Archive for the ‘infrastructure’ Category

In Ethernet Summit 2014, Alan Weckel of the Dell’Oro Group showed a very interesting chart on projections for server adoption. Due to copyright issues, I’d summarize the info as follows:

In 2013, cloud and server providers account for ~20% server unit shipments, by 2018, this group of customers is forecasted to account for up to 50% of server unit shipments. If this trend continues, the there would no growth to server shipments to enterprise customers.

Since servers account for part of the data center, the implication is that both networking and storage gear would move this way as well. Cloud and SP are significantly changing the data center equipment market.

Another interest point, 2 players dominate in the cloud, Google and Amazon, while Facebook could be an up-and-comer. These players design their own data center equipment and directly work with ODMs to manufacture their own equipment. It would take some hard maneuvers for an IT equipment vendor to get into these accounts. HP is trying such as maneuver: creating low-cost entry servers in partnership in Foxconn. Time will tell whether this would work.


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This is the claim by Nvidia CEO Jen-Hsun Huang, that 3 Nvidia Titan Z CUDA based GPU cards could offer the same performance in running deep learning neural nets as done in the Google Brian project using Intel processors.

  • 1/150 the acquisition cost reduction
  • 1/150 heat consumption

If this could be done in general for deep learning type problems, we could have many more machines to do machine learning on the explosion of data. At the same time, to use the CUDA cores, software programmers would need to learn program this hardware and / or use OpenCL. The cost savings could warrant pushing over the learning curve.

This paper is referenced: “Deep Learning with COTS HPC Systems” by A. Coates, B. Huval, T. Wang, D. Wu, A. Ng, B. Catanzaro, published on ICML 2013

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