Tag Archives: AMD Radeon Vega Frontier Edition

The Radeon Pro Software Crimson ReLive Edition 17.10 Revealed!

AMD just unveiled their next major professional software update for developers, Radeon Pro Software Crimson ReLive Edition 17.10 for Radeon Pro WX 9100 and Radeon Vega Frontier Edition graphics cards, in addition to an  array of new features and improved performance for Radeon ProRender, AMD’s powerful physically-based rendering engine.



The Radeon Pro Software Crimson ReLive Edition 17.10

The Radeon Pro Software Crimson ReLive Edition 17.10 builds upon the massive feature set previewed this past summer, providing professionals with new industry-leading stability, performance and feature improvements, including:

  • Optimization of game development workflows. The new, optional “Driver Options” in Radeon Pro Software 17.10 lets developers swap between “Radeon Pro Software” and the latest “Radeon Software Crimson ReLive Edition for Radeon Pro” driver with Radeon WX 9100 and Vega Frontier Edition cards.
  • Strengthened security against malware. Radeon WX 9100 and Vega Frontier Edition cards have a dedicated AMD Secure Processor built into the silicon, which works in tandem with Radeon Pro Software and Microsoft Device Guard to thwart malicious attacks, while securing graphics bound IP at the same time.
  • Radeon Pro ReLive, refined. Updates include higher maximum recording bitrate (up to 100 Mbps), camera transparency, improved notifications and enhanced audio controls.


Improved Radeon ProRender

AMD is also introducing enhancements to Radeon ProRender, providing more responsive interactive rendering and enabling artists to get their work done sooner. The updated Radeon ProRender plug-in for Blender, provides up to 29 percent faster rendering than the previous version, the 3ds Max plug-in up to 35 percent, the Maya plug-in up to 28 percent, and the SOLIDWORKS add-in up to 14 percent.

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New features include:

  • Shadow Catcher material, which allows developers to collect shadows and reflections from rendered objects for compositing objects to IBL (Image-Based Lighting) environments or backplate images.
  • IES lighting and the ability to load the IES file-format, letting content creators use the exact scattering and fall-offs of real-world lighting
  • Simplified sampling for ProRender makes sampling easier for all plug-ins and add-ins. Developers no longer need to set AA sampling, as now all rendering samples are used for anti-aliasing.

Radeon ProRender now also offers open-source availability for developers. In addition, ProRender is integrated into the new MAXON Cinema 4D Release 19 (R19), with planned integration for Foundry’s Modo in 2018.

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AMD Vega Memory Architecture Q&A With Jeffrey Cheng

At the AMD Computex 2017 Press Conference, AMD President & CEO Dr. Lisa Su announced that AMD will launch the Radeon Vega Frontier Edition on 27 June 2017, and the Radeon RX Vega graphics cards at the end of July 2017. We figured this is a great time to revisit the new AMD Vega memory architecture.

Now, who better to tell us all about it than AMD Senior Fellow Jeffrey Cheng, who built the AMD Vega memory architecture? Check out this exclusive Q&A session from the AMD Tech Summit in Sonoma!

Updated @ 2017-06-11 : We clarified the difference between the AMD Vega’s 64-bit flat address space, and the 512 TB addressable memory. We also added new key points, and time stamps for the key points.

Originally posted @ 2017-02-04

Don’t forget to also check out the following AMD Vega-related articles :


The AMD Vega Memory Architecture

Jeffrey Cheng is an AMD Senior Fellow in the area of memory architecture. The AMD Vega memory architecture refers to how the AMD Vega GPU manages memory utilisation and handles large datasets. It does not deal with the AMD Vega memory hardware design, which includes the High Bandwidth Cache and HBM2 technology.


AMD Vega Memory Architecture Q&A Summary

Here are the key takeaway points from the Q&A session with Jeffrey Cheng :

  • Large amounts of DRAM can be used to handle big datasets, but this is not the best solution because DRAM is costly and consumes lots of power (see 2:54).
  • AMD chose to design a heterogenous memory architecture to support various memory technologies like HBM2 and even non-volatile memory (e.g. Radeon Solid State Graphics) (see 4:40 and 8:13).[adrotate group=”2″]
  • At any given moment, the amount of data processed by the GPU is limited, so it doesn’t make sense to store a large dataset in DRAM. It would be better to cache the data required by the GPU on very fast memory (e.g. HBM2), and intelligently move them according to the GPU’s requirements (see 5:40).
  • The AMD Vega’s heterogenous memory architecture allows for easy integration of future memory technologies like storage-class memory (flash memory that can be accessed in bytes, instead of blocks) (see 8:13).
  • The AMD Vega has a 64-bit flat address space for its shaders (see 12:0812:36 and 18:21), but like NVIDIA, AMD is (very likely) limiting the addressable memory to 49-bits, giving it 512 TB of addressable memory.
  • AMD Vega has full access to the CPU’s 48-bit address space, with additional bits beyond that used to handle its own internal memory, storage and registers (see 12:16). This ties back to the High Bandwidth Cache Controller and heterogenous memory architecture, which allows the use of different memory and storage types.

  • Game developers currently try to manage data and memory usage, often extremely conservatively to support graphics cards with limited amounts of graphics memory (see 16:29).
  • With the introduction of AMD Vega, AMD wants game developers to leave data and memory management to the GPU. Its High Bandwidth Cache Controller and heterogenous memory system will automatically handle it for them (see 17:19).
  • The memory architectural advantages of AMD Vega will initially have little impact on gaming performance (due to the current conservative approach of game developers). This will change when developers hand over data and memory management to the GPU. (see 24:42).[adrotate group=”2″]
  • The improved memory architecture in AMD Vega will mainly benefit AI applications (e.g. deep machine learning) with their large datasets (see 24:52).

Don’t forget to also check out the following AMD Vega-related articles :

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