Nothing Special   »   [go: up one dir, main page]

Skip to main content

Towards a Spatial Model Checker on GPU

  • Conference paper
  • First Online:
Formal Techniques for Distributed Objects, Components, and Systems (FORTE 2021)

Abstract

The tool VoxLogicA merges the state-of-the-art library of computational imaging algorithms ITK with the combination of declarative specification and optimised execution provided by spatial logic model checking. The analysis of an existing benchmark for segmentation of brain tumours via a simple logical specification reached very high accuracy. We introduce a new, GPU-based version of VoxLogicA and present preliminary results on its implementation, scalability, and applications.

Research partially supported by the MIUR Project PRIN 2017FTXR7S “IT- MaTTerS” and by POR FESR Toscana 2014–2020 As. 1 - Az. 1.1.5 – S.A. A1 N. 7165 project STINGRAY. The authors are thankful to: Raffaele Perego, Franco Maria Nardini and the HPC-Lab at ISTI-CNR for a powerful GPU used in early development; Gina Belmonte, Diego Latella, and Mieke Massink, for fruitful discussions. The authors are listed in alphabetical order, having equally contributed to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    VoxLogicA: see https://github.com/vincenzoml/VoxLogicA.

  2. 2.

    VoxLogicA-GPU is Free and Open Source software. Its source code is currently available at https://github.com/vincenzoml/VoxLogicA/tree/experimental-gpu.

  3. 3.

    FSharp: see https://fsharp.org. NET Core: see https://dotnet.microsoft.com. OpenCL: see https://www.khronos.org/opencl. ITK: see https://itk.org.

  4. 4.

    Pointer jumping or path doubling is a design technique for parallel algorithms that operate on pointer structures, such as linked lists and directed graphs. It allows an algorithm to follow paths with a time complexity that is logarithmic with respect to the length of the longest path. It does this by “jumping” to the end of the path computed by neighbors. See https://en.wikipedia.org/wiki/Pointer_jumping.

  5. 5.

    See e.g. https://en.wikipedia.org/wiki/MapReduce.

  6. 6.

    Since checking termination takes log(N) iterations, instead of waiting for mainIteration to converge, reconnect is called each k iterations (\(k = 8\) in the current implementation, which experimentally proved to be a reasonable compromise).

  7. 7.

    All the tests we present, and the script to run them, are available in the source code repository https://github.com/vincenzoml/VoxLogicA/tree/experimental-gpu.

References

  1. Allegretti, S., Bolelli, F., Grana, C.: Optimized block-based algorithms to label connected components on GPUs. IEEE Trans. Parallel Distrib. Syst. 31(2), 423–438 (2020)

    Article  Google Scholar 

  2. Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 1–13 (2017)

    Article  Google Scholar 

  3. Banci Buonamici, F., Belmonte, G., Ciancia, V., Latella, D., Massink, M.: Spatial logics and model checking for medical imaging. Softw. Tools Technol. Transf. 22(2), 195–217 (2020). https://doi.org/10.1007/s10009-019-00511-9

    Article  Google Scholar 

  4. Bartocci, E., Bortolussi, L., Loreti, M., Nenzi, L.: Monitoring mobile and spatially distributed cyber-physical systems. In: Talpin, J., Derler, P., Schneider, K. (eds.) MEMOCODE 2017, pp. 146–155. ACM (2017)

    Google Scholar 

  5. Bartocci, E., Gol, E., Haghighi, I., Belta, C.: A formal methods approach to pattern recognition and synthesis in reaction diffusion networks. IEEE Trans. Control Netw. Syst. 5(1), 308–320 (2016)

    Article  MathSciNet  Google Scholar 

  6. Belmonte, G., Broccia, G., Ciancia, V., Latella, D., Massink, M.: Feasibility of spatial model checking for nevus segmentation. In: Bliudze, S., Semini, L. (eds.) FORMALISE@ICSE 2021 (2021, to appear)

    Google Scholar 

  7. Belmonte, G., Ciancia, V., Latella, D., Massink, M.: VoxLogicA: a spatial model checker for declarative image analysis. In: Vojnar, T., Zhang, L. (eds.) TACAS 2019. LNCS, vol. 11427, pp. 281–298. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17462-0_16

    Chapter  Google Scholar 

  8. Berkovich, S., Bonakdarpour, B., Fischmeister, S.: GPU-based runtime verification. In: IPDPS 2013, pp. 1025–1036. IEEE Computer Society (2013)

