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Top 23 Intel Open-Source Projects
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Some times ago I have played with johnny-five, a JavaScript Robotics & IoT platform. In short words, we can communicate with our Arduino or Raspberry by using JavaScript with a very friendly syntax.
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Project mention: Nvtop: Linux Task Monitor for Nvidia, AMD and Intel GPUs | news.ycombinator.com | 2024-03-12
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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I really like magic-trace [0].
https://github.com/janestreet/magic-trace
Not that the exact tracing relies on Intel PT - support for AMD was added recently but uses perf so suffers from the same sampling/skew issues, but is still very useful.
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obs-StreamFX
StreamFX is a plugin for OBS® Studio which adds many new effects, filters, sources, transitions and encoders! Be it 3D Transform, Blur, complex Masking, or even custom shaders, you'll find it all here.
Project mention: OBS telling me I need to update or remove plugins but I can’t find these two plugins in my plugins folder in my C-Drive. What do I do? | /r/obs | 2023-07-01 -
You can visualize how instructions are encoded with zydisinfo. Pass in your architecture and the hex bytes of the instructions and it’ll show all relevant info
https://github.com/zyantific/zydis/tree/master
https://www.hexacorn.com/blog/2023/09/27/zydisinfo-the-disas...
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waifu2x-ncnn-vulkan
waifu2x converter ncnn version, runs fast on intel / amd / nvidia / apple-silicon GPU with vulkan
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FastDeploy
⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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I don't know how often it's a problem, but I work for a company doing software video encoding, and we always fill up all the dimm slots on servers to have as much bandwidth as possible, even if we have only really use maybe 1/4 of the RAM.
I'm not sure any of the standard Linux tools can show you memory bandwidth usage easily (maybe perf), I know we use Intel PCM (https://github.com/intel/pcm) and AMDuProfPCM (https://www.amd.com/en/developer/uprof.html)
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Project mention: Implementing a GPU's Programming Model on a CPU | news.ycombinator.com | 2023-10-14
This so-called GPU programming model has existed many decades before the appearance of the first GPUs, but at that time the compilers were not so good like the CUDA compilers, so the burden for a programmer was greater.
As another poster has already mentioned, there exists a compiler for CPUs which has been inspired by CUDA and which has been available for many years: ISPC (Implicit SPMD Program Compiler), at https://github.com/ispc/ispc .
NVIDIA has the very annoying habit of using a lot of terms that are different from those that have been previously used in computer science for decades. The worst is that NVIDIA has not invented new words, but they have frequently reused words that have been widely used with other meanings.
SIMT (Single-Instruction Multiple Thread) is not the worst term coined by NVIDIA, but there was no need for yet another acronym. For instance they could have used SPMD (Single Program, Multiple Data Stream), which dates from 1988, two decades before CUDA.
Moreover, SIMT is the same thing that was called "array of processes" by C.A.R. Hoare in August 1978 (in "Communicating Sequential Processes"), or "replicated parallel" by Occam in 1985 or "PARALLEL DO" by "OpenMP Fortran" in 1997-10 or "parallel for" by "OpenMP C and C++" in 1998-10.
The only (but extremely important) innovation brought by CUDA is that the compiler is smart enough so that the programmer does not need to know the structure of the processor, i.e. how many cores it has and how many SIMD lanes has each core. The CUDA compiler distributes automatically the work over the available SIMD lanes and available cores and in most cases the programmer does not care whether two executions of the function that must be executed for each data item are done on two different cores or on two different SIMD lanes of the same core.
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optimum
🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools
Project mention: FastEmbed: Fast and Lightweight Embedding Generation for Text | dev.to | 2024-02-02Shout out to Huggingface's Optimum – which made it easier to quantize models.
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Project mention: Has anyone been able to figure out how to read VCCSA (system agent voltage) on linux? | /r/overclocking | 2023-05-08
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Autodesk-Fusion-360-for-Linux
This is a project, where I give you a way to use Autodesk Fusion 360 on Linux!
i found a script someone made (here) but it just isnt working, when i use bottle i get an error that it failed to login, and when i use the script wget just times out. i manually downloaded the script, but the script has wget in it anyway. i tried adding -4 to wget, which didnt help.
