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Verified integrity properties for safe approximate program transformations

Published: 21 January 2013 Publication History

Abstract

Approximate computations (for example, video, audio, and image processing, machine learning, and many scientific computations) have the freedom to generate a range of acceptable results. Approximate program transformations (for example, task skipping and loop perforation) exploit this freedom to produce computations that can execute at a variety of points in an underlying accuracy versus performance trade-off space. One potential concern is that these transformations may change the semantics of the program and therefore cause the program to crash, perform an illegal operation, or otherwise violate its integrity.
We investigate how verifying integrity properties -- key correctness properties that the transformed computation must respect -- can enable the safe application of approximate program transformations. We present experimental results from a compiler that verifies integrity properties of perforated loops to enable the safe application of loop perforation.

References

[1]
Frama-C. http://frama-c.com/.
[2]
pycparser. http://code.google.com/p/pycparser/.
[3]
J. Ansel, C. Chan, Y. L. Wong, M. Olszewski, Q. Zhao, A. Edelman, and S. Amarasinghe. PetaBricks: A language and compiler for algorithmic choice. PLDI, 2009.
[4]
W. Baek and T. M. Chilimbi. Green: A framework for supporting energy-conscious programming using controlled approximation. PLDI, 2010.
[5]
C. Bienia, S. Kumar, J. Pal Singh, and K. Li. The PARSEC benchmark suite: Characterization and architectural implications. PACT, 2008.
[6]
M. Carbin, D. Kim, S. Misailovic, and M. Rinard. Proving acceptability properties of relaxed nondeterministic approximate programs. PLDI, 2012.
[7]
S. Chaudhuri, S. Gulwani, R. Lublinerman, and S. Navidpour. Proving programs robust. FSE, 2011.
[8]
H. Hoffmann, S. Misailovic, S. Sidiroglou, A. Agarwal, and M. Rinard. Using code perforation to improve performance, reduce energy consumption, and respond to failures. Technical Report MIT-CSAIL-TR-2009-042, MIT, 2009.
[9]
H. Hoffmann, Sidiroglou S., Carbin M., Misailovic S., Agarwal A., and M. Rinard. Dynamic knobs for responsive power-aware computing. ASPLOS, 2011.
[10]
S. Misailovic, D. Kim, and M. Rinard. Parallelizing sequential programs with statistical accuracy tests. Technical Report MIT-CSAIL-TR-2010-038, MIT, 2010.
[11]
S. Misailovic, S. Sidiroglou, H. Hoffmann, and M. Rinard. Quality of service profiling. ICSE, 2010.
[12]
S. Misailovic, D. Roy, and M. Rinard. Probabilistically accurate program transformations. SAS, 2011.
[13]
G. C. Necula, S. McPeak, S. P. Rahul, and W. Weimer. CIL: Intermediate language and tools for analysis and transformation of C programs. CC, 2002.
[14]
M. Rinard. Probabilistic accuracy bounds for fault-tolerant computations that discard tasks. ICS, 2006.
[15]
M. Rinard. Using early phase termination to eliminate load imbalances at barrier synchronization points. OOPSLA, 2007.
[16]
A. Sampson, W. Dietl, E. Fortuna, D. Gnanapragasam, L. Ceze, and D. Grossman. EnerJ: Approximate data types for safe and general low-power computation. PLDI, 2011.
[17]
S. Sidiroglou, S. Misailovic, H. Hoffmann, and M. Rinard. Managing performance vs. accuracy trade-offs with loop perforation. FSE, 2011.
[18]
Z. Zhu, S. Misailovic, J. Kelner, and M. Rinard. Randomized accuracy-aware program transformations for efficient approximate computations. POPL, 2012.

Cited By

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  • (2022)Accuracy-Aware CompilersApproximate Computing Techniques10.1007/978-3-030-94705-7_7(177-214)Online publication date: 3-Jan-2022
  • (2021)Diamont: Dynamic Monitoring of Uncertainty for Distributed Asynchronous ProgramsRuntime Verification10.1007/978-3-030-88494-9_10(184-206)Online publication date: 6-Oct-2021
  • (2020)Aloe: verifying reliability of approximate programs in the presence of recovery mechanismsProceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization10.1145/3368826.3377924(56-67)Online publication date: 22-Feb-2020
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Published In

cover image ACM Conferences
PEPM '13: Proceedings of the ACM SIGPLAN 2013 workshop on Partial evaluation and program manipulation
January 2013
162 pages
ISBN:9781450318426
DOI:10.1145/2426890
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 January 2013

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Author Tags

  1. approximate computing
  2. integrity properties
  3. loop perforation
  4. relaxed programs

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POPL '13
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PEPM '13 Paper Acceptance Rate 13 of 29 submissions, 45%;
Overall Acceptance Rate 66 of 120 submissions, 55%

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Cited By

View all
  • (2022)Accuracy-Aware CompilersApproximate Computing Techniques10.1007/978-3-030-94705-7_7(177-214)Online publication date: 3-Jan-2022
  • (2021)Diamont: Dynamic Monitoring of Uncertainty for Distributed Asynchronous ProgramsRuntime Verification10.1007/978-3-030-88494-9_10(184-206)Online publication date: 6-Oct-2021
  • (2020)Aloe: verifying reliability of approximate programs in the presence of recovery mechanismsProceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization10.1145/3368826.3377924(56-67)Online publication date: 22-Feb-2020
  • (2019)Verifying safety and accuracy of approximate parallel programs via canonical sequentializationProceedings of the ACM on Programming Languages10.1145/33605453:OOPSLA(1-29)Online publication date: 10-Oct-2019
  • (2019)PANDORA: a parallelizing approximation-discovery framework (WIP paper)Proceedings of the 20th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3316482.3326345(198-202)Online publication date: 23-Jun-2019
  • (2019)ApproxSymate: path sensitive program approximation using symbolic executionProceedings of the 20th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3316482.3326341(148-162)Online publication date: 23-Jun-2019
  • (2019)When Are Software Verification Results Valid for Approximate Hardware?Tests and Proofs10.1007/978-3-030-31157-5_1(3-20)Online publication date: 23-Sep-2019
  • (2018)Leto: verifying application-specific hardware fault tolerance with programmable execution modelsProceedings of the ACM on Programming Languages10.1145/32765332:OOPSLA(1-30)Online publication date: 24-Oct-2018
  • (2018)Validity of Software Verification Results on Approximate HardwareIEEE Embedded Systems Letters10.1109/LES.2017.275820010:1(22-25)Online publication date: 1-Mar-2018
  • (2017)Probabilistic Horn Clause VerificationStatic Analysis10.1007/978-3-319-66706-5_1(1-22)Online publication date: 19-Aug-2017
  • Show More Cited By

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