Compiler Optimization-Space Exploration

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Compiler Optimization-SpaceExploration
Authors Spyridon Triantafyllis, Manish
Vachharajani, Neil Vachharajani, David I. August

• Adrian Pop
• IDA/PELAB
• adrpo_at_ida.liu.se

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Outline

• Introduction
• The Problem
• Predictive Heuristics and A Priori Evaluation
• Some Solutions
• Iterative Compilation and A Posteriori Evaluation
• Our Solution
• Optimization-Space Exploration
• Evaluation
• Conclusion

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Introduction

• Processors
• become more complex
• incorporate additional computational resources
• Consequence
• Compilers
• become more complex
• use aggressive optimizations
• have to use predictive heuristics in order to
  decide where and to what extend optimizations
  should be applied

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The Problem Predictive Heuristics

• Predictive Heuristics
• tries to determine a priori the benefits of
  certain optimization
• are tuned to give the highest average performance
• The Result
• significant performance gains are unrealized!

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Some Solutions Iterative Compilation

• Iterative Compilation
• optimize the programs in many ways
• choose a posteriori the best code version
• Pitfall of current schemes
• prohibitive compilation times!
• limitation to specific architectures
• embedded systems
• limited to specific optimizations

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Our solution Optimization-Space Exploration

• OSE Compiler (Practical Iterative Compilation)
• explores the space of optimization configurations
  through multiple compilations
• it uses the experience of the compiler writer to
  prune the number of configurations that should be
  explored
• uses a performance estimator to not evaluate the
  code by execution
• selects a custom configuration for each code
  segment
• selects next optimization configuration by
  examining the previous configurations
  characteristics

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OSE over many conigurations
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OSE Limiting the Search Space

• Optimization Space
• derived from a set of optimization parameters
• Optimization Parameters
• Optimization level
• High Level Optimization (HLO) level
• Micro-architecture type
• Coalesce adjacent loads and stores
• HLO phase order
• Loop unroll limit
• Update dependencies after unrolling
• Perform software pipelining

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OSE Limiting the Search Space

• Optimization Parameters
• Heuristic to disable software pipelining
• Allow control speculation during software
  pipelining
• Software pipeline outer loops
• Enable if-conversion heuristic for software
  pipelining
• Software pipeline loops with early exists
• Enable if conversion
• Enable non-standard predication
• Enable pre-scheduling
• Scheduler ready criterion

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OSE Limiting the Search Space

• Compiler Construction-time Pruning
• limit the total number of configurations that
  will be considered at compile time
• construct a set S with at most N configurations
• S is chosen by determining the impact on a
  representative set of code segments C as follows
• S default configuration configurations with
  non-default parameters
• a) run C compiled with S on real hardware and
  retain in S only the valuable configurations
• b) consider the combination of configurations in
  S as S repeat a) for S and retain only the
  best N configurations
• repeat b) until no new configurations can be
  generated or the speedup does not improve

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OSE Limiting the Search Space

• Characterizing Configuration Correlations
• build a optimization configuration tree
• critical configurations conf. at the same level
• 1. Construct O set of m most important
• configurations in S for all
• code segments in C
• 2. Choose all oi in O as the successor of the
  root node.
• 3. For each configurations oi in O
• 4. Construct Ci cj argmax(pj,k) i k1m
• 5. Repeat steps 3, 4 to find oi successors
  limiting
• the code segments to Ci and configurations to
  S\O.

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OSE Limiting the Search Space

• Compile-time search
• do a breadth first search on the optimization
  configuration tree
• choose the configuration that yields the best
  estimated performance

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OSE Limiting the Search Space

• Limit the OSE application
• to hot code segments
• hot code segments are identified through
  profiling or hardware performance counters during
  a program run

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Evaluation

• OSE Compiler Algorithm
• 1. Profile the code
• 2. For each Function
• 3. Compile to the high level IR
• 4. Optimize using HLO
• 5. For each Function
• 6. If the function is hot
• 7. Perform OSE on second HLO and CG
• 8. Emit the function using the best
• configuration
• 9. If the function is not hot use the
• standard configuration

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Compile-time Performance Estimation

• Model Based on
• Ideal Cycle Count T
• Data cache performance, Lambda, L
• Instruction cache performance, I
• Branch misprediction, B

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Results
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Conclusions