Abstract
Task-based programming models are becoming increasingly important, as they can reduce the synchronization costs of parallel programs on multi-cores. Instances of the same task type in task-based programs consist of the same code, which leads us to the hypothesis that their performance should be regular and thus their execution time should be predictable. We evaluate this hypothesis for a set of 12 task-based programs on 4 different machines: a high-end Intel SandyBridge, an IBM POWER7, an ARM Cortex-A9 and an ARM Cortex-A15. We show, that predicting execution time assuming performance regularity can lead to errors of up to 92%. We identify and analyze three sources of execution time impredictability: input dependence, multiple behaviors per task type and resource sharing. We present two models based on linear interpolation and clustering, reducing the prediction error to less than 12% for input dependent task types and to less than 2% for task types with multiple classes of behavior. All in all, this work invalidates the assumption that performance is always regular across instances of the same task type and quantifies its variability on a wide range of benchmarks and multi-core systems.
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Amarasinghe, S., et al.: ASCR programming challenged for exascale computing. Report of the 2011 Workshop on Exascale Programming Challenges (2011)
Bienia, C., et al.: Benchmarking Modern Multiprocessors. PhD thesis, Princeton University (January 2011)
Browne, S., et al.: A portable programming interface for performance evaluation on modern processors. Journal of High Performance Computing Applications 14(3), 189–204 (2000)
Duran, A., et al.: Barcelona openmp tasks suite: A set of benchmarks targeting the exploitation of task parallelism in openmp. In: ICPP 2009, pp. 124–131 (2009)
Duran, A., et al.: OmpSs: a proposal for programming heterogeneous multi-core architectures. Parallel Processing Letters 21(02), 173–193 (2011)
Genbrugge, D., et al.: Interval Simulation: Raising the Level of Abstraction in Architectural Simulation. In: HPCA 2010, pp. 1–12 (2010)
Halfhill, T.R.: ARM’s 64-Bit Makeover. Microprocessor Report (December 24, 2012)
Karkhanis, T.S., et al.: A first-order superscalar processor model. In: ISCA, Washington, DC, USA, p. 338 (2004)
Kerbyson, D.J., et al.: Predictive Performance and Scalability Modeling of a Large-scale Application. In: Supercomputing 2001, p. 37 (2001)
Nussbaum, S., et al.: Modeling superscalar processors via statistical simulation. In: PACT, pp. 15–24 (2001)
Olivier, S.L., et al.: Characterizing and mitigating work time inflation in task parallel programs. In: 2012 International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 1–12. IEEE (2012)
Rajovic, N., et al.: Experiences with Mobile Processors for Energy Efficient HPC. In: DATE 2013, pp. 464–468 (2013)
Rajovic, N., et al.: Supercomputing with Commodity CPUs: Are Mobile SoCs Ready for HPC? In: SC 2013 (2013)
Rico, A., et al.: Available Task-level Parallelism on the Cell BE. Scientific Programming 17(1-2), 59–76 (2009)
Schmidl, D., Philippen, P., Lorenz, D., Rössel, C., Geimer, M., an Mey, D., Mohr, B., Wolf, F.: Performance analysis techniques for task-based openMP applications. In: Chapman, B.M., Massaioli, F., Müller, M.S., Rorro, M. (eds.) IWOMP 2012. LNCS, vol. 7312, pp. 196–209. Springer, Heidelberg (2012)
Snavely, A., et al.: A Framework for Performance Modeling and Prediction. In: Supercomputing 2002, pp. 1–17 (2002)
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Grass, T., Rico, A., Casas, M., Moreto, M., Ramirez, A. (2014). Evaluating Execution Time Predictability of Task-Based Programs on Multi-Core Processors. In: Lopes, L., et al. Euro-Par 2014: Parallel Processing Workshops. Euro-Par 2014. Lecture Notes in Computer Science, vol 8806. Springer, Cham. https://doi.org/10.1007/978-3-319-14313-2_19
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DOI: https://doi.org/10.1007/978-3-319-14313-2_19
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