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Revert "Vulkan: Feedback loop detection and barriers (#7226)" #264

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KeatonTheBot
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@KeatonTheBot KeatonTheBot commented Nov 17, 2024

This reverts commit ca59c3f.

Prevents slowdowns in Paper Mario: The Thousand Year Door, the Xenoblade games, and others. Feel free to test and let me know if you get speedups in games where it would normally slow down.

There are known issues with 7000 series AMD cards, so an AMD-only fix would be ideal.

@github-actions github-actions bot added gpu Affects GPU emulation graphics-backend:vulkan Affects the Vulkan Graphics backend gui Affects the Avalonia UI or translations. labels Nov 17, 2024
@sunshineinabox
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Thought I should add my two cents to this. This is not a good idea, it would be better to use performance and best practice layer to fix this.

@GreemDev
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Thought I should add my two cents to this. This is not a good idea, it would be better to use performance and best practice layer to fix this.

There's a reason why I haven't merged it yet. We're figuring out a way to keep the behavior when it needs it.

@extherian
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extherian commented Nov 22, 2024

Now that the sparse-jit PR has been merged, would you consider rebasing this PR on the latest Canary release? I didn't see any benefits from either sparse jit nor this Vulkan barrier revert before since I have a weak CPU, but I am curious to see if there will be benefits from having both of them at once.

@KeatonTheBot
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Now that the sparse-jit PR has been merged, would you consider rebasing this PR on the latest Canary release? I didn't see any benefits from either sparse jit nor this Vulkan barrier revert before since I have a weak CPU, but I am curious to see if there will be benefits from having both of them at once.

Done.

@extherian
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The City in Xenoblade went from 40 FPS on both the sparse jit build and barrier revert build individually, to 50 - 55 FPS when both are merged together in this pull request. I guess all that we need to finish it is some way of detecting RDNA3 GPUs to enable vulkan barrier detection for them specifically.

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4 participants