The Technological Roadmap of Parallware and Its Alignment with the OpenPOWER Ecosystem

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10524)


Accelerated, heterogeneous systems are becoming the norm in High Performance Computing (HPC). The challenge is choosing the right parallel programming framework to maximize performance, efficiency and productivity. The design and implementation of benchmark codes is important in many activities carried out at HPC facilities. Well known examples are fair comparison of R+D results, acceptance tests for the procurement of HPC systems, and the creation of miniapps to better understand how to port real applications to current and future supercomputers. As a result of these efforts there is a variety of public benchmark suites available to the HPC community, e.g., Linpack, NAS Parallel Benchmarks (NPB), CORAL benchmarks, and Unified European Application Benchmark Suite. The upcoming next generation of supercomputers is now leading to create new miniapps to evaluate the potential performance of different programming models on mission critical applications, such as the XRayTrace miniapp under development at the Oak Ridge National Laboratory. This paper presents the technological roadmap of Parallware, a new suite of tools for high-productivity HPC education and training, that also facilitates the porting of HPC applications. This roadmap is driven by best practices used by HPC expert developers in the parallel scientific C/C++ codes found in CORAL, NPB, and XRayTrace. The paper reports preliminary results about the parallel design patterns used in such benchmark suites, which define features that need to be supported in upcoming realeases of Parallware tools. The paper also presents performance results using standards OpenMP 4.5 and OpenACC 2.5, compilers GNU and PGI, and devices CPU and GPU from IBM, Intel and NVIDIA.


Hybrid heterogeneous programming models OpenACC OpenMP4 Static analysis tools LLVM Performance portability Use of parallware on minsky 



The authors gratefully acknowledge the access to the HPB PCP Pilot Systems at Julich Supercomputing Centre, which have been partially funded by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (HPB). Also thanks to the Supercomputing Centre of Galicia (CESGA) for providing access to the FinisTerrae supercomputer.


