Nvidia to Present Turing Graphic Architecture

Simultaneously with the announcement of NVIDIA Turing, Jensen Huang presented the first video cards built on the new architecture
15 August 2018   1143

At the SIGGRAPH computer graphics conference in Vancouver, NVIDIA CEO Jensen Huang talked about the company's new development - the GPU architecture of NVIDIA Turing, which supports hybrid rendering. This technology combines ray tracing in real time, machine learning models, simulation and rasterization. The first products based on Turing will appear on the market in the fourth quarter of 2018.

The new development of NVIDIA has received support for ray tracing in real time, which is provided by special processors - RT-cores. They accelerate the processing of light and sound in a voluminous environment up to 10 Gigarays per second, as well as ray tracing calculations 25 times compared to the previous Pascal architecture.

In addition, NVIDIA Turing is equipped with tensor cores to improve the operation of deep neural networks, which perform up to 500 trillion tensor operations per second. This performance is used by the new NVIDIA NGX SDK to integrate graphics, sounds and video into applications with trained neural networks.

The new streaming multiprocessor, equipped with video accelerators based on Turing, adds an integer execution block in parallel to the data channel with a floating point and a new unified cache architecture with twice the bandwidth as compared to Pascal. GPUs equipped with 4608 CUDA cores provide up to 16 trillion integer calculations per second in parallel with floating point operations.

Simultaneously with the announcement of NVIDIA Turing, Jensen Huang presented the first video cards built on the new architecture: Quadro RTX 8000 for $ 10,000, Quadro RTX 6000 for $ 6300 and Quadro RTX 5000 for $ 2300. In addition, the company's CEO has announced the NVIDIA Quadro RTX server for on-demand rendering in large data centers. Among the main characteristics of new products:

  • from 16 GB of memory type GDDR6 from Samsung, which supports the processing of complex graphics resources like movies in 8K format;
  • NVIDIA NVLink technology for connecting two video cards and getting a cluster capable of transmitting data at 100 GB / s, and also having up to 96 GB of memory;
  • native support for VirtualLink technology for unified connection of VR devices via USB Type-C;
  • graphical tools Variable Rate Shading, Multi-View Rendering and VRWorks Audio for advanced VR applications.

During the conference, the company also published training materials on the use of NVIDIA RTX technologies and Microsoft DXR extensions for DirectX for developers wishing to achieve cinematic quality graphics in their gaming projects.

TensorFlow 2.0 to be Released

New major release of the machine learning platform brought a lot of updates and changes, some stuff even got cut
01 October 2019   199

A significant release of the TensorFlow 2.0 machine learning platform is presented, which provides ready-made implementations of various deep machine learning algorithms, a simple programming interface for building models in Python, and a low-level interface for C ++ that allows you to control the construction and execution of computational graphs. The system code is written in C ++ and Python and is distributed under the Apache license.

The platform was originally developed by the Google Brain team and is used in Google services for speech recognition, facial recognition in photographs, determining the similarity of images, filtering spam in Gmail, selecting news in Google News and organizing the translation taking into account the meaning. Distributed machine learning systems can be created on standard equipment, thanks to the built-in support in TensorFlow for spreading computing to multiple CPUs or GPUs.

TensorFlow provides a library of off-the-shelf numerical computation algorithms implemented through data flow graphs. The nodes in such graphs implement mathematical operations or entry / exit points, while the edges of the graph represent multidimensional data arrays (tensors) that flow between the nodes. The nodes can be assigned to computing devices and run asynchronously, simultaneously processing all the suitable tensors at the same time, which allows you to organize the simultaneous operation of nodes in the neural network by analogy with the simultaneous activation of neurons in the brain.

Get more info about the update at official website.