Conditionally correct superoptimization

Sharma, Rahul; Schkufza, Eric; Churchill, Berkeley; Aiken, Alex

doi:10.1145/2814270.2814278

Cited by 24 publications

(19 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimizing NaCl code requires more aggressive transformations compared to the ones described in previous works that use STOKE [36,39,40]. In particular, previous work made no changes to the control flow.…”

Section: Transformationsmentioning

confidence: 99%

Sound Loop Superoptimization for Google Native Client

Churchill

Sharma

Bastien

et al. 2017

SIGOPS Oper. Syst. Rev.

Self Cite

View full text Add to dashboard Cite

Software fault isolation (SFI) is an important technique for the construction of secure operating systems, web browsers, and other extensible software. We demonstrate that superoptimization can dramatically improve the performance of Google Native Client, a SFI system that ships inside the Google Chrome Browser. Key to our results are new techniques for superoptimization of loops: we propose a new architecture for superoptimization tools that incorporates both a fully sound verification technique to ensure correctness and a bounded verification technique to guide the search to optimized code. In our evaluation we optimize 13 libc string functions, formally verify the correctness of the optimizations and report a median and average speedup of 25% over the libraries shipped by Google.

show abstract

Section: Transformationsmentioning

confidence: 99%

Sound Loop Superoptimization for Google Native Client

Churchill

Sharma

Bastien

et al. 2017

SIGOPS Oper. Syst. Rev.

Self Cite

View full text Add to dashboard Cite

show abstract

“…These methods use stochastic search, such as Markov Chain Monte Carlo (MCMC) sampling or Evolutionary Computation (EC). Schkufza et al proposed STOKE [24,85,86,93] using MCMC sampling for X86-64 assembly codes to improve runtime. STOKE has the same overarching goal as GEVO, which is to search for program optimizations without guaranteeing exact program semantics.…”

Section: Program Synthesis and Superoptimizationmentioning

confidence: 99%

Gevo

Liou

Wang

Forrest

et al. 2020

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

GPUs are a key enabler of the revolution in machine learning and high-performance computing, functioning as de facto co-processors to accelerate large-scale computation. As the programming stack and tool support have matured, GPUs have also become accessible to programmers, who may lack detailed knowledge of the underlying architecture and fail to fully leverage the GPU's computation power. GEVO (Gpu optimization using EVOlutionary computation) is a tool for automatically discovering optimization opportunities and tuning the performance of GPU kernels in the LLVM representation. GEVO uses population-based search to find edits to GPU code compiled to LLVM-IR and improves performance on desired criteria while retaining required functionality. We demonstrate that GEVO improves the execution time of general-purpose GPU programs and machine learning (ML) models on NVIDIA Tesla P100. For the Rodinia benchmarks, GEVO improves GPU kernel runtime performance by an average of 49.48% and by as much as 412% over the fully compiler-optimized baseline. If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline by an average of 51.08%. For the ML workloads, GEVO achieves kernel performance improvement for SVM on the MNIST handwriting recognition (3.24×) and the a9a income prediction (2.93×) datasets with no loss of model accuracy. GEVO achieves 1.79× kernel performance improvement on image classification using ResNet18/CIFAR-10, with less than 1% model accuracy reduction.

show abstract

“…The second relevant line of work is synthesis-aided compilation [48,49,55,56]. This work is limited to generating low-level straight-line code, which is an easy target for correctness validation and cost estimation.…”

Section: Domain-specific Program Synthesis Most Program Synthesis Tecmentioning

confidence: 99%

“…One of the greatest challenges in software development is to write programs that are not only correct but also efficient with respect to memory usage, execution time, or domain specific resource metrics. For this reason, automatically optimizing program performance has long been a goal of synthesis, and several existing techniques tackle this problem for low-level straight-line code [9,48,49,55,56] or add efficient synchronization to concurrent programs [11,12,21,28]. However, the developed techniques are not applicable to recent advances in the synthesis of high-level looping or recursive programs manipulating custom data structures [22,35,39,46,50,51].…”

Section: Introductionmentioning

confidence: 99%

Resource-guided program synthesis

Knoth

Wang

Polikarpova

et al. 2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

View full text Add to dashboard Cite

This article presents resource-guided synthesis, a technique for synthesizing recursive programs that satisfy both a functional specification and a symbolic resource bound. The technique is type-directed and rests upon a novel type system that combines polymorphic refinement types with potential annotations of automatic amortized resource analysis. The type system enables efficient constraint-based type checking and can express precise refinement-based resource bounds. The proof of type soundness shows that synthesized programs are correct by construction. By tightly integrating program exploration and type checking, the synthesizer can leverage the user-provided resource bound to guide the search, eagerly rejecting incomplete programs that consume too many resources. An implementation in the resource-guided synthesizer ReSyn is used to evaluate the technique on a range of recursive data structure manipulations. The experiments show that ReSyn synthesizes programs that are asymptotically more efficient than those generated by a resource-agnostic synthesizer. Moreover, synthesis with ReSyn is faster than a naive combination of synthesis and resource analysis. ReSyn is also able to generate implementations that have a constant resource consumption for fixed input sizes, which can be used to mitigate side-channel attacks.

show abstract

Conditionally correct superoptimization

Cited by 24 publications

References 37 publications

Sound Loop Superoptimization for Google Native Client

Sound Loop Superoptimization for Google Native Client

Gevo

Resource-guided program synthesis

Contact Info

Product

Resources

About