gpu_clock_stabilizer
cuda-api-wrappers
gpu_clock_stabilizer | cuda-api-wrappers | |
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1 | 10 | |
6 | 731 | |
- | - | |
0.0 | 8.5 | |
over 1 year ago | 3 days ago | |
C++ | C++ | |
MIT License | BSD 3-clause "New" or "Revised" License |
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gpu_clock_stabilizer
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GPU clock stabilizer for consistent GPU profiling on Windows
I've just released a simple GPU clock stabilizer used for consistent GPU profiling on Windows. It allows more deterministic timestamp query results on modern graphics APIs used for calculating elapsed GPU time at the expense of lower performance. For any questions you can find me on twitter.
cuda-api-wrappers
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VUDA: A Vulkan Implementation of CUDA
1. This implements the clunky C-ish API; there's also the Modern-C++ API wrappers, with automatic error checking, RAII resource control etc.; see: https://github.com/eyalroz/cuda-api-wrappers (due disclosure: I'm the author)
2. Implementing the _runtime_ API is not the right choice; it's important to implement the _driver_ API, otherwise you can't isolate contexts, dynamically add newly-compiled JIT kernels via modules etc.
3. This is less than 3000 lines of code. Wrapping all of the core CUDA APIs (driver, runtime, NVTX, JIT compilation of CUDA-C++ and of PTX) took me > 14,000 LoC.
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WezTerm is a GPU-accelerated cross-platform terminal emulator
> since the underlying API's are still C/C++,
If the use of GPUs is via CUDA, there are my https://github.com/eyalroz/cuda-api-wrappers/ which are RAII/CADRe, and therefore less unsafe. And on the Rust side - don't you need a bunch of unsafe code in the library enabling GPU support?
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GNU Octave
Given your criteria, you might want to consider (modern) C++.
* Fast - in many cases faster than Rust, although the difference is inconsequential relative to Python-to-Rust improvement I guess.
* _Really_ utilize CUDA, OpenCL, Vulcan etc. Specifically, Rust GPU is limited in its supported features, see: https://github.com/Rust-GPU/Rust-CUDA/blob/master/guide/src/... ...
* Host-side use of CUDA is at least as nice, and probably nicer, than what you'll get with Rust. That is, provided you use my own Modern C++ wrappers for the CUDA APIs: https://github.com/eyalroz/cuda-api-wrappers/ :-) ... sorry for the shameless self-plug.
* ... which brings me to another point: Richer offering of libraries for various needs than Rust, for you to possibly utilize.
* Easier to share than Rust. A target system is less likely to have an appropriate version of Rust and the surrounding ecosystem.
There are downsides, of course, but I was just applying your criteria.
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How CUDA Programming Works
https://github.com/eyalroz/cuda-api-wrappers
I try to address these and some other issues.
We should also remember that NVIDIA artificially prevents its profiling tools from supporting OpenCL kernels - with no good reason.
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are there communities for cuda devs so we can talk and grow together?
On the host side however - the API you use to orchestrate execution of kernels on GPUs, data transfers etc. - the official API is very C'ish, annoying and confusing. I have written C++'ish wrappers for it which many enjoy but are of course not officially supported or endorsed: https://github.com/eyalroz/cuda-api-wrappers
- Thin C++-Flavored Wrappers for the CUDA APIs: Runtime, Driver, Nvrtc and NVTX
- Integrating the CUDA APIs (Driver, Runtime, JIT) in pleasant modern-C++ wrappers
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Cybercriminals who breached Nvidia issue one of the most unusual demands ever
Oh, I really wish those hackers would release the sources rather than pursue their dumbass crypto-mining demands... "We decided to help mining and gaming community" - hurting the gaming community, helping the get-rich-quick "community".
My own C++ wrappers for the CUDA APIs (shameless self-plug: https://github.com/eyalroz/cuda-api-wrappers/) would really benefit a lot from behind-the-curtains access to the driver; and even if I just know how the internal logic of the driver and the runtime works, without actually being able to hook into that logic - I would already be able to leverage this somewhat in my design considerations.
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AMD’s Lisa Su Breaks Through the Silicon Ceiling
As a person making a living from being the "GPU guy" - I definitely agree.
The ecosystem around AMD GPUs is quite small - and now that they seem to have abandoned OpenCL (possibly not their own fault though) - even that is put into question.
But things are bad even on the NVIDIA side. Example of how bad: I had to write my own C++ bindings for the CUDA runtime API (https://github.com/eyalroz/cuda-api-wrappers/). You'd think they would have that after 13 years of CUDA being available, right? Wrong. I repeatedly tried to pitch this to them, but they seem to suffer from the "Not Invented Here" syndrome (https://learnosity.com/not-invented-here-syndrome-explained/). This despite me having a lot of respect for people like Mark Harris, Bryce Lelbach, Duane Merrill et alia, and their work.
You're also rights about the "two kinds of brains" - or rather, it's not clear to me that the brains creating the silicon and the brains creating the software are in close enough cooperation.
By the way - it is possible to extract a pretty distribution of CUDA to justify run 20 lines of GPGPU code, from their installer. But they won't be bothered to package this nicely for you.
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How do I use gpus (c++)
Try Vulcan, or OpenCL. There are tons of wrappers for CUDA to make coding simpler ie https://github.com/eyalroz/cuda-api-wrappers
What are some alternatives?
ParallelReductionsBenchmark - Thrust, CUB, TBB, AVX2, CUDA, OpenCL, OpenMP, SyCL - all it takes to sum a lot of numbers fast!
imgui - Dear ImGui: Bloat-free Graphical User interface for C++ with minimal dependencies
libcudacxx - [ARCHIVED] The C++ Standard Library for your entire system. See https://github.com/NVIDIA/cccl
Duilib
takedetour - A template (and a sample) for writing tracers on Windows. Based on the Detours library.
ILGPU - ILGPU JIT Compiler for high-performance .Net GPU programs
GLSL-PathTracer - A toy physically based GPU path tracer (C++/OpenGL/GLSL)
nana - a modern C++ GUI library
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
AdaptiveCpp - Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
Elements C++ GUI library - Elements C++ GUI library
FTXUI - Features: - Functional style. Inspired by [1] and React - Simple and elegant syntax (in my opinion). - Support for UTF8 and fullwidth chars (→ 测试). - No dependencies. - Cross platform. Linux/mac (main target), Windows (experimental thanks to contributors), - WebAssembly. - Keyboard & mouse navigation. Operating systems: - linux emscripten - linux gcc - linux clang - windows msvc - mac clang