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Superoptimization

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Compiler optimization technique

Superoptimization is the process where a compiler automatically finds the optimal sequence for a loop-free sequence of instructions. Real-world compilers generally cannot produce genuinely optimal code, and while most standard compiler optimizations only improve code partly, a superoptimizer's goal is to find the optimal sequence, the canonical form. Superoptimizers can be used to improve conventional optimizers by highlighting missed opportunities so a human can write additional rules.

History

The term superoptimization was first coined by Alexia Massalin in the 1987 paper Superoptimizer: A Look at the Smallest Program. The label "program optimization" has been given to a field that does not aspire to optimize but only to improve. This misnomer forced Massalin to call her system a superoptimizer, which is actually an optimizer to find an optimal program.

In 1992, the GNU Superoptimizer (GSO) was developed to integrate into the GNU Compiler Collection (GCC). Later work further developed and extended these ideas.

Techniques

Traditionally, superoptimizing is performed via exhaustive brute-force search in the space of valid instruction sequences. This is a costly method, and largely impractical for general-purpose compilers. Yet, it has been shown to be useful in optimizing performance-critical inner loops. It is also possible to use a SMT solver to approach the problem, vastly improving the search efficiency (although inputs more complex than a basic block remains out of reach).

In 2001, goal-directed superoptimizing was demonstrated in the Denali project by Compaq research. In 2006, answer set declarative programming was applied to superoptimization in the Total Optimisation using Answer Set Technology (TOAST) project at the University of Bath.

Superoptimization can be used to automatically generate general-purpose peephole optimizers.

Publicly available superoptimizers

Several superoptimizers are available for free download.

  • For the x86 family of instruction sets:
    • GNU superoptimizer (superopt) (GSO) (1992) – also supports many other ISAs
    • STOKE, a stochastic optimizer for x86-64 x86 assembly language.
  • For ARM:
    • Unbounded Superoptimizer, transforming LLVM IR into ARMv7-A assembly
  • For embedded systems:
  • For the JVM:
  • For LLVM IR:
    • souper superoptimizer for programs in the LLVM intermediate language.
  • For WebAssembly
    • slumps provides superoptimization for WASM programs based on souper.

See also

References

  1. Massalin, Henry (1987). "Superoptimizer: A look at the smallest program" (PDF). ACM SIGARCH Computer Architecture News. 15 (5): 122–126. doi:10.1145/36177.36194. Retrieved 2023-05-01. Given an instruction set, the superoptimizer finds the shortest program to compute a function. Startling programs have been generated, many of them engaging in convoluted bit-fiddling bearing little resemblance to the source programs which defined the functions. The key idea in the superoptimizer is a probabilistic test that makes exhaustive searches practical for programs of useful size.
  2. Joshi, Rajeev; Nelson, Greg; Randall, Keith (2002). "Denali: A Goal-directed Superoptimizer". ACM SIGPLAN Notices. 37 (5): 304–314. doi:10.1145/543552.512566.
  3. ^ Granlund, Torbjörn; Kenner, Richard. "Eliminating branches using a superoptimizer and the GNU C compiler". Proceedings of the ACM SIGPLAN 1992 conference on Programming language design and implementation - PLDI '92. CiteSeerX 10.1.1.58.3509. doi:10.1145/143095.14314. ISBN 978-0-89791475-8. S2CID 8825539.
  4. ^ "Index of /gnu/superopt". GNU Operating System. Free Software Foundation, Inc. 1995-06-14. Retrieved 2023-05-01.
  5. ^ Jangda, Abhinav; Yorsh, Greta (2017-10-25). Unbounded superoptimization. Onward!’17, October 25–27, 2017, Vancouver, Canada. pp. 78–88. doi:10.1145/3133850.3133856.
  6. Joshi, Rajeev; Nelson, Greg; Randall, Keith (2001-07-30). "Denali: a goal-directed superoptimizer". Compaq Systems Research Center. HP Labs. Hewlett-Packard Co. Retrieved 2023-05-01.
  7. "TOAST – KRRwiki". Department of Computer Science, Mathematical Foundations Group. Knowledge Representation and Reasoning (KRR) group. University of Bath. 2007-08-07. Archived from the original on 2012-11-28. Retrieved 2016-09-03.
  8. Brain, Martin; Crick, Tom; De Vos, Marina; Fitch, John (2006-08-17). "TOAST: Applying Answer Set Programming to Superoptimisation". In Etalle, Sandro; Truszczyński, Mirosław (eds.). Logic Programming. Springer-Verlag. pp. 270–284. doi:10.1007/11799573_21. ISBN 978-3-540-36636-2.
  9. Crick, Tom (2009). Superoptimisation: Provably Optimal Code Generation using Answer Set Programming (PhD thesis). University of Bath. Retrieved 2024-11-15.
  10. Bansal, Sorav; Aiken, Alex (2006). "Automatic Generation of Peephole Superoptimizers" (PDF). Retrieved 2023-05-01.
  11. "GNU Superoptimizer".
  12. StanfordPL. "STOKE". GitHub.
  13. Bansal, Sorav; Aiken, Alex (2008). "Binary Translation Using Peephole Superoptimizers" (PDF). Retrieved 2023-05-01.
  14. Serpell, Daniel (2003). "SuperOptimizer for Microchip's PIC microcontrollers". Google Sites. Archived from the original on 2016-10-11. Retrieved 2016-09-06.
  15. Serpell, Daniel (2003-06-21). "PIC Microcontroller SuperOptimizer". Freecode. Slashdot Media. Archived from the original on 2016-09-17. Retrieved 2016-09-06.
  16. "A feasibility study by Embecosm".
  17. Superoptimization – Embecosm Source Code
  18. Hume, Tom (2012-08-21). "Clojure program to exhaustively search for optimal Java programs". GitHub. Archived from the original on 2018-06-10. Retrieved 2016-09-06.
  19. Sasnauskas, Raimondas; Chen, Yang; Collingbourne, Peter; Ketema, Jeroen; Lup, Gratian; Taneja, Jubi; Regehr, John (2017). "Souper: A Synthesizing Superoptimizer". arXiv:1711.04422 . GitHub source code
  20. Cabrera Arteaga, Javier; Donde, Shrinish; Gu, Jian; Floros, Orestis; Satabin, Lucas; Baudry, Benoit; Monperrus, Martin (2020-03-23). Superoptimization of WebAssembly bytecode. MoreVMs: Workshop on Modern Language Runtimes, Ecosystems, and VMs. pp. 36–40. arXiv:2002.10213. doi:10.1145/3397537.3397567. GitHub source code
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