    Google Scholar 

  9. Broccia, G., Milazzo, P., Ölveczky, P.C.: Formal modeling and analysis of safety-critical human multitasking. Innovations Syst. Softw. Eng. 15(3–4), 169–190 (2019). https://doi.org/10.1007/s11334-019-00333-7

    Article  Google Scholar 

  10. Bussi, L., Ciancia, V., Gadducci, F.: A spatial model checker in GPU (extended version). CoRR abs/2010.07284 (2020)

    Google Scholar 

  11. Ciancia, V., Latella, D., Massink, M., Paškauskas, R., Vandin, A.: A tool-chain for statistical spatio-temporal model checking of bike sharing systems. In: Margaria, T., Steffen, B. (eds.) ISoLA 2016. LNCS, vol. 9952, pp. 657–673. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47166-2_46

    Chapter  Google Scholar 

  12. Ciancia, V., Gilmore, S., Grilletti, G., Latella, D., Loreti, M., Massink, M.: Spatio-temporal model checking of vehicular movement in public transport systems. Softw. Tools Technol. Transf. 20(3), 289–311 (2018). https://doi.org/10.1007/s10009-018-0483-8

    Article  Google Scholar 

  13. Grosu, R., Smolka, S., Corradini, F., Wasilewska, A., Entcheva, E., Bartocci, E.: Learning and detecting emergent behavior in networks of cardiac myocytes. Commun. ACM 52(3), 97–105 (2009)

    Article  Google Scholar 

  14. Gustafson, J.L.: Reevaluating Amdahl’s law. Commun. ACM 31(5), 532–533 (1988)

    Article  Google Scholar 

  15. Ma, M., Bartocci, E., Lifland, E., Stankovic, J., Feng, L.: SaSTl: spatial aggregation signal temporal logic for runtime monitoring in smart cities. In: ICCPS 2020, pp. 51–62. IEEE (2020)

    Google Scholar 

  16. Neele, T., Wijs, A., Bošnački, D., van de Pol, J.: Partial-order reduction for GPU model checking. In: Artho, C., Legay, A., Peled, D. (eds.) ATVA 2016. LNCS, vol. 9938, pp. 357–374. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46520-3_23

    Chapter  Google Scholar 

  17. Nenzi, L., Bortolussi, L., Ciancia, V., Loreti, M., Massink, M.: Qualitative and quantitative monitoring of spatio-temporal properties with SSTL. Log. Methods Comput. Sci. 14(4), 2:1–2:38 (2018)

    Google Scholar 

  18. Osama, M., Wijs, A.: Parallel SAT simplification on GPU architectures. In: Vojnar, T., Zhang, L. (eds.) TACAS 2019. LNCS, vol. 11427, pp. 21–40. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17462-0_2

    Chapter  Google Scholar 

  19. Shiloach, Y., Vishkin, U.: An O(logn) parallel connectivity algorithm. J. Algorithms 3(1), 57–67 (1982)

    Article  MathSciNet  Google Scholar 

  20. Tsigkanos, C., Kehrer, T., Ghezzi, C.: Modeling and verification of evolving cyber-physical spaces. In: Bodden, E., Schäfer, W., van Deursen, A., Zisman, A. (eds.) ESEC/FSE 2017, pp. 38–48. ACM (2017)

    Google Scholar 

  21. Wijs, A., Bošnački, D.: Many-core on-the-fly model checking of safety properties using GPUs. Softw. Tools Technol. Transf. 18(2), 169–185 (2016). https://doi.org/10.1007/s10009-015-0379-9

    Article  Google Scholar 

  22. Wijs, A., Neele, T., Bošnački, D.: GPUexplore 2.0: unleashing GPU explicit-state model checking. In: Fitzgerald, J., Heitmeyer, C., Gnesi, S., Philippou, A. (eds.) FM 2016. LNCS, vol. 9995, pp. 694–701. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48989-6_42

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vincenzo Ciancia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bussi, L., Ciancia, V., Gadducci, F. (2021). Towards a Spatial Model Checker on GPU. In: Peters, K., Willemse, T.A.C. (eds) Formal Techniques for Distributed Objects, Components, and Systems. FORTE 2021. Lecture Notes in Computer Science(), vol 12719. Springer, Cham. https://doi.org/10.1007/978-3-030-78089-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78089-0_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78088-3

  • Online ISBN: 978-3-030-78089-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics