Abstract
Performance prediction of applications has always been a great challenge, even for homogeneous architectures. However, today’s trend is the design of cluster running in a heterogeneous architecture, which increases the complexity of new strategies to predict the behavior and time spent by an application to run. In this paper we present a strategy that predicts the performance of an application on different architectures and rank then according to the performance that the application can achieve on each architecture. The proposed strategy was able to correctly rank three of four applications tested without overhead implications. Our next step is to extend the metrics in order to increase the accuracy.
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Acknowledgements
The authors would like to thank the Center for Scientific Computing at the São Paulo State University (NCC/UNESP) for the use of the manycore computing resources, partially funded by Intel in the context of the following projects: “Intel Parallel Computing Center”, “Intel Modern Code Partner”, and “Intel/Unesp Center of Excellence in Machine Learning”.
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Stanzani, S. et al. (2019). Towards a Strategy for Performance Prediction on Heterogeneous Architectures. In: Senger, H., et al. High Performance Computing for Computational Science – VECPAR 2018. VECPAR 2018. Lecture Notes in Computer Science(), vol 11333. Springer, Cham. https://doi.org/10.1007/978-3-030-15996-2_18
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DOI: https://doi.org/10.1007/978-3-030-15996-2_18
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