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Meta Optimization

Meta Optimization. Improving Compiler Heuristics with Machine Learning. Mark Stephenson, Una-May O’Reilly, Martin Martin, and Saman Amarasinghe MIT Computer Architecture Group. Motivation. Compiler writers are faced with many challenges: Many compiler problems are NP-hard

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Meta Optimization

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  1. Meta Optimization Improving Compiler Heuristics with Machine Learning Mark Stephenson, Una-May O’Reilly, Martin Martin, and Saman Amarasinghe MIT Computer Architecture Group http://www.cag.lcs.mit.edu/metaopt

  2. Motivation • Compiler writers are faced with many challenges: • Many compiler problems are NP-hard • Modern architectures are inextricably complex • Simple models can’t capture architecture intricacies • Micro-architectures change quickly http://www.cag.lcs.mit.edu/metaopt

  3. Motivation • Heuristics alleviate complexity woes • Find good approximate solutions for a large class of applications • Find solutions quickly • Unfortunately… • They require a lot of trial-and-error tweaking to achieve suitable performance http://www.cag.lcs.mit.edu/metaopt

  4. Priority Functions • A heuristic’s Achilles heel • A single priority or cost function often dictates the efficacy of a heuristic • Priority functions rank the options available to a compiler heuristic • Graph coloring register allocation (selecting nodes to spill) • List scheduling (identifying instructions in worklist to schedule first) • Hyperblock formation (selecting paths to include) http://www.cag.lcs.mit.edu/metaopt

  5. Machine Learning • We propose using machine learning techniques to automatically search the priority function space • Search space is feasible • Make use of spare computer cycles http://www.cag.lcs.mit.edu/metaopt

  6. Case Study I: Hyperblock Formation • Find predicatable regions of control flow • Enumerate paths of control in region • Exponential, but in practice it’s okay • Prioritize paths based on several characteristics • The priority function we want to optimize • Add paths to hyperblock in priority order http://www.cag.lcs.mit.edu/metaopt

  7. Favor frequently Executed paths Favor short paths Penalize paths with hazards Favor parallel paths Case Study I: IMPACT’s Function http://www.cag.lcs.mit.edu/metaopt

  8. Hyperblock Formation • What are the important characteristic of a hyperblock formation priority function? • IMPACT uses four characteristics • Extract all the characteristics you can think of and have a machine learning algorithm find the priority function http://www.cag.lcs.mit.edu/metaopt

  9. Hyperblock Formation http://www.cag.lcs.mit.edu/metaopt

  10. * / - predictability num_ops 2.3 4.1 Genetic Programming • GP’s representation is a directly executable expression • Basically a lisp expression (or an AST) • In our case, GP variables are interesting characteristics of the program http://www.cag.lcs.mit.edu/metaopt

  11. Genetic Programming • Searching algorithm analogous to natural selection • Maintain a population of expressions • Selection • The fittest expressions in the population are more likely to reproduce • Sexual reproduction • Crossing over subexpressions of two expressions • Mutation http://www.cag.lcs.mit.edu/metaopt

  12. Genetic Programming Create initial population (initial solutions) • Most expressions in initial population are randomly generated • It also seeded with the compiler writer’s best guesses Evaluation Generation of variants (mutation and crossover) Selection Generations < Limit? END http://www.cag.lcs.mit.edu/metaopt

  13. Baseline expression in the one that’s distributed with Trimaran Genetic Programming Create initial population (initial solutions) • Each expression is evaluated by compiling and running benchmark(s) • Fitness is the relative speedup over the baseline on benchmark(s) Evaluation Generation of variants (mutation and crossover) Selection Generations < Limit? END http://www.cag.lcs.mit.edu/metaopt

  14. Genetic Programming Create initial population (initial solutions) • Just as with Natural Selection, the fittest individuals are more likely to survive and reproduce. Evaluation Generation of variants (mutation and crossover) Selection Generations < Limit? END http://www.cag.lcs.mit.edu/metaopt

  15. Genetic Programming Create initial population (initial solutions) Evaluation Generation of variants (mutation and crossover) Selection Generations < Limit? END http://www.cag.lcs.mit.edu/metaopt

  16. Genetic Programming Create initial population (initial solutions) • Use crossover and mutation to generate new expressions Evaluation Generation of variants (mutation and crossover) Selection Generations < Limit? END http://www.cag.lcs.mit.edu/metaopt

