Synthesizing replacement classes

Samak, Malavika; Kim, Deokhwan; Rinard, Martin

doi:10.1145/3371120

Cited by 12 publications

(3 citation statements)

References 69 publications

(76 reference statements)

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“…One line of studies reduces equivalence checking to proving specific verification conditions, such as relational verification [29]- [34]. Similar approaches include using symbolic execution for loop-free programs [27], [35]- [38]. Different from the existing efforts that target sophisticated program constructs, EQDAC focuses on a domainspecific language in FinTech systems, pursuing efficiency over the capability of handling a flexible program syntax.…”

Section: A Program Equivalence Checkingmentioning

confidence: 99%

Verifying Data Constraint Equivalence in FinTech Systems

Wang¹,

Fan²,

Yao³

et al. 2023

Preprint

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Data constraints are widely used in FinTech systems for monitoring data consistency and diagnosing anomalous data manipulations. However, many equivalent data constraints are created redundantly during the development cycle, slowing down the FinTech systems and causing unnecessary alerts. We present EQDAC, an efficient decision procedure to determine the data constraint equivalence. We first propose the symbolic representation for semantic encoding and then introduce two light-weighted analyses to refute and prove the equivalence, respectively, which are proved to achieve in polynomial time. We evaluate EQDAC upon 30,801 data constraints in a FinTech system. It is shown that EQDAC detects 11,538 equivalent data constraints in three hours. It also supports efficient equivalence searching with an average time cost of 1.22 seconds, enabling the system to check new data constraints upon submission.

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Section: A Program Equivalence Checkingmentioning

confidence: 99%

Verifying Data Constraint Equivalence in FinTech Systems

Wang¹,

Fan²,

Yao³

et al. 2023

Preprint

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show abstract

“…Another common approach is a backend-independent API [93,122] allowing for migrations to happen under the hood. Samak et al [111,112] approach a similar problem in the object-oriented space, using embeddings and synthesis to generate adapter classes for drop-in replacement classes in Java. The tools, Mask and ClassFinder, use symbolic execution to prove equivalence, a technique which does not scale to FFT-sized algorithms.…”

Section: Existing Compilation Techniquesmentioning

confidence: 99%

Bind the gap: compiling real software to hardware FFT accelerators

Woodruff

Armengol-Estapé

Ainsworth

et al. 2022

Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation

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Specialized hardware accelerators continue to be a source of performance improvement. However, such specialization comes at a programming price. The fundamental issue is that of a mismatch between the diversity of user code and the functionality of fixed hardware, limiting its wider uptake.Here we focus on a particular set of accelerators: those for Fast Fourier Transforms. We present FACC (Fourier ACcelerator Compiler), a novel approach to automatically map legacy code to Fourier Transform accelerators. It automatically generates drop-in replacement adapters using Input-Output (IO)-based program synthesis that bridge the gap between user code and accelerators. We apply FACC to unmodified GitHub C programs of varying complexity and compare against two existing approaches. We target FACC to a high-performance library, FFTW, and two hardware accelerators, the NXP PowerQuad and the Analog Devices FFTA, and demonstrate mean speedups of 9x, 17x and 27x respectively.

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“…For example, Volt [Pailoor et al 2021] is the latest data structure synthesizer, which aims to refine the data structure satisfying integrity constraints when introducing auxiliary fields. Mask [Samak et al 2020] replaces outdated data structures by synthesizing their methods with the latest ones. The two synthesizers assure functional correctness while do not concern the efficiency.…”

Section: Data Structure Synthesismentioning

confidence: 99%

Complexity-guided container replacement synthesis

Wang

Yao

Tang

et al. 2022

Proc. ACM Program. Lang.

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Containers, such as lists and maps, are fundamental data structures in modern programming languages. However, improper choice of container types may lead to significant performance issues. This paper presents Cres, an approach that automatically synthesizes container replacements to improve runtime performance. The synthesis algorithm works with static analysis techniques to identify how containers are utilized in the program, and attempts to select a method with lower time complexity for each container method call. Our approach can preserve program behavior and seize the opportunity of reducing execution time effectively for general inputs. We implement Cres and evaluate it on 12 real-world Java projects. It is shown that Cres synthesizes container replacements for the projects with 384.2 KLoC in 14 minutes and discovers six categories of container replacements, which can achieve an average performance improvement of 8.1%.

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Synthesizing replacement classes

Cited by 12 publications

References 69 publications

Verifying Data Constraint Equivalence in FinTech Systems

Verifying Data Constraint Equivalence in FinTech Systems

Bind the gap: compiling real software to hardware FFT accelerators

Complexity-guided container replacement synthesis

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