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Tracing and Profiling Machine Learning Dataflow Applications on GPU

  • Pierre ZinsEmail author
  • Michel Dagenais
Article

Abstract

In this paper, we propose a profiling and tracing method for dataflow applications with GPU acceleration. Dataflow models can be represented by graphs and are widely used in many domains like signal processing or machine learning. Within the graph, the data flows along the edges, and the nodes correspond to the computing units that process the data. To accelerate the execution, some co-processing units, like GPUs, are often used for computing intensive nodes. The work in this paper aims at providing useful information about the execution of the dataflow graph on the available hardware, in order to understand and possibly improve the performance. The collected traces include low-level information about the CPU, from the Linux Kernel (system calls), as well as mid-level and high-level information respectively about intermediate libraries like CUDA, HIP or HSA, and the dataflow model. This is followed by post-mortem analysis and visualization steps in order to enhance the trace and show useful information to the user. To demonstrate the effectiveness of the method, it was evaluated for TensorFlow, a well-known machine learning library that uses a dataflow computational graph to represent the algorithms. We present a few examples of machine learning applications that can be optimized with the help of the information provided by our proposed method. For example, we reduce the execution time of a face recognition application by a factor of 5X. We suggest a better placement of the computation nodes on the available hardware components for a distributed application. Finally, we also enhance the memory management of an application to speed up the execution.

Keywords

Performance analysis Dataflow Machine learning GPU Tracing Profiling 

Notes

Acknowledgements

The financial support of Ericsson, Ciena, Google, EfficiOS, Prompt and the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged. We are also grateful to Advanced Micro Devices (AMD) for providing the hardware and software that made this research possible.

