llvm
kompute
llvm | kompute | |
---|---|---|
10 | 37 | |
1,166 | 1,494 | |
3.9% | 2.9% | |
10.0 | 8.1 | |
6 days ago | 1 day ago | |
C++ | ||
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
llvm
-
Vcc – The Vulkan Clang Compiler
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.
[1] https://github.com/intel/llvm
-
device::aspects ?
You are not missing anything spec-wise, it is just that particular version of the compiler/runtime doesn't support that query. Support for it was added in intel/llvm#7937 and it should be available in the next oneAPI release.
-
How to install OpenCL for AMD CPU?
Install the Intel OpenCL CPU Runtime. AMD CPUs are x86-64 too, so they work just like Intel CPUs do. Afaik, performance is significantly better than with POCL. This also works with EPYC, like the new 96-core Genoa.
-
Modern Software Development Tools and oneAPI Part 2
The Meson build system Version: 1.0.0 Source dir: /var/home/sri/Projects/simple-oneapi Build dir: /var/home/sri/Projects/simple-oneapi/builddir Build type: native build Project name: simple-oneapi Project version: 0.1.0 C compiler for the host machine: clang (clang 16.0.0 "clang version 16.0.0 (https://github.com/intel/llvm 08be083e07b1fd6437267e26adb92f1b647d57dd)") C linker for the host machine: clang ld.bfd 2.34 C++ compiler for the host machine: clang++ (clang 16.0.0 "clang version 16.0.0 (https://github.com/intel/llvm 08be083e07b1fd6437267e26adb92f1b647d57dd)") C++ linker for the host machine: clang++ ld.bfd 2.34 Host machine cpu family: x86_64 Host machine cpu: x86_64 Build targets in project: 1 Found ninja-1.11.1.git.kitware.jobserver-1 at /var/home/sri/.local/bin/ninja
-
Modern Software Development Tools and oneAPI Part 1
$ sudo mkdir -p /opt/intel $ sudo mkdir -p /etc/OpenCL/vendors/intel_fpgaemu.icd $ cd /tmp $ wget https://github.com/intel/llvm/releases/download/2022-WW50/oclcpuexp-2022.15.12.0.01_rel.tar.gz $ wget https://github.com/intel/llvm/releases/download/2022-WW50/fpgaemu-2022.15.12.0.01_rel.tar.gz $