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StringZilla
Up to 10x faster strings for C, C++, Python, Rust, and Swift, leveraging SWAR and SIMD on Arm Neon and x86 AVX2 & AVX-512-capable chips to accelerate search, sort, edit distances, alignment scores, etc 🦖
Project mention: Measuring energy usage: regular code vs. SIMD code | news.ycombinator.com | 2024-02-19The 3.5x energy-efficiency gap between serial and SIMD code becomes even larger when
A. you do byte-level processing instead of float words;
B. you use embedded, IoT, and other low-energy devices.
A few years ago I've compared Nvidia Jetson Xavier (long before the Orin release), Intel-based MacBook Pro with Core i9, and AVX-512 capable CPUs on substring search benchmarks.
On Xavier one can quite easily disable/enable cores and reconfigure power usage. At peak I got to 4.2 GB/J which was an 8.3x improvement in inefficiency over LibC in substring search operations. The comparison table is still available in the older README: https://github.com/ashvardanian/StringZilla/tree/v2.0.2?tab=...
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Macbook Airs of that era ran Sandy Bridge processors, so, assuming apple didn't factory under volt them, you can usually get away with at least a 100 mV under volt, which would increase all core performance, extend turboboost time, and increase battery life.
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intel-extension-for-pytorch
A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
OK I found it. Looks like they use SYCL (which for some reason they've rebranded to DPC++): https://github.com/intel/intel-extension-for-pytorch/tree/v2...
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Real-ESRGAN-ncnn-vulkan
NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.
Don't even need topaz, there's plenty of free upscale models check the openmodeldb, and you have reve which is based on Real-ESRGAN-ncnn-vulkan.
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scikit-learn-intelex
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
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Project mention: AMD Ryzen Master Utility for Overclocking Control | news.ycombinator.com | 2024-02-17
Makes sens though. Overclocking APUs in laptops is much more trickier and riskier than desktop CPUs, especially that the optimal settings have already been tuned by the OEM in firmware based on known thermal and VRM power design limitations.
If you really do want to tinker with your laptop APU try this:
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Intel's modern compilers (icx, icpx) are clang-based. There is an open-source version [1], and the closed-source version is built atop of this with extra closed-source special sauce.
AOCC and ROCm are also based on LLVM/clang.
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Intel related posts
- Measuring energy usage: regular code vs. SIMD code
- AMD Ryzen Master Utility for Overclocking Control
- Show HN: StringZilla v3 with C++, Rust, and Swift bindings, and AVX-512 and NEON
- How fast is rolling Karp-Rabin hashing?
- FastEmbed: Fast and Lightweight Embedding Generation for Text
- Efficient LLM inference solution on Intel GPU
- When Optimising Code, Measure
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A note from our sponsor - SaaSHub
www.saashub.com | 28 Mar 2024
Index
What are some of the best open-source Intel projects? This list will help you:
Project | Stars | |
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1 | johnny-five | 13,186 |
2 | nvtop | 7,231 |
3 | magic-trace | 4,411 |
4 | obs-StreamFX | 3,775 |
5 | zydis | 3,149 |
6 | waifu2x-ncnn-vulkan | 2,874 |
7 | FastDeploy | 2,653 |
8 | pcm | 2,494 |
9 | ispc | 2,386 |
10 | optimum | 2,068 |
11 | CoreFreq | 1,898 |
12 | cpufetch | 1,755 |
13 | QualityScaler | 1,676 |
14 | Autodesk-Fusion-360-for-Linux | 1,662 |
15 | StringZilla | 1,660 |
16 | thor-os | 1,599 |
17 | undervolt | 1,463 |
18 | NFF-Go | 1,344 |
19 | intel-extension-for-pytorch | 1,279 |
20 | Real-ESRGAN-ncnn-vulkan | 1,270 |
21 | scikit-learn-intelex | 1,148 |
22 | Universal-x86-Tuning-Utility | 1,145 |
23 | llvm | 1,117 |