References

  1. 1.
    Moore, G.E.: Cramming more components onto integrated circuits. Electronics 38(8), 114 (1965)Google Scholar
  2. 2.
    Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: Gpu computing. Proc. IEEE 96(5), 879–899 (2008)CrossRefGoogle Scholar
  3. 3.
    Boutellier, J., Wu, J., Huttunen, H., Bhattacharyya, S.S.: PRUNE: dynamic and decidable dataflow for signal processing on heterogeneous platforms (2018). CoRR arXiv:1802.06625
  4. 4.
    Boutellier, J., Nyländen, T.: Design flow for GPU and multicore execution of dynamic dataflow programs. J. Signal Process. Syst. 89(3), 469–478 (2017)CrossRefGoogle Scholar
  5. 5.
    Bezati, E., Mattavelli, M., Raulet, M.: Rvc-cal dataflow implementations of mpeg avc/h.264 cabac decoding. In: 2010 Conference on Design and Architectures for Signal and Image Processing (DASIP), pp. 207–213 (2010)Google Scholar
  6. 6.
    Hentati, M., Aoudni, Y., Nezan, J.F., Abid, M.: A hierarchical implementation of hadamard transform using rvc-cal dataflow programming and dynamic partial reconfiguration. In: Proceedings of the 2012 Conference on Design and Architectures for Signal and Image Processing, pp. 1–7 (2012)Google Scholar
  7. 7.
    Blattner, T., Keyrouz, W., Halem, M., Brady, M., Bhattacharyya, S.S.: A hybrid task graph scheduler for high performance image processing workflows. In: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 634–637 (2015)Google Scholar
  8. 8.
    Bourrasset, C., Maggiani, L., Srot, J., Berry, F.: Dataflow object detection system for fpga-based smart camera. IET Circuits Devices Syst. 10(4), 280–291 (2016)CrossRefGoogle Scholar
  9. 9.
    Halbwachs, N., Caspi, P., Raymond, P., Pilaud, D.: The synchronous data flow programming language lustre. Proc. IEEE 79(9), 1305–1320 (1991)CrossRefGoogle Scholar
  10. 10.
    Caspi, P., Pilaud, D., Halbwachs, N., Plaice, J.A.: LUSTRE: a declarative language for real-time programming. In: Proceedings of the 14th ACM SIGACT-SIGPLAN Symposium on Principles of Programming Languages, POPL ’87, pp. 178–188. ACM, New York (1987)Google Scholar
  11. 11.
    Wadge, W.W., Ashcroft, E.A.: LUCID, the Dataflow Programming Language. Academic Press Professional, Inc., San Diego (1985)zbMATHGoogle Scholar
  12. 12.
    Eker, J., Janneck, J.W.: CAL language report: specification of the CAL actor language (2003)Google Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3–6, 2012, Lake Tahoe, Nevada, USA, pp. 1106–1114 (2012)Google Scholar
  14. 14.
    Theano Development Team.: Theano: a python framework for fast computation of mathematical expressions (2016). CoRR arXiv:1605.02688
  15. 15.
    Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010. Oral Presentation (2010)Google Scholar
  16. 16.
    Abadi, M., Isard, M., Murray, D.G.: A computational model for tensorflow (an introduction) (2017)Google Scholar
  17. 17.
    Abadi, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems (2016). CoRR arXiv:1603.04467
  18. 18.
    Abadi, M., et al.: Tensorflow: a system for large-scale machine learning (2016). CoRR arXiv:1605.08695
  19. 19.
    David, G.: Unified kernel/user-space efficient linux tracing architecture. Master’s thesis, cole Polytechnique de Montral. Retrieved from https://publications.polymtl.ca/842/ (2012)
  20. 20.
    Fournier, P.-M., Desnoyers, M., Dagenais, M.R.: Combined tracing of the kernel and applications with LTTng. In: Proceedings of the 2009 Linux Symposium (2009)Google Scholar
  21. 21.
    Hesik, C.: CodeXL 2.6 is released!. https://gpuopen.com/codexl-2-6-released/ (2018)
  22. 22.
    NVIDIA Developer Tools Overview. https://developer.nvidia.com/tools-overview (2018)
  23. 23.
    Get Started with Intel Graphics Performance Analyzers (Intel GPA). https://software.intel.com/en-us/gpa_getting_started (2018)
  24. 24.
  25. 25.
    Gregg, B.: Strace wow much syscall (2014)Google Scholar
  26. 26.
    Gregg, B.: Perf Examples (2014)Google Scholar
  27. 27.
    Rostedt, S.: Finding Origins of Latencies Using Ftrace (2009)Google Scholar
  28. 28.
    Gregg, B.: Flame Graphs (2011)Google Scholar
  29. 29.
    Desnoyers, M., Dagenais, M.R.: The LTTng tracer: a low impact performance and behavior monitor for GNU/Linux. OLS (Ottawa Linux Symposium) (2006)Google Scholar
  30. 30.
