Skip to main content
Springer Nature Link
Log in

Generation of high-performance code based on a domain-specific language for algorithmic skeletons

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Parallel programming can be difficult and error prone, in particular if low-level optimizations are required in order to reach high performance in complex environments such as multi-core clusters using MPI and OpenMP. One approach to overcome these issues is based on algorithmic skeletons. These are predefined patterns which are implemented in parallel and can be composed by application programmers without taking care of low-level programming aspects. Support for algorithmic skeletons is typically provided as a library. However, optimizations are hard to implement in this setting and programming might still be tedious because of required boiler plate code. Thus, we propose a domain-specific language for algorithmic skeletons that performs optimizations and generates low-level C++ code. Our experimental results on four benchmarks show that the models are significantly shorter and that the execution time and speedup of the generated code often outperform equivalent library implementations using the Muenster Skeleton Library.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. The model-driven software development community prefers the notion DSL model rather than DSL program.

  2. Due to the lack of space, only the overall structure and the main concepts of the language are presented here and an excerpt of the DSL is given in Listing 2. The full DSL specification can be found in our code repository [25].

  3. The Xtext representation of the full DSL is available in our code repository [25].

References

  1. Chapman B, Jost G, van der Pas R (2008) Using OpenMP: portable shared memory parallel programming. Scientific and engineering computation. MIT Press, Cambridge

    Google Scholar 

  2. Gropp W, Lusk E, Skjellum A (2014) Using MPI: portable parallel programming with the message-passing interface. Scientific and engineering computation, 3rd edn. MIT Press, Cambridge

    Google Scholar 

  3. Nickolls J, Buck I, Garland M, Skadron K (2008) Scalable parallel programming with CUDA. Queue 6(2):40–53

    Article  Google Scholar 

  4. Stone JE, Gohara D, Shi G (2010) OpenCL: a parallel programming standard for heterogeneous computing systems. Comput Sci Eng 12(3):66–73

    Article  Google Scholar 

  5. Cole M (1991) Algorithmic skeletons: structured management of parallel computation. MIT Press, Cambridge

    MATH  Google Scholar 

  6. Ernsting S, Kuchen H (2012) Algorithmic skeletons for multi-core, multi-GPU systems and clusters. Int J High Perform Comput Netw 7(2):129–138

    Article  Google Scholar 

  7. Ernsting S, Kuchen H (2017) Data parallel algorithmic skeletons with accelerator support. Int J Parallel Program 45(2):283–299

    Article  Google Scholar 

  8. Aldinucci M, Danelutto M, Meneghin M, Torquati M, Kilpatrick P (2010) Efficient streaming applications on multi-core with FastFlow: the biosequence alignment test-bed. In: Chapman B, Desprez F, Joubert GR, Lichnewsky A, Peters F, Priol T (eds) Parallel computing: from multicores and GPU’s to petascale, advances in parallel computing, vol 19. IOS Press, Amsterdam

    Google Scholar 

  9. Benoit A, Cole M, Gilmore S, Hillston J (2005) Flexible skeletal programming with eSkel. In: Cunha JC, Medeiros PD, (eds) Proceedings of the 11th International Euro-Par Conference on Parallel Processing (Euro-Par ’05), Lecture Notes in Computer Science, vol 3648. Springer, pp 761–770

  10. Ernstsson A, Li L, Kessler C (2017) SkePU 2: Flexible and type-safe skeleton programming for heterogeneous parallel systems. Int J Parallel Program 46(1):62–80

    Article  Google Scholar 

  11. Matsuzaki K, Emoto K (2010) Implementing fusion-equipped parallel skeletons by expression templates. In: Morazán MT, Scholz S (eds) Implementation and application of functional languages. Springer, Berlin, pp 72–89

    Chapter  Google Scholar 

  12. Veldhuizen T (1995) Expression templates. C++ Rep 7(5):26–31

    Google Scholar 

  13. Stahl T, Völter M (2006) Model-driven software development. Wiley, Chichester

    MATH  Google Scholar 

  14. Mernik M, Heering J, Sloane AM (2005) When and how to develop domain-specific languages. ACM Comput Surv 37(4):316–344

    Article  Google Scholar 

  15. Almorsy M, Grundy J (2015) Supporting scientists in re-engineering sequential programs to parallel using model-driven engineering. In: 2015 IEEE/ACM 1st International Workshop on Software Engineering for High Performance Computing in Science, pp 1–8. IEEE

