M. Martinasso, Grzegorz Kwasniewski, S. R. Alam, Thomas Schulthess, Torsten 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.
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BibTeX
@inproceedings{, author={M. Martinasso and Grzegorz Kwasniewski and S. R. Alam and Thomas Schulthess and Torsten 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/}, }
