Synthesis of machine code from semantics

SrinivasanVenkatesh,; RepsThomas,

doi:10.1145/2813885.2737960

Cited by 14 publications

(34 citation statements)

References 23 publications

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“…McSynth [37] is a synthesizer that generates machine code instructions from semantic specifications of their behavior. While McSynth alone does not consider optimality, the authors note that it could be extended to generate optimal solutions with a naive algorithm that generates every solution to the synthesis problem and returns the one among them with minimum cost.…”

Section: Related Workmentioning

confidence: 99%

Optimizing synthesis with metasketches

et al. 2016

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Many advanced programming tools---for both end-users and expert developers---rely on program synthesis to automatically generate implementations from high-level specifications. These tools often need to employ tricky, custom-built synthesis algorithms because they require synthesized programs to be not only correct, but also optimal with respect to a desired cost metric, such as program size. Finding these optimal solutions efficiently requires domain-specific search strategies, but existing synthesizers hard-code the strategy, making them difficult to reuse. This paper presents metasketches, a general framework for specifying and solving optimal synthesis problems. metasketches make the search strategy a part of the problem definition by specifying a fragmentation of the search space into an ordered set of classic sketches. We provide two cooperating search algorithms to effectively solve metasketches. A global optimizing search coordinates the activities of local searches, informing them of the costs of potentially-optimal solutions as they explore different regions of the candidate space in parallel. The local searches execute an incremental form of counterexample-guided inductive synthesis to incorporate information sent from the global search. We present Synapse, an implementation of these algorithms, and show that it effectively solves optimal synthesis problems with a variety of different cost functions. In addition, metasketches can be used to accelerate classic (non-optimal) synthesis by explicitly controlling the search strategy, and we show that Synapse solves classic synthesis problems that state-of-the-art tools cannot.

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Section: Related Workmentioning

confidence: 99%

Optimizing synthesis with metasketches

et al. 2016

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“…A couple of designs that guarantee correctness have been developed by Joshi et al [193,194] and Crick et al [90], who applied automatic theorem proving and a method called answer set programming, respectively. Recently, a similar technique based on quantifier-free bit-vector logic formulas was introduced by Srinivasan and Reps [312].…”

Section: Further Developmentsmentioning

confidence: 99%

Instruction Selection

Blindell

2016

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“…Component-based synthesis [13] introduces an alternative encoding, which significantly improves the performance of a symbolic search; however, even with this encoding, the symbolic solver from SyGus'14 competition still did not perform well [3]. Another pruning strategy using divide-and-conquer to break QFBV formula potentially reduces synthesis time by many orders of magnitude [28], but it is likely synthesizing the same program as given. The refutation-based approach used in the CVC4 solver [23], the winner of SyGus'15 competition, is also not suitable for superoptimization problems because it tends to produce very large solutions with many if-else constructs.…”

Section: Related Workmentioning

confidence: 99%

Scaling up Superoptimization

Phothilimthana

Thakur

Bodík

et al. 2016

SIGOPS Oper. Syst. Rev.

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Developing a code optimizer is challenging, especially for new, idiosyncratic ISAs. Superoptimization can, in principle, discover machine-specific optimizations automatically by searching the space of all instruction sequences. If we can increase the size of code fragments a superoptimizer can optimize, we will be able to discover more optimizations.We develop LENS, a search algorithm that increases the size of code a superoptimizer can synthesize by rapidly pruning away invalid candidate programs. Pruning is achieved by selectively refining the abstraction under which candidates are considered equivalent, only in the promising part of the candidate space. LENS also uses a bidirectional search strategy to prune the candidate space from both forward and backward directions. These pruning strategies allow LENS to solve twice as many benchmarks as existing enumerative search algorithms, while LENS is about 11-times faster.Additionally, we increase the effective size of the superoptimized fragments by relaxing the correctness condition using contexts (surrounding code). Finally, we combine LENS with complementary search techniques into a cooperative superoptimizer, which exploits the stochastic search to make random jumps in a large candidate space, and a symbolic (SAT-solver-based) search to synthesize arbitrary constants. While existing superoptimizers consistently solve 9-16 out of 32 benchmarks, the cooperative superoptimizer solves 29 benchmarks. It can synthesize code fragments that are up to 82% faster than code generated by gcc -O3 from WiBench and MiBench.

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Synthesis of machine code from semantics

Cited by 14 publications

References 23 publications

Optimizing synthesis with metasketches

Optimizing synthesis with metasketches

Instruction Selection

Scaling up Superoptimization

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