Optimizations to drawing performance and blending precision

Just saw this improvement in the new software release. What does that mean?

Don’t know but how could you not like optimization, performance and precision?

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I don’t know about optimization and performance, but I just took a shot with my best macro lens at my bridge’s output and the 0s are way much rounded now and the 1s are straight as an arrow, before every 10 bytes or so the first and third 1 (if any) were somehow hunched to the left… now that’s precision!

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Usually when the release notes say something like this, it’s because the details are fairly technical and uninteresting to the average person. This release included a workaround for an AMD bug that caused diagonal texture artifacts on RX 500 and Vega series GPUs (and possibly other AMD GPUs based on the GCN architecture) on Windows. A performance-improving side-effect of the workaround for this bug was a reduction in the amount of memory bandwidth required of “immediate mode” rendering hardware (ie: most non-mobile GPUs).

As for blending precision, this build included a change to the pixel format used for the intermediate render target that is required for some GPUs/platforms. When we first shipped 1.7, we were rendering to sRGB-encoded targets when required. We discovered that some hardware was particularly bad at blending in this color space, resulting in inaccurate visual output, so we switched to using either a 10 or 16 bit-per-channel linear texture format instead (depending on how capable we think the GPU is). We still use sRGB for cases where it’s needed as part of a workaround for platform or GPU deficiencies on certain Android devices, but loss of blending precision doesn’t seem to be a problem in these cases due to the architecture of tile-based mobile GPUs.

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Allan, that is an impressive rundown on drawing performance and blending precision! Even though I don’t believe it affects my system, it’s another sign of the continuing efforts by Roon and its team to address system issues at a granular level. Kudos to you, @danny @enno and the entire Roon team!

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