  16. Anderson TA, Liu H, Kuper L, Totoni E, Vitek J, Shpeisman T (2017) Parallelizing Julia with a non-invasive DSL. In: Müller P (ed) 31st European Conference on Object-Oriented Programming (ECOOP ’17), Leibniz International Proceedings in Informatics (LIPIcs), Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, vol 74. Dagstuhl, Germany, pp 4:1–4:29, 2017

  17. Griebler D, Danelutto M, Torquati M, Fernandes LG (2017) SPar: a DSL for high-level and productive stream parallelism. Parallel Process Lett 27(01):1740005

    Article  MathSciNet  Google Scholar 

  18. Sujeeth AK, Brown KJ, Lee H, Rompf T, Chafi H, Odersky M, Olukotun K (2014) Delite: a compiler architecture for performance-oriented embedded domain-specific languages. ACM Trans Embed Comput Syst (TECS) 13(4s):134

    Google Scholar 

  19. Danelutto M, Torquati M, Kilpatrick P, (2016) A DSL based toolchain for design space exploration in structured parallel programming, Procedia Computer Science, vol 80, pp 1519–1530. In: International Conference on Computational Science 2016, ICCS 2016, San Diego, California, USA, 6–8 June 2016

  20. Chamberlain BL, Callahan D, Zima HP (2007) Parallel programmability and the chapel language. Int J High Perform Comput Appl 21(3):291–312

    Article  Google Scholar 

  21. Standard ISO (2015) Programming languages–technical specification for C++ extensions for parallelism, standard ISO/IEC TS 19570:2015. International Organization for Standardization, Geneva

  22. Nugteren C, Corporaal H (2015) Bones: an automatic skeleton-based C-to-CUDA compiler for GPUs. ACM Trans Archit Code Optim (TACO) 11(4):35

    Google Scholar 

  23. Steuwer M, Fensch C, Lindley S, Dubach C (2015) Generating performance portable code using rewrite rules: from high-level functional expressions to high-performance OpenCL code. In: Proceedings of the 20th ACM SIGPLAN International Conference on Functional Programming, ICFP ’15. ACM, New York pp 205–217

  24. Bettini L (2013) Implementing domain-specific languages with Xtext and Xtend. Community experience distilled. Packt Publishing, Birmingham

    Google Scholar 

  25. Wrede F, Rieger C (2018) Musket material repository. https://github.com/wwu-pi/musket-material. Accessed 26 Mar 2019

  26. Eclipse foundation (2019) The eclipse foundation. Xtext documentation. https://eclipse.org/Xtext/documentation/. Accessed 26 Mar 2019

  27. Kuchen H (2004) Optimizing sequences of skeleton calls. In: Lengauer C, Batory D, Consel C, Odersky M (eds) Domain-specific program generation. Lecture notes in computer science, vol 3016. Springer, Berlin, pp 254–274

    Chapter  Google Scholar 

  28. Yoo AB, Jette MA, Grondona M (2003) SLURM: simple linux utility for resource management. In: Feitelson D, Rudolph L, Schwiegelshohn U (eds) Job scheduling strategies for parallel processing. Springer, Berlin, pp 44–60

    Chapter  Google Scholar 

  29. Nethercote N, Seward J (2007) Valgrind: a framework for heavyweight dynamic binary instrumentation. In: Proceedings of the 28th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI ’07, ACM, New York, NY, USA, pp 89–100

  30. Bastos-Filho CJA, de Lima Neto FB, Lins AJCC, Nascimento AIS, Lima MP (2008) A novel search algorithm based on fish school behavior. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC ’08). IEEE, pp 2646–2651

  31. Wrede F, Menezes B, Kuchen H (2018) Fish school search with algorithmic skeletons. Int J Parallel Program 47(2):234–252

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Systems, University of Muenster, European Research Center for Information Systems (ERCIS), Leonardo-Campus 3, 48149, Muenster, Germany

    Fabian Wrede, Christoph Rieger & Herbert Kuchen

Authors
  1. Fabian Wrede
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Christoph Rieger
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Herbert Kuchen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Fabian Wrede.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wrede, F., Rieger, C. & Kuchen, H. Generation of high-performance code based on a domain-specific language for algorithmic skeletons. J Supercomput 76, 5098–5116 (2020). https://doi.org/10.1007/s11227-019-02825-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02825-6

Keywords