Facebook sets up open-source system for machine learning

Facebook has decided to open source a GPU server designed for machine learning.

Dubbed as Big Sur, it packs eight Nvidia Tesla M40 graphics accelerators, each drawing up to 300W, and is the first system to use the high-end cards targeted at training deep neural networks.

The work is one of many efforts to apply FPGAs and GPUs to accelerate big data centre jobs, increasingly using deep neural networks. More than a year ago, rivals Baidu and Microsoft said they were rolling out FPGAs for a variety of data centre applications including search, claiming GPUs have greater performance but at much higher power consumption and cost.

In February, rivals Microsoft and Google announced breakthroughs in image recognition using deep neural networks. Big Sur marks Facebook's first foray beyond standard server, storage and switch designs. In November, Facebook announced a 100Gbit/s switch.

Details of Big Sur won't be available until an unspecified date when the design is released to the Open Compute Project, originally launched by Facebook. However, the Web giant did say the server uses the project's Open Rack specification. In addition, it has "flexibility to configure between multiple PCI-e topologies."

Facebook's artificial intelligence research team is only working with Nvidia for now. Big Sur "was built with the Nvidia Tesla M40 in mind, but is qualified to support a wide range of PCI-e cards," said a Facebook representative.

Big Sur packs eight Nvidia Tesla M40 accelerators using an OpenCL interface, but is qualified to handle other PCI Express cards. (Source: Facebook)

Nvidia was chosen for a variety of reasons, including the "fact that they have hardware agnostic APIs like Open CL," the Facebook representative said.

Facebook is currently in the final phase of testing Big Sur with plans to use it in production networks next year.

The Web giant has "developed software that can read stories, answer questions about scenes, play games and even learn unspecified tasks through observing some examples. But we realised that truly tackling these problems at scale would require us to design our own systems," engineers said in a blog posted today.

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