Compiler Optimization-Space Exploration Spyridon Triantafyllis, Manish Vachharajani, Neil Vachharajani, David I. August

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Compiler Optimization-Space
Exploration
Authors
Spyridon Triantafyllis, Manish Vachharajani,
Neil Vachharajani, David I. August
Adrian Pop
IDA/PELAB
[email protected]
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} k=1…m
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:
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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
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