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uxlfoundation
oneDNN
C++2019–2024
uxlfoundation / oneDNN

oneDNN

Open-source library of optimized deep learning operations (matmul, convolution, attention) for CPUs (x64, AArch64, RISC-V) and Intel GPUs. Used by PyTorch, TensorFlow, OpenVINO, and ONNX Runtime.

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9Distinct people
9Mentions
0Explicit recommenders
4.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Open-source library of optimized deep learning operations (matmul, convolution, attention) for CPUs (x64, AArch64, RISC-V) and Intel GPUs. Used by PyTorch, TensorFlow, OpenVINO, and ONNX Runtime.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 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 year9 dated mentions

Observed mentions: 2019-07-21 05:25:29 UTC to 2024-07-03 14:56:09 UTC.

012342019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024

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
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC202
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC404
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC202

What Hacker News says

9 sources
0xfaded·Jul 3, 2024
I'm going to hijack the mention of ML to share xbyaku, a c++ library presenting a DSL for assembling machine code at runtime (useful for JIT). It's used by some of the pytorch back ends. https://github.com/herumi/xbyak Example use: https://github.com/oneapi-src/oneDNN/blob/main/src/cpu/aarch... I learned about these through a blog post about speeding up pytorch on ARM: https://pytorch.org/blog/optimized-pytorch-w-graviton/
In Exploring biphasic programming: a new approach in language design
Lockal·Apr 18, 2024
If you are talking about non-small matrix multiplication in MKL, is now in opensource as a part of oneDNN. It literally has exactly the same code, as in MKL (you can see this by inspecting constants or doing high-precision benchmarks). For small matmul there is libxsmm. It may take tremendous efforts make something faster than oneDNN and libxsmm, as jit-based approach of https://github.com/oneapi-src/oneDNN/blob/main/src/gpu/jit/g... is too flexible: if someone finds a better sequence, oneDNN can reuse it without major change of design. But MKL is not limited to matmul, I understand it...
In Blaze: A High Performance C++ Math Library
AlbertoGP·Jan 22, 2022
My main workstation is too a Raptor Power9 running Void since November 2019, and I’m very happy with it although as the article mentions, “My experience with firmware is that open source does not mean necessarily better, rather the opposite”. OpenBMC (the firmware for the little ARM-based embedded computer that boots the big one) has a number of rough edges. From time to time I submit patches to software to make it work (or work better) on this machine, last week it was these two trivial ones: https://github.com/ciao-lang/ciao/pull/45 https://github.com/janet-lang/janet/pull/915 Typically that’s all that’s needed, in some cases it’s just more of it: https://github.com/oneapi-src/oneDNN/pull/767 But for most things, since it’s Linux, it’s just those small changes: (one of my customers runs the software I write on AIX and that’s a little mo
Read the rest
re work) https://github.com/IntelRealSense/librealsense/pull/5586 https://sourceforge.net/p/speed-dreams/tickets/1022/ For Rust programs, many packages used i8 for C strings (to interface with OpenGL for instance) which broke when running on ppc64 and had to be changed to c_char, but that’s not a problem these days, I guess because many people now test on ARM. https://github.com/femtovg/femtovg/pull/5 Before Power10 was done, IBM actually asked us Raptor users about proposals for useful machine code instructions to add to it. I replied that I’d like to have hardware UTF-8 de-/encoding but they wanted a more detailed proposal and I never got around to write it. I’m not even sure that this would be worthwhile, but I see UTF-8 de-/encoding everywhere in the code I write and would like it to approach memory read/write speeds. I was very disappointed to learn that they had gone more proprietary with Power10 so I would not have been able to use those instructions anyway. What a pity! This machine still covers my needs so I’m not planning to replace it any time soon.
In Keeping POWER relevant in the open source world
hellking4u·Aug 31, 2020
From the oneDNN documentation[1]: > The library is optimized for Intel Architecture Processors, Intel Processor Graphics and Xe architecture-based Graphics. Now, whether "optimized" means tuned for Intel architecture, or crippled for other architecture, is something to be figured out. Given the precedent set by MKL, I wouldn't trust it to be the 'tuned' option. Someone who speaks C++ may be able to better discern if such concerns exist with one-dnn. [1] https://github.com/oneapi-src/oneDNN
In Blender 2.90
sitkack·Jul 23, 2020
It sounds like you are off to a great start. Afaik there really isn't any 400 or 500 level material for advanced assembly programming. You could read Goto BLAS [1] or mkl-dnn [2] I find reading assembly output from compilers to be a good start, it also helps you develop a mental model of what compilers expect in terms of stack hygiene. Before godlbolt, https://godbolt.org/ there was `gcc -S` and now there is [3] gcc -Wa,-adhln -g <file.c> for interleaving source and assembly output. Keep the reference manual close and write lots of little experiments to confirm your findings. [1] https://en.wikipedia.org/wiki/GotoBLAS [2] uses macros and intrinsics https://github.com/oneapi-src/oneDNN [3] https://stackoverflow.com/questions/3867721/is-there-any-c-c...
In Ask HN: How did you learn x86-64 assembly?
m0zg·Dec 8, 2019
AMD needs to do something like https://github.com/intel/mkl-dnn and https://github.com/opencv/dldt, either by talking to Intel to get them to accept contributions (vastly preferable), or, failing that, by forking the libs and implementing their own support. With recent work on efficient NN architectures the CPUs are pretty viable for most _inference_ (not training) tasks. DLDT/OpenVINO perf is particularly impressive. It does, however, extensively use JIT kernel generation through https://github.com/herumi/xbyak depending on the supported features for a particular chip (search for "mayiuse()" in the code to see examples). It's anyone's guess what it will detect and generate on an AMD chip however. Now that AMD is raking in more cash, it'd be a great time to invest some of that back into their dev ecosystem.
In Reducing the Performance Gap of Intel's MKL on AMD Threadripper

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