    NVIDIA Nsight Systems User Guide. https://docs.nvidia.com/nsight-systems/index.html (2018)
  31. 31.
  32. 32.
  33. 33.
    Ponweiser, T.: Profiling and tracing tools for performance analysis of large scale applications (2017)Google Scholar
  34. 34.
    Pillet, V., Labarta, J., Cortes, T., Girona, S., and Departament D’arquitectura De Computadors.: Paraver: a tool to visualize and analyze parallel code. Technical report, In WoTUG-18 (1995)Google Scholar
  35. 35.
    Canale, M., Casale-Brunet, S., Bezati, E., Mattavelli, M., Janneck, J., Casale-Brunet, S., Bezati, E., Mattavelli, M., Marco Mattavelli@epfl Ch., Janneck, J.: Dataflow programs analysis and optimization using model predictive control techniques two examples of bounded buffer scheduling: deadlock avoidance and deadlock recovery strategies. J. Signal Process. Syst. 84, 371–381 (2016)Google Scholar
  36. 36.
    Janneck, J.W., Miller, I.D., Parlour, D.B.: Profiling dataflow programs. In: 2008 IEEE International Conference on Multimedia and Expo, ICME 2008-Proceedings, pp. 1065–1068 (2008)Google Scholar
  37. 37.
    Brunet, S.C., Mattavelli, M., Janneck, J.W.: Profiling of dataflow programs using post mortem causation traces. In: IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation, pp. 220–225 (2012)Google Scholar
  38. 38.
    Mysore, S., Mazloom, B., Agrawal, B., Sherwood, T.: Understanding and visualizing full systems with data flow tomography (2008)Google Scholar
  39. 39.
    Osmari, D.K., Vo, H.T., Silva, C.T., Comba, J.L.D., Lins, L.: Visualization and analysis of parallel dataflow execution with smart traces. In: Brazilian Symposium of Computer Graphic and Image Processing, pp. 165–172 (2014)Google Scholar
  40. 40.
    Stoner, G.: ROCm: platform for a new era of heterogeneous in HPC and ultrascale computing (2016)Google Scholar
  41. 41.
    Rogers, P.: HSA Overview, pp. 7–18 (2015)Google Scholar
  42. 42.
    Goli, M., Iwanski, L., Richards, A.: Accelerated machine learning using TensorFlow and SYCL on OpenCL Devices. In: Proceedings of the 5th International Workshop on OpenCL, IWOCL 2017, pp. 8:1–8:4. ACM, New York (2017)Google Scholar
  43. 43.
    Keryell, R., Reyes, R., Howes, L.: Khronos sycl for opencl: a tutorial. In: Proceedings of the 3rd International Workshop on OpenCL, IWOCL ’15, pp. 24:1–24:1. ACM, New York (2015)Google Scholar
  44. 44.
    Lea, D.: A Memory Allocator (1996)Google Scholar
  45. 45.
    Paul, M.: Traage logiciel d’applications utilisant un processeur graphique. Master’s thesis, cole Polytechnique de Montral. Retrieved from https://publications.polymtl.ca/2838/ (2017)
  46. 46.
    Couturier, D., Dagenais, M.R.: LTTng CLUST: a system-wide unified CPU and GPU tracing tool for OpenCL applications. Adv. Softw. Eng. 2015, 2:2–2:2 (2015)CrossRefGoogle Scholar
  47. 47.
    Poirier, B., Roy, R., Dagenais, M.: Accurate offline synchronization of distributed traces using kernel-level events. SIGOPS Oper. Syst. Rev. 44(3), 75–87 (2010)CrossRefGoogle Scholar
  48. 48.
    Jabbarifar, M.: On line trace synchronization for large scale distributed systems. PhD thesis, École Polytechnique de Montréal (2013)Google Scholar
  49. 49.
    Wininger, F., Ezzati-Jivan, N., Dagenais, M.R.: A declarative framework for stateful analysis of execution traces. Softw. Qual. J. 25, 201–229 (2016)CrossRefGoogle Scholar
  50. 50.
    Kouame, K., Ezzati-Jivan, N., Dagenais, M.R.: A flexible data-driven approach for execution trace filtering. In: 2015 IEEE International Congress on Big Data, pp. 698–703 (2015)Google Scholar
  51. 51.
    Moindrot, O.: Triplet Loss and Online Triplet Mining in TensorFlow (2018)Google Scholar
  52. 52.
    Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.A.: Striving for simplicity: the all convolutional net (2014). CoRR arXiv:1412.6806
  53. 53.
    Mayer, R., Mayer, C., Laich, L.: The TensorFlow Partitioning and Scheduling Problem: It’s the Critical Path! pp. 1–6 (2017)Google Scholar
  54. 54.
    Mirhoseini, A., Pham, H., Le, Q.V., Steiner, B., Larsen, R., Zhou, Y., Kumar, N., Norouzi, M., Bengio, S., Dean, J.: Device placement optimization with reinforcement learning. In: Icml (2017)Google Scholar
  55. 55.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Ecole Polytechnique MontrealMontrealCanada

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