A portable global optimizer and linker
PLDI '88 Proceedings of the ACM SIGPLAN 1988 conference on Programming Language design and Implementation
An approach to ordering optimizing transformations
PPOPP '90 Proceedings of the second ACM SIGPLAN symposium on Principles & practice of parallel programming
An introduction to genetic algorithms
An introduction to genetic algorithms
An approach for exploring code improving transformations
ACM Transactions on Programming Languages and Systems (TOPLAS)
The SimpleScalar tool set, version 2.0
ACM SIGARCH Computer Architecture News
Optimizing for reduced code space using genetic algorithms
Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
GAPS: A Compiler Framework for Genetic Algorithm (GA) Optimised Parallelisation
HPCN Europe 1998 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
A Feasibility Study in Iterative Compilation
ISHPC '99 Proceedings of the Second International Symposium on High Performance Computing
Cache Models for Iterative Compilation
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
Compiler optimization-space exploration
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Predicting the impact of optimizations for embedded systems
Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems
Finding effective optimization phase sequences
Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Phase coupling and constant generation in an optimizing microcode compiler
MICRO 15 Proceedings of the 15th annual workshop on Microprogramming
Combined Selection of Tile Sizes and Unroll Factors Using Iterative Compilation
PACT '00 Proceedings of the 2000 International Conference on Parallel Architectures and Compilation Techniques
Fast searches for effective optimization phase sequences
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Finding effective compilation sequences
Proceedings of the 2004 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
A Model-Based Framework: An Approach for Profit-Driven Optimization
Proceedings of the international symposium on Code generation and optimization
ACME: adaptive compilation made efficient
LCTES '05 Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Fast and efficient searches for effective optimization-phase sequences
ACM Transactions on Architecture and Code Optimization (TACO)
A Heuristic Search Algorithm Based on Unified Transformation Framework
ICPPW '05 Proceedings of the 2005 International Conference on Parallel Processing Workshops
Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
Exhaustive Optimization Phase Order Space Exploration
Proceedings of the International Symposium on Code Generation and Optimization
MiBench: A free, commercially representative embedded benchmark suite
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
In search of near-optimal optimization phase orderings
Proceedings of the 2006 ACM SIGPLAN/SIGBED conference on Language, compilers, and tool support for embedded systems
Program optimization space pruning for a multithreaded gpu
Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
Program optimization carving for GPU computing
Journal of Parallel and Distributed Computing
Practical exhaustive optimization phase order exploration and evaluation
ACM Transactions on Architecture and Code Optimization (TACO)
A Framework for Exploring Optimization Properties
CC '09 Proceedings of the 18th International Conference on Compiler Construction: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009
Constructing an optimisation phase using grammatical evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Improving both the performance benefits and speed of optimization phase sequence searches
Proceedings of the ACM SIGPLAN/SIGBED 2010 conference on Languages, compilers, and tools for embedded systems
Eliminating false phase interactions to reduce optimization phase order search space
CASES '10 Proceedings of the 2010 international conference on Compilers, architectures and synthesis for embedded systems
Reducing training time in a one-shot machine learning-based compiler
LCPC'09 Proceedings of the 22nd international conference on Languages and Compilers for Parallel Computing
Mitigating the compiler optimization phase-ordering problem using machine learning
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Performance potential of optimization phase selection during dynamic JIT compilation
Proceedings of the 9th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Preliminary results for neuroevolutionary optimization phase order generation for static compilation
Proceedings of the 11th Workshop on Optimizations for DSP and Embedded Systems
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Program-specific or function-specific optimization phase sequences are universally accepted to achieve better overall performance than any fixed optimization phase ordering. A number of heuristic phase order space search algorithms have been devised to find customized phase orderings achieving high performance for each function. However, to make this approach of iterative compilation more widely accepted and deployed in mainstream compilers, it is essential to modify existing algorithms, or develop new ones that find near-optimal solutions quickly. As a step in this direction, in this paper we attempt to identify and understand the important properties of some commonly employed heuristic search methods by using information collected during an exhaustive exploration of the phase order search space. We compare the performance obtained by each algorithm with all others, as well as with the optimal phase ordering performance. Finally, we show how we can use the features of the phase order space to improve existing algorithms as well as devise new, and better performing search algorithms.