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GPU/Graphics: OpenCL, AMD, X.Org

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Graphics/Benchmarks
  • OpenCL 2.2 Sees A Maintenance Update With Document Clarifications, Bug Fixes

    While OpenCL 2.2 support by the major hardware vendors' drivers are sadly languishing, a new maintenance release of this year-old OpenCL standard was issued today to provide various bug fixes as well as documentation clarifications to the specification. Additionally, there is also an update to the OpenCL SPIR-V specification.

  • AMD's Compressonator 3.0 Brings Better Texture Compression

    AMD's GPUOpen team has released Compressonator 3.0, the latest major update to this tools collection for dealing with texture and 3D model compression and optimizations for Linux, macOS, and Windows.

    The Compressonator 3.0 release brings improved texture compression, mesh optimizations, mesh compression support, and other enhancements.

  • AMDKFD In Linux 4.18 Bringing Vega GPU Support

    The AMDKFD kernel driver in the upcoming Linux 4.17 has the long-awaited discrete Radeon GPU support working so it can be used with the ROCm/OpenCL compute user-space, but Vega GPU support wasn't ready for this release. Fortunately, it's ready for Linux 4.18.

  • Four Years After Launch, AMD Kaveri Sees Huge Performance Boost On Linux

    For those making use of AMD Kaveri APUs, the latest Linux graphics stack improvements will now yield much better performance -- up to twice as fast in some instances! Here are some benchmarks with Ubuntu 18.04 on the AMD A10-7870K.

  • X.Org Server 1.21 Opens For Development

    Following the long drawn out and feature-packed X.Org Server 1.20 cycle, the 1.21 window officially opened up today.

    Adam Jackson of Red Hat who continues serving as the X.Org Server release manager today did the post-1.20 version bump to begin allowing new feature material to land for this next cycle.

    For the time being xserver Git is living as version 1.20.99.1 and Adam's latest codename is "Carrot and Ginger Soup."

    No release plans have been posted yet, so it remains to be seen if 1.21 will aim to get back on a six-month release cadence like X.Org had been getting good at delivering on. Or if it will be like 1.20 where it was one and a half years in the making.

More in Tux Machines

Red Hat News/Leftovers

Cloudgizer: An introduction to a new open source web development tool

Cloudgizer is a free open source tool for building web applications. It combines the ease of scripting languages with the performance of C, helping manage the development effort and run-time resources for cloud applications. Cloudgizer works on Red Hat/CentOS Linux with the Apache web server and MariaDB database. It is licensed under Apache License version 2. Read more

James Bottomley on Linux, Containers, and the Leading Edge

It’s no secret that Linux is basically the operating system of containers, and containers are the future of the cloud, says James Bottomley, Distinguished Engineer at IBM Research and Linux kernel developer. Bottomley, who can often be seen at open source events in his signature bow tie, is focused these days on security systems like the Trusted Platform Module and the fundamentals of container technology. Read more

TransmogrifAI From Salesforce

  • Salesforce plans to open-source the technology behind its Einstein machine-learning services
    Salesforce is open-sourcing the method it has developed for using machine-learning techniques at scale — without mixing valuable customer data — in hopes other companies struggling with data science problems can benefit from its work. The company plans to announce Thursday that TransmogrifAI, which is a key part of the Einstein machine-learning services that it believes are the future of its flagship Sales Cloud and related services, will be available for anyone to use in their software-as-a-service applications. Consisting of less than 10 lines of code written on top of the widely used Apache Spark open-source project, it is the result of years of work on training machine-learning models to predict customer behavior without dumping all of that data into a common training ground, said Shubha Nabar, senior director of data science for Salesforce Einstein.
  • Salesforce open-sources TransmogrifAI, the machine learning library that powers Einstein
    Machine learning models — artificial intelligence (AI) that identifies relationships among hundreds, thousands, or even millions of data points — are rarely easy to architect. Data scientists spend weeks and months not only preprocessing the data on which the models are to be trained, but extracting useful features (i.e., the data types) from that data, narrowing down algorithms, and ultimately building (or attempting to build) a system that performs well not just within the confines of a lab, but in the real world.