Partial evaluation of high-level imperative programming languages with applications in hard real-time systems

Nirkhe, Vivek; Pugh, William

doi:10.1145/143165.143223

Cited by 30 publications

(7 citation statements)

References 19 publications

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“…Recently Nirkhe and Pugh [21] developed an off-line system for a similar language for the specialization of hard real-time problems. Gomard and Jones report a self-applicable partial evaluator for a small untyped flow-chart language with S-expressions from Lisp as the only data structure [17].…”

Section: Related Workmentioning

confidence: 99%

Fortran program specialization

et al. 1995

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Abstract. We have developed and implemented a partial evaluator for a subset of Fortran 77. A partial evaluator is a tool for program transformation which takes as input a general program and a part of its input, and produces as output a specialized program. The goal is efficiency: a specialized program often runs an order of magnitude faster than the general program. The partial evaluator is based on the off-line approach and uses a binding-time analysis prior to the specialization phase. The source language includes multi-dimensional arrays, procedures and functions, as well as global storage. The system is presented and experimental results are given.

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Section: Related Workmentioning

confidence: 99%

Fortran program specialization

et al. 1995

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“…Program specialization. Program specialization can specialize a program according to various goals [14,20,22,27,31]. For instance, it can specialize according to common inputs [14,27].…”

Section: Related Workmentioning

confidence: 99%

Sound and precise analysis of parallel programs through schedule specialization

Tang

et al. 2012

SIGPLAN Not.

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Parallel programs are known to be difficult to analyze. A key reason is that they typically have an enormous number of execution interleavings, or schedules. Static analysis over all schedules requires over-approximations, resulting in poor precision; dynamic analysis rarely covers more than a tiny fraction of all schedules. We propose an approach called schedule specialization to analyze a parallel program over only a small set of schedules for precision, and then enforce these schedules at runtime for soundness of the static analysis results. We build a schedule specialization framework for C/C++ multithreaded programs that use Pthreads. Our framework avoids the need to modify every analysis to be schedule-aware by specializing a program into a simpler program based on a schedule, so that the resultant program can be analyzed with stock analyses for improved precision. Moreover, our framework provides a precise schedule-aware def-use analysis on memory locations, enabling us to build three highly precise analyses: an alias analyzer, a data-race detector, and a path slicer. Evaluation on 17 programs, including 2 real-world programs and 15 popular benchmarks, shows that analyses using our framework reduced may-aliases by 61.9%, false race reports by 69%, and path slices by 48.7%; and detected 7 unknown bugs in well-checked programs.

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“…As a result, the whole program has to be placed on the DP side, which is exactly what we intend to avoid. Program specialization and partial evaluation have been studied for decades [35,19,20,62,60,28], and are extensively used in compiler generation, real-time systems and many other areas [36,57,19,20,37,41,49,11]. To our knowledge, our approach is the first attempt to apply program specialization to privacy-preserving genomic computations.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving genomic computation through program specialization

Wang

et al. 2009

Proceedings of the 16th ACM Conference on Computer and Communications Security

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In this paper, we present a new approach to performing important classes of genomic computations (e.g., search for homologous genes) that makes a significant step towards privacy protection in this domain. Our approach leverages a key property of the human genome, namely that the vast majority of it is shared across humans (and hence public), and consequently relatively little of it is sensitive. Based on this observation, we propose a privacy-protection framework that partitions a genomic computation, distributing the part on sensitive data to the data provider and the part on the pubic data to the user of the data. Such a partition is achieved through program specialization that enables a biocomputing program to perform a concrete execution on public data and a symbolic execution on sensitive data. As a result, the program is simplified into an efficient query program that takes only sensitive genetic data as inputs. We prove the effectiveness of our techniques on a set of dynamic programming algorithms common in genomic computing. We develop a program transformation tool that automatically instruments a legacy program for specialization operations. We also demonstrate that our techniques can greatly facilitate secure multi-party computations on large biocomputing problems.

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Partial evaluation of high-level imperative programming languages with applications in hard real-time systems

Cited by 30 publications

References 19 publications

Fortran program specialization

Fortran program specialization

Sound and precise analysis of parallel programs through schedule specialization

Privacy-preserving genomic computation through program specialization

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