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mesa3d
mesa
HISTORICAL RECORD2017–2023
mesa3d / mesa · Not publicly available

mesa

Discover this repository through its Hacker News conversations.

8Distinct people
8Mentions
0Explicit recommenders
REPOSITORY AT A GLANCE

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What it is

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What people are saying

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This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:59:58 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year8 dated mentions

Observed mentions: 2017-08-26 09:33:15 UTC to 2023-02-03 04:08:05 UTC.

01232017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC303
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC303
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101

What Hacker News says

3 sources
> Closed source GPU drivers are not ordinary drivers. They have a lot of quirks and workarounds for programs, especially games. The quirks part is a bit of a meme. Mesa contains a few application-specific overrides here and there: https://github.com/mesa3d/mesa/blob/c62996796cc46f1a1406f365..., https://github.com/mesa3d/mesa/blob/a678ec9b8c057311ed7e9697.... The reason Linux drivers, including the one from Nvidia, have fewer quirks is simply that Linux has fewer 3d applications overall. Those quirks also reside in userspace driver parts and should not affect system stability. > When you try to port these drivers as-is to another platforms, things get awry. Again, quirks are a property of a relatively high-level userspace part of the driver. There is nothing hazardous about porting them.
In AMD vs. Intel vs. Nvidia: How Users Are Switching
$1.4 billion. Wow. That's a lot of dough. I feel like 202x might be another decade where no new chip making companies emerge. I had great hopes for Nuvia becoming one of them. My first most primitive reaction is that this is like another PA Semi[1]: great talent & new prospects, fresh air, vanishing into one of the super-massive existing players. Qualcomm does some open source work, but most of the support comes from outside engineers reverse engineering their products or otherwise doing the work[2]. The Freedreno graphics driver for example allows their gpu to be used (OpenGL, Vulkan drivers), & has been a huge effort. That work mostly comes from Google engineers like Rob Clark (who started Freedreno iirc), Eric Anholt, Igalia engineers like Danil, & countless others[3]. In the wifi-router world, where Qualcomm also has a huge presence,
Read the rest
we rely on hunting around for open-source code drops of what usually seems like pretty old but better than we've got code, such as this recent find[4], which is hopefully going to help us catch a couple years up in support level to what Qualcomm is doing today. Nuvia explicitly stated they wanted to chase hyperscaler market. And I think a lot of this was positioning. That hyper-scalers wanted good tech, would use good tech. Where-as a lot of the world, they want what they have but better, a lot of legacy concerns, hand holding. Nuvia wanted to build a new platform, a good, well supported platform, with next-gen technologies. We were all expecting open-source perhaps Rust-based firmwares & management systems, pursuant to latest OpenCompute Project specs &c. They were clear about targetting hyper-scalers, & big chips, but somehow the hope always was that they'd help reform & reshape computing & what was available to everyone. They certainly talked the good game, sounded real good on Twitter. Ah well. [1] https://venturebeat.com/2012/09/18/more-details-shake-loose-... [2] https://www.phoronix.com/scan.php?page=search&q=Qualcomm [3] https://github.com/mesa3d/mesa/commits/master/src/freedreno [4] https://forum.openwrt.org/t/adding-openwrt-support-for-xiaom...
In Qualcomm to Acquire Nuvia: A CPU Magnitude Shift
While I do not believe Broadcom has released any documentation on the 3D hardware in the Raspberry Pi 4, you can figure out a fair amount by looking at the Mesa driver. In particular see the shader processor instruction packing code (https://github.com/mesa3d/mesa/blob/master/src/broadcom/qpu/...). For vertex arrays, while in theory the driver could load vec2/vec4 F16 attributes into the vertex shader as-is, AFAICT the Mesa driver does not attempt to do this; they get converted to F32 as they are loaded from main memory. There would be downsides to getting rid of this conversion... in particular the hardware does not support F32->F16 conversions when loading attributes, so the driver would have to recompile the shader if you switched the vertex arrays to F32 (or perhaps more likely recompile the shader when it realises you are using F16 v
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ertex arrays rather than F32). In any case, the hardware does support zero overhead F16->F32 conversions when loading attributes, so you should not see a performance drop when switching vertex arrays to F16! The performance should go up slightly as less data needs to be loaded from main memory. If you're seeing an 80% performance drop something has gone terribly wrong!
In Dissecting the Apple M1 GPU, part I

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