Subject: General Tech, Graphics Cards, Networking, Shows and Expos | March 19, 2019 - 06:16 PM | Jeremy Hellstrom
Tagged: nvidia, t4, amazon, microsoft, NGC, Mellanox, CUDA-X, GTC, jen-hsun huang, DRIVE Constellation, ai
As part of their long list of announcements yesterday, NVIDIA revealed they are partnering with Cisco, Dell EMC, Fujitsu, Hewlett Packard Enterprise, Inspur, Lenovo and Sugon to provide servers powered by T4 Tensor Core GPUs, optimized to run their CUDA-X AI accelerators.
Those T4 GPUs have been on the market for a while but this marks the first major success for NVIDIA in the server room, with models available for purchase from those aforementioned companies. Those who prefer other people's servers can also benefit from these new products, with Amazon and Microsoft offering Cloud based solutions. Setting yourself up to run NVIDIA's NGC software may save a lot of money down the road, the cards sip a mere 70W of power which is rather more attractive than the consumption of a gaggle of Tesla V100s. One might be guilty of suspecting this offers an explanation for their recent acquisition of Mellanox.
NGC software offers more than just a platform to run optimizations on, it also offers a range of templates to start off with from classification, and object detection, through sentiment analysis and most other basic starting points for training a machine. Customers will also be able to upload their own models to share internally or, if in the mood, externally with other users and companies. It supports existing products such as TensorFlow and PyTorch but also offers access to CUDA-X AI, which as the name suggests takes advantage of the base design of the T4 GPU to reduce the amount of time waiting for results and letting users advance designs quickly.
If you are curious exactly what particular implementations of everyone's favourite buzzword might be, NVIDIA's DRIVE Constellation is a example after JoshTekk's own heart. Literally an way to create open, scalable simulation for large fleets of self-driving cars to train them ... for good one hopes. Currently the Toyota Research Institute-Advanced Development utilizes these products in the development of their next self-driving fleet, and NVIDIA obviously hopes others will follow suit.
There is not much to see from the perspective of a gamer in the short term, but considering NVIDIA's work at shifting the horsepower from the silicon you own to their own Cloud this will certainly impact the future of gaming from both a hardware and gameplay perspective. GPUs as a Service may not be the future many of us want but this suggests it could be possible, not to mention the dirty tricks enemy AIs will be able to pull with this processing power behind them.
One might even dream that escort missions could become less of a traumatic experience!
How deep is your learning?
Recently, we've had some hands-on time with NVIDIA's new TITAN V graphics card. Equipped with the GV100 GPU, the TITAN V has shown us some impressive results in both gaming and GPGPU compute workloads.
However, one of the most interesting areas that NVIDIA has been touting for GV100 has been deep learning. With a 1.33x increase in single-precision FP32 compute over the Titan Xp, and the addition of specialized Tensor Cores for deep learning, the TITAN V is well positioned for deep learning workflows.
In mathematics, a tensor is a multi-dimensional array of numerical values with respect to a given basis. While we won't go deep into the math behind it, Tensors are a crucial data structure for deep learning applications.
NVIDIA's Tensor Cores aim to accelerate Tensor-based math by utilizing half-precision FP16 math in order to process both dimensions of a Tensor at the same time. The GV100 GPU contains 640 of these Tensor Cores to accelerate FP16 neural network training.
It's worth noting that these are not the first Tensor operation-specific hardware, with others such as Google developing hardware for these specific functions.
|PC Perspective Deep Learning Testbed|
|Processor||AMD Ryzen Threadripper 1920X|
|Motherboard||GIGABYTE X399 AORUS Gaming 7|
|Memory||64GB Corsair Vengeance RGB DDR4-3000|
|Storage||Samsung SSD 960 Pro 2TB|
|Power Supply||Corsair AX1500i 1500 watt|
|OS||Ubuntu 16.04.3 LTS|
|Drivers||AMD: AMD GPU Pro 17.50
For our NVIDIA testing, we used the NVIDIA GPU Cloud 17.12 Docker containers for both TensorFlow and Caffe2 inside of our Ubuntu 16.04.3 host operating system.
For all tests, we are using the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) data set.