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The Vulkan/SPIR-V memory model was built on the foundation of the C++ memory model, but ended up diverging in a number of places.
A lot of how GPU programming models work across modern graphics APIs has evolved through years of development, reflecting the markets that those APIs have targeted. Naturally, the Vulkan/SPIR-V memory model has made several decisions that reflect this. We added several new facets to the model, including scopes, storage classes, and memory availability and visibility operations to name some of the more prominent ones.
However, It is not a strict superset either, and there are a few places where some features have been omitted for similar reasons. For example, sequential consistency is not supported, and forward progress guarantees are limited.
This post aims to give a high-level overview of the differences, explaining what the differences are, why they are different, and how (if at all) C++ concepts can map to the Vulkan/SPIR-V memory model. It is aimed primarily at people already familiar with the C++ memory model who either want to get some insight into what the differences are or those who are curious about why we took the direction we did.
Khronos has released a provisional Vulkan Memory Model Specification that includes extensions for Vulkan, SPIR-V, and GLSL and gives Vulkan developers additional control over how their shaders synchronize access to should cooperate safely over memory operations in a parallel execution environment. In tandem with the extension specification, Khronos has released memory model extension conformance tests to enable implementers to do early tests on their shader compilers to ensure that the specified memory synchronization is implemented correctly. The memory model will have an Alloy description of the extension functionality to enable formal modeling and experimentation.
Virtual reality and augmented reality have great potential for entertainment, training and education, and other industries, but are currently being held back by industry fragmentation. The Khronos Group is addressing this by creating the OpenXR API, and shares details of its creation and considerations, as well as the first demo of the API at SIGGRAPH 2018.
The demand for 3D content is growing quickly across markets. New formats, applications, and tools are being developed to keep up with the demand . TurboSquid has been eagerly watching the development of the glTF 2.0 specification and has now added full support for the format for its StemCell initiative, which standardizes how 3D models are built and makes buying a 3D model as easy as buying a stock photo.
In April, Khronos introduced the Safety Critical Advisory Forum was created in response to developers’ growing concerns and demands of functional safety standards on hardware and software. The advice and support that the forum provides to Khronos Working Groups directly contributes to the creation of SC APIs. Members and non-members can contribute in the forum, this post outlines the benefits of participation.
The Khronos™ OpenCL™ working group has today released a maintenance update to OpenCL 2.2 to consolidate numerous bug fixes and clarifications to make the specification more precisely defined and more easily understood. In this maintenance release, the OpenCL C specification has now also been put into open source.
Learn why one company chose the Khronos industry file format, glTF to create a searchable platform of interactive 3D content and find out how Sketchfab unlocked more than 150,000 glTF assets available for free download under Creative Commons licenses.
Facebook’s recent adoption of glTF 2.0 enables its users to place and see 3D content in their News Feeds, underscoring the social media platform’s plan to enable users to bring 3D objects and assets with them across AR, VR, mobile, and web experiences — using open standards. Facebook’s prominent support for glTF is already stimulating the creation of innovative tools to generate glTF content, such as Sony 3D Creator, Oculus Medium, and Foundry Modo.
As technology around artificial intelligence and autonomous driving advances, the need for safety critical systems also grows. Khronos Group has created a Safety Critical Advisory Forum and invites functional safety experts to join the group to advise and help develop open standards for the safety critical domain. By contributing your expertise to Khronos’ safety-critical work, you will enable Khronos and its API designers to deliver safety-critical APIs for a safer autonomous world.
Subgroups are an important new feature in Vulkan 1.1 because they enable highly-efficient sharing and manipulation of data between multiple tasks running in parallel on a GPU. In this tutorial, we will cover how to use the new subgroup functionality.
Standards make life easier, and we depend on them for more than we might realize — from knowing exactly how to drive any car, to knowing how to get hot or cold water from a faucet. When they fail us, the outcome can be comical or disastrous: non-standard plumbing, for instance, can result in an unexpected cold shower or a nasty scald. We need standards, and the entire computing world is built on them.
NNEF and ONNX are two similar open formats to represent and interchange neural networks among deep learning frameworks and inference engines. At the core, both formats are based on a collection of often used operations from which networks can be built. Because of the similar goals of ONNX and NNEF, we often get asked for insights into what the differences are between the two. Although Khronos has not been involved in the detailed design principles of ONNX, in this post we explain how we see the differences according to our understanding of the two projects. We welcome constructive discussion as the industry explores the need for neural network exchange and hope this post may be a constructive start to that conversation.
Authoring content for a new file format can be exciting, liberating, and at the same time scary. To be the most efficient and avoid frustration, it helps to understand the format's requirements. To help achieve that, I am going to walk through several paths for authoring content in the glTF format as well as outline specific settings to maximize your success. I will touch on both free and commercial software packages to ensure everyone has a path into glTF, but first let's outline a few important concepts.
Every year in December, millions of people get in the holiday spirit with NORAD Tracks Santa, the website that lets you track Santa’s magical midnight voyage through the sky on Christmas Eve. Part of what makes the NORAD Tracks Santa website possible are Khronos standards WebGL and glTF. Today, over 22 million people follow Santa’s journey on a 3D map built with Cesium. Before gITF and WebGL, Mr. Claus’s delivery route was much harder to trace.
Previous blog posts have stressed that the deployment process of neural networks to inference engines is becoming fragmented. An accepted standard can facilitate the industrial use of artificial intelligence by creating mutual compatibility between deep-learning frameworks and inference engines. The Neural Network Exchange Format (NNEF) is the Khronos Group’s solution to this problem.