This week’s GPU Technology Conference, being held at the San Jose Convention Center, is a fascinating insight into the power of artificial intelligence, and how it is being harnessed by software developers and engineers to break through old barriers in a bevy of industries, from autonomous vehicles to virtual reality to medicine.
The conference, which has drawn more than 8,000 developers, researchers and executives this year, has been a bevy of activity, with attendees clustered around several key areas on the exhibit floor, in particular those focused on self-driving vehicles and virtual reality.
The opening keynote, by NVIDIA’s CEO Jensen Huang, was packed, and Huang offered his insights on a wide variety of topics, in addition to several big product announcements from the company.
黄谈到了增压计算时代，近年来，CPU增长降低了摩尔法律，但GPU加速计算的增长速度比他所谓的“法律奇迹”更快。
他说：“正在进行一项新法律，一项增压法律，我认为这是计算的未来。”
He showed examples of how AI is impacting the rendering of images in video games and film—and noted how computing power is still lagging behind on major science initiatives.
According to Huang, a Caltech-Oak Ridge National Laboratory project on reinventing the lithium-ion battery took 7 days on the Titan supercomputer. A Princeton-Oak Ridge study on mapping the Earth’s core took 17 days on Titan. An Illinois/NCSA study looking into the structure of HIV took 16 days on Blue Water. And an ETH Zürich/MeteoSwiss look at cloud-resolving climate simulation took an amazing 840 days on Piz Daint. Soon, this technology will reduce those times to 1 day, he said, thanks to GPU-accelerated computing.
“有严重的开创性科学要做。我们将构建一台Exascale计算机，这些模拟时间将被压缩到一天……科学需要增压计算机。”
Among the corporate news releases that Huang discussed was a series of advances to the company’s deep learning computing platform, which delivers a 10x performance boost on deep learning workloads compared with the previous generation six months ago.
Advancements to the NVIDIA platform include a 2x memory boost to NVIDIA Tesla V100, the most powerful datacenter GPU, and a new GPU interconnect fabric called NVIDIA NVSwitch, which enables up to 16 Tesla V100 GPUs to simultaneously communicate at a record speed of 2.4 terabytes per second.
NVIDIA还引入了DGX-2，它说这是第一台能够提供两种PETAFLOPS计算能力的单个服务器。DGX-2具有300台服务器的深度学习处理能力，占据了15个数据中心空间，同时较小60倍，功率更高。
“The extraordinary advances of deep learning only hint at what is still to come,” said Huang. “Many of these advances stand on NVIDIA’s deep learning platform, which has quickly become the world’s standard. We are dramatically enhancing our platform’s performance at a pace far exceeding Moore’s law, enabling breakthroughs that will help revolutionize healthcare, transportation, science exploration and countless other areas.”
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