HIGH PERFORMANCE COMPUTING
Processing the future
ROBERT ROE LOOKS AT RECENT DEVELOPMENTS IN PROCESSOR AND ACCELERATOR TECHNOLOGY
Huang said: ‘Clearly the adoption of GPU
computing is growing and it’s growing at quite a fast rate. The world needs larger computers because there is so much work to be done in reinventing energy, trying to understand the Earth’s core to predict future disasters, or understanding and simulating weather, or understanding how the HIV virus works.’ In an interview with Scientific Computing
Nvidia’s GPU technology Conference (GTC) takes place this week in the US, with the company
launching several new technologies aimed at increasing AI performance – but they are not the only company with new technology that could stir up renewed competition in AI and HPC. As with previous years, the GTC took
place in Silicon Valley with Jensun Huang, CEO, delivering a keynote on the second day of proceedings detailing Nvidia’s latest developments. There were several notable announcements focusing on the development of graphics, medical imaging, autonomy and self-driving technology, but there were two announcements around AI and datacentre products, such as a new 32GB NVIDIA Tesla V100 GPU and GPU interconnect fabric called NVIDIA NVSwitch, which enables communication between 16 Tesla V100 GPUs.
Jensun Huang, CEO and founder of Nvidia, noted that there has been huge development in GPU technology over the past five years. Comparing the Tesla K20 with today’s high-end devices, Huang stressed that GPU development had continued to grow at a faster rate than Moore’s Law.
4 Scientific Computing World April/May 2018
World Ian Buck, vice president, Tesla Data Center Business at NVIDIA, stated that the announcements centred on AI demonstrate the focus the company has to delivering ‘GPUs with more capabilities and more performance’. ‘AI has been at the forefront of changing
the way we interact with devices and companies are transforming their businesses with new AI services,’ said Buck.
‘Just five years ago we had the first AlexNet, which was the first neural network GPU interconnect fabric from Nvidia, NVSwitch
to become famous for image recognition. Today’s modern neural networks for image recognition – such as Inception-v4 from Google – are up to 350 times larger than the original AlexNet. It is larger because it is more intelligent, more accurate and it can recognise more objects,’ added Buck. Nvidia announced two key hardware announcements alongside a host of other improvements such as TensorRT 4 and advancements to its DRIVE system. Key advancements to the NVIDIA platform – which, Huang stated, had been adopted by every major cloud service provider and server maker, were the new GPU and GPU fabric NVSwitch. Buck explained that the Tesla V100 GPU ‘will deliver the same performance in terms of floating point performance, same number of Tesla cores, mechanicals and form factor. We simply have more memory now, and that memory allows us to train some of those larger and more complicated neural networks.’ ‘We will still offer the 16 GB but we will
Jensen Huang
now also offer the 32GB version. Because we kept the form factor identical it has been very easy for our channel partners to accept this product and add it to their product line and they will be making their own announcements this week at GTC,’ he added.
@scwmagazine |
www.scientific-computing.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40
