What is Angle?

Overview of conformant OpenGL ES implementation for Windows, Mac and Linux, developed by Google
27 October 2017   1666

What is Angle?

ANGLE stands for Almost Native Graphics Layer Engine. The goal of ANGLE is to allow users of multiple operating systems to seamlessly run WebGL and other OpenGL ES content by translating OpenGL ES API calls to one of the hardware-supported APIs available for that platform. ANGLE currently provides translation from OpenGL ES 2.0 and 3.0 to desktop OpenGL, OpenGL ES, Direct3D 9, and Direct3D 11. Support for translation from OpenGL ES to Vulkan is underway, and future plans include compute shader support (ES 3.1) and MacOS support.

ANGLE v1.0.772 was certified compliant by passing the ES 2.0.3 conformance tests in October 2011. ANGLE also provides an implementation of the EGL 1.4 specification.

ANGLE is used as the default WebGL backend for both Google Chrome and Mozilla Firefox on Windows platforms. Chrome uses ANGLE for all graphics rendering on Windows, including the accelerated Canvas2D implementation and the Native Client sandbox environment.

Portions of the ANGLE shader compiler are used as a shader validator and translator by WebGL implementations across multiple platforms. It is used on Mac OS X, Linux, and in mobile variants of the browsers. Having one shader validator helps to ensure that a consistent set of GLSL ES shaders are accepted across browsers and platforms. The shader translator can be used to translate shaders to other shading languages, and to optionally apply shader modifications to work around bugs or quirks in the native graphics drivers. The translator targets Desktop GLSL, Direct3D HLSL, and even ESSL for native GLES2 platforms.

Links

If you are interested, you can learn more at:

  • Website
  • GitHub

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   172

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.