  1. 1.
    Andión, J., Arenaz, M., Rodríguez, G., Touriño, J.: A novel compiler support for automatic parallelization on multicore systems. Parallel Comput. 39(9), 442–460 (2013)CrossRefGoogle Scholar
  2. 2.
    Appentra: Parallware Trainer, April 2017.
  3. 3.
    Arenaz, M., Domínguez, J., Crespo, A.: Democratization of HPC in the oil & gas industry through automatic parallelization with parallware. In: 2015 Rice Oil and Gas HPC Workshop, March 2015Google Scholar
  4. 4.
    Arenaz, M., Touriño, J., Doallo, R.: XARK: an extensible framework for automatic recognition of computational kernels. ACM Trans. Program. Lang. Syst. (TOPLAS) 30(6), 32:1–32:56 (2008)CrossRefGoogle Scholar
  5. 5.
    Asanovic, K., Bodik, R., Catanzaro, B.C., Gebis, J.J., Husbands, P., Keutzer, K., Patterson, D.A., Plishker, W.L., Shalf, J., Williams, S.W., Yelick, K.A.: The landscape of parallel computing research: a view from Berkeley. Technical report, UC Berkeley (2006)Google Scholar
  6. 6.
    Bailey, D., Barszcz, E., Barton, J., Browning, D., Carter, R., Dagum, L., Fatoohi, R., Frederickson, P., Lasinski, T., Schreiber, R., Simon, H., Venkatakrishnan, V., Weeratunga, S.: The NAS parallel benchmarks - summary and preliminary results. In Proceedings of the 1991 ACM/IEEE Conference on Supercomputing, Supercomputing 1991, pp. 158–165. ACM (1991)Google Scholar
  7. 7.
    Blume, W., Doallo, R., Eigenmann, R., Grout, J., Hoeflinger, J., Lawrence, T., Lee, J., Padua, D., Paek, Y., Pottenger, B., Rauchwerger, L., Tu, P.: Parallel programming with Polaris. Computer 29(12), 78–82 (1996)CrossRefGoogle Scholar
  8. 8.
    Dave, C., Bae, H., Min, S.-J., Lee, S., Eigenmann, R., Midkiff, S.: Cetus: a source-to-source compiler infrastructure for multicores. IEEE Micro 42(12), 36–42 (2009)Google Scholar
  9. 9.
    Department of Energy (DoE): CORAL Benchmark Codes (2014).
  10. 10.
    Gómez-Sousa, H., Arenaz, M., Rubiños-López, O., Martínez-Lorenzo, J.: Novel source-to-source compiler approach for the automatic parallelization of codes based on the method of moments. In: Proceedings of the 9th European Conference on Antenas and Propagation, EuCap 2015, April 2015Google Scholar
  11. 11.
    Ishihara, M., Honda, H., Sato, M.: Development and implementation of an interactive parallelization assistance tool for OpenMP: iPat/OMP. IEICE Trans. Inf. Syst. 89–D(2), 399–407 (2006)CrossRefGoogle Scholar
  12. 12.
    Jiang, Q., Lee, Y.C., Zomaya, A., Arenaz, M., Leslie, L.: Optimizing scientific workflows in the cloud: a montage example. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), pp. 517–522. IEEE, December 2014Google Scholar
  13. 13.
    Johnson, S., Evans, E., Jin, H., Ierotheou, C.: The ParaWise expert assistant – widening accessibility to efficient and scalable tool generated OpenMP code. In: Chapman, B.M. (ed.) WOMPAT 2004. LNCS, vol. 3349, pp. 67–82. Springer, Heidelberg (2005). doi: 10.1007/978-3-540-31832-3_7 CrossRefGoogle Scholar
  14. 14.
    Lawrence Livermore National Laboratory: Open-Source Fortran Compiler Technology for LLVM (2015).
  15. 15.
    Liao, S.-W., Diwan, A., Bosch Jr., R.P., Ghuloum, A., Lam, M.S.: SUIF explorer: an interactive and interprocedural parallelizer. In: Proceedings of the 7th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPopp 1999, pp. 37–48. ACM Press, New York (1999)Google Scholar
  16. 16.
    Lobeiras, J., Arenaz, M.: a success case using parallware: the NAS parallel benchmark EP. In: Proceedings of the OpenMPCon Developers Conference (2015)Google Scholar
  17. 17.
    Lobeiras, J., Arenaz, M., Hernández, O.: Experiences in extending parallware to support OpenACC. In: Chandrasekaran, S., Foertter, F. (eds.) Proceedings of the Second Workshop on Accelerator Programming using Directives, WACCPD 2015, Austin, Texas, USA, 15 November 2015, pp. 4:1–4:12. ACM (2015)Google Scholar
  18. 18.
    Lopez, M.G., Larrea, V.V., Joubert, W., Hernandez, O., Haidar, A., Tomov, S., Dongarra, J.: Towards achieving performance portability using directives for accelerators. In: Proceedings of the Third International Workshop on Accelerator Programming Using Directives, WACCPD 2016, pp. 13–24. IEEE Press, Piscataway (2016)Google Scholar
  19. 19.
    Berril, M.: XRayTrace miniapp (2017).
  20. 20.
    Martineau, M., Price, J., McIntosh-Smith, S., Gaudin, W.: Pragmatic performance portability with OpenMP 4.x. In: Maruyama, N., de Supinski, B.R., Wahib, M. (eds.) IWOMP 2016. LNCS, vol. 9903, pp. 253–267. Springer, Cham (2016). doi: 10.1007/978-3-319-45550-1_18 CrossRefGoogle Scholar
  21. 21.
    Mattson, T., Sanders, B., Massingill, B.: Patterns for Parallel Programming, 1st edn. Addison-Wesley Professional (2004)Google Scholar
  22. 22.
    McCool, M., Reinders, J., Robison, A.: Structured Parallel Programming: Patterns for Efficient Computation, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2012)Google Scholar
  23. 23.
    OpenACC Architecture Review Board: The OpenACC Application Programming Interface, Version 2.5, October 2015.
  24. 24.
    OpenMP Architecture Review Board: OpenMP Application Program Interface, Version 4.5, November 2015.
  25. 25.
    Wienke, S., Miller, J., Schulz, M., Müller, M.S.: Development effort estimation in HPC. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016, pp. 10:1–10:12. IEEE Press, Piscataway (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of A Coruna and Appentra SolutionsA CoruñaSpain
  2. 2.Oak Ridge National LaboratoryOak RidgeUSA
  3. 3.Julich Supercomputing CenterJülichGermany

Personalised recommendations