Intro and NNEF 1.0 Finalization

SIGGRAPH 2018 is a huge computer graphics expo that occurs in a seemingly random host city around North America. (Asia has a sister event, called SIGGRAPH Asia, which likewise shuffles around.) In the last twenty years, the North American SIGGRAPH seems to like Los Angeles, which hosted the event nine times over that period, but Vancouver won out this year. As you would expect, the maintainers of OpenGL and Vulkan are there, and they have a lot to talk about.

In summary:

  • NNEF 1.0 has been finalized and released!
  • The first public demo of OpenXR is available and on the show floor.
  • glTF Texture Transmission Extension is being discussed.
  • OpenCL Ecosystem Roadmap is being discussed.
  • Khronos Educators Program has launched.

I will go through each of these points. Feel free to skip around between the sections that interest you!

Read on to see NNEF or see page 2 for the rest!

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.

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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.

Test Setup

  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
NVIDIA: 387.34

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.

AMD testing was done using the hiptensorflow port from the AMD ROCm GitHub repositories.

For all tests, we are using the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) data set.

Continue reading our look at deep learning performance with the NVIDIA Titan V!!