Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Stochastic Program Optimization. |
| Συγγραφείς: |
Schkufza, Eric1 (AUTHOR) eschkufz@cs.stanford.edu, Sharma, Rahul1 (AUTHOR) sharmar@cs.stanford.edu, Aiken, Alex1 (AUTHOR) aiken@cs.stanford.edu |
| Πηγή: |
Communications of the ACM. Feb2016, Vol. 59 Issue 2, p114-122. 9p. 2 Diagrams, 5 Charts, 4 Graphs. |
| Θεματικοί όροι: |
*Optimizers (Computer software), *Stochastic processes, *Markov chain Monte Carlo, Compilers (Computer programs), Program transformation |
| Περίληψη: |
The optimization of short sequences of loop-free, fixed-point assembly code sequences is an important problem in high-performance computing. However, the competing constraints of transformation correctness and performance improvement often force even special purpose compilers to produce sub-optimal code. We show that by encoding these constraints as terms in a cost function, and using a Markov Chain Monte Carlo sampler to rapidly explore the space of all possible code sequences, we are able to generate aggressively optimized versions of a given target code sequence. Beginning from binaries compiled by llvm −O0, we are able to produce provably correct code sequences that either match or outperform the code produced by gcc −O3, icc −O3, and in some cases expert handwritten assembly. [ABSTRACT FROM AUTHOR] |
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