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AMD Graphics and Linux

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Graphics/Benchmarks
Linux
  • Radeon EQAA Anti-Aliasing Support Merged To Mesa 18.2

    In addition to the potentially performance-doubling AMD Kaveri fix landing yesterday in Mesa 18.2 Git, also hitting this next version of Mesa is Enhanced Quality Anti-Aliasing (EQAA) support for Radeon GCN graphics processors.

    RadeonSI Gallium3D has wired up its Enhanced Quality Anti-Aliasing support. EQAA aims to deliver better quality over multi-sample anti-aliasing (MSAA) by providing more coverage samples per pixel. EQAA should have only slightly higher performance requirements than MSAA but with significant visual quality benefits.

  • AMD Kaveri Gets A Big Performance Boost With Mesa 18.2 & AMDGPU DRM

    When using the latest Git/development code of Mesa 18.2 on Kaveri APUs you may find up to a 2x increase in performance if you are using the AMDGPU DRM driver rather than the default Radeon DRM driver.

    It turns out the number of render back-ends reported by the kernel driver was wrong for Kaveri: there's two, not one. Both render back-ends for Kaveri should now be enabled when using Mesa 18.2 Git since yesterday, but you need to be using the AMDGPU kernel driver as otherwise with the Radeon DRM kernel driver one of the back-ends will still be disabled.

  • Radeon ROCm 1.8 Compute Stack Released

    Following the slew of recent AMD/Radeon Linux driver updates, the ROCm 1.8.0 release was issued today for the Radeon Open Compute stack.

    ROCm 1.8 can be obtained via the GitHub instructions. Binary packages are provided for Ubuntu 16.04 and CentOS/RHEL 7.4.

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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.