  17. Hyperblock ResultsCompiler Specialization 3.5 Train data set Alternate data set 3 (add (sub (cmul (gt (cmul $b0 0.8982 $d17)…$d7)) (cmul $b0 0.6183 $d28))) 2.5 (add (div $d20 $d5) (tern $b2 $d0 $d9)) 2 Speedup 1.5 1.54 1.23 1 0.5 0 toast Average huff_enc mpeg2dec huff_dec rawdaudio rawcaudio g721encode g721decode 129.compress http://www.cag.lcs.mit.edu/metaopt

  18. Hyperblock ResultsA General Purpose Priority Function http://www.cag.lcs.mit.edu/metaopt

  19. Cross ValidationTesting General Purpose Applicability http://www.cag.lcs.mit.edu/metaopt

  20. Case Study II: Register AllocationA General Purpose Priority Function http://www.cag.lcs.mit.edu/metaopt

  21. Register Allocation ResultsCross Validation http://www.cag.lcs.mit.edu/metaopt

  22. Conclusion • Machine learning techniques can identify effective priority functions • ‘Proof of concept’ by evolving two well known priority functions • Human cycles v. computer cycles http://www.cag.lcs.mit.edu/metaopt

  23. GP Hyperblock SolutionsGeneral Purpose (add (sub (mulexec_ratio_mean 0.8720) 0.9400) (mul 0.4762 (cmul (not has_pointer_deref) (mul 0.6727 num_paths) (mul 1.1609 (add (sub (mul (divnum_opsdependence_height) 10.8240) exec_ratio) (sub (mul (cmulhas_unsafe_jsrpredict_product_mean 0.9838) (sub 1.1039 num_ops_max)) (sub (muldependence_height_mean num_branches_max) num_paths))))))) Intron that doesn’t affect solution http://www.cag.lcs.mit.edu/metaopt

  24. GP Hyperblock SolutionsGeneral Purpose (add (sub (mulexec_ratio_mean 0.8720) 0.9400) (mul 0.4762 (cmul (not has_pointer_deref) (mul 0.6727 num_paths) (mul 1.1609 (add (sub (mul (divnum_opsdependence_height) 10.8240) exec_ratio) (sub (mul (cmulhas_unsafe_jsrpredict_product_mean 0.9838) (sub 1.1039 num_ops_max)) (sub (muldependence_height_mean num_branches_max) num_paths))))))) Favor paths that don’t have pointer dereferences http://www.cag.lcs.mit.edu/metaopt

  25. Favor highly parallel (fat) paths GP Hyperblock SolutionsGeneral Purpose (add (sub (mulexec_ratio_mean 0.8720) 0.9400) (mul 0.4762 (cmul (not has_pointer_deref) (mul 0.6727 num_paths) (mul 1.1609 (add (sub (mul (divnum_opsdependence_height) 10.8240) exec_ratio) (sub (mul (cmulhas_unsafe_jsrpredict_product_mean 0.9838) (sub 1.1039 num_ops_max)) (sub (muldependence_height_mean num_branches_max) num_paths))))))) http://www.cag.lcs.mit.edu/metaopt

  26. GP Hyperblock SolutionsGeneral Purpose (add (sub (mulexec_ratio_mean 0.8720) 0.9400) (mul 0.4762 (cmul (not has_pointer_deref) (mul 0.6727 num_paths) (mul 1.1609 (add (sub (mul (divnum_opsdependence_height) 10.8240) exec_ratio) (sub (mul (cmulhas_unsafe_jsrpredict_product_mean 0.9838) (sub 1.1039 num_ops_max)) (sub (muldependence_height_mean num_branches_max) num_paths))))))) If a path calls a subroutine that may have side effects, penalize it http://www.cag.lcs.mit.edu/metaopt

  27. Case Study I: IMPACT’s Algorithm A 4k 24k B C 10 4k 22k 2k E D 2k 10 25 F 28k G 28k http://www.cag.lcs.mit.edu/metaopt

  28. Case Study I: IMPACT’s Algorithm A 4k 24k B C 10 4k 22k 2k E D 2k 10 25 F 28k G 28k http://www.cag.lcs.mit.edu/metaopt

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