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M. Martinasso, G. Kwasniewski, S. R. Alam, T. C. Shulthess, T. Hoefler:
	A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator Servers (In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC16), presented in Salt Lake City, Utah, pages 63:1--63:11, IEEE Press, ISBN: 978-1-4673-8815-3, Nov. 2016) Abstract MeteoSwiss, the Swiss national weather forecast institute, has selected densely populated accelerator servers are their primary system to compute weather forecast simulation. Servers with multiple accelerator devices that are primarily connected by a PCI-Express (PCIe) network achieve a significantly higher energy efficiency. Memory transfers between accelerators in such a system are subjected to PCIe arbitration policies. In this paper, we study the impact of PCIe topology and develop a congestion-aware performance model for PCIe communication. We present an algorithm for computing penalty coefficients of every communication in a congestion graph that characterises the dynamic usage of network resources by an application. Our validation results on two different topologies of 8 GPU devices demonstrate that our model achieves an accuracy of over 97% within the PCIe network. We use the model on a weather forecast application to identify the best algorithm for its communication patterns among GPUs. Documents download article: download slides: Recorded talk (best effort)
	BibTeX
@inproceedings{,   author={M. Martinasso and G. Kwasniewski and S. R. Alam and T. C. Shulthess and T. Hoefler},   title={{A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator Servers}},   year={2016},   month={Nov.},   pages={63:1--63:11},   booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC16)},   location={Salt Lake City, Utah},   publisher={IEEE Press},   isbn={978-1-4673-8815-3},   source={http://www.unixer.de/~htor/publications/}, }