is set to jump to its corresponding
  linker stub which invokes Dynamos interpretive
  loop.
• Once a fragment is put into the fragment cache
  and linked, the hot counter is recycled, thus
  allowing Dynamo to control the counter storage
  amount used for trace selection.

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The above illustrates the trace selection,
fragment formation, and fragment linking
processes in terms of application flow
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Optimization Results for Dynamo

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Dynamos Worst-Case Scenario

• Designed to be an adaptive system, Dynamo can
  adjust its thresholds based on the changing
  behavior of the program running on top.
• This allows Dynamo to bail out of the overhead of
  its own operationallowing the input program to
  run directly on the machine. (typical result is
  break even)

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Whats Next?

• The eventual goal is to run applications that use
  Dynamo as a backend to optimize dynamically
  generated code.
• Virtual Machines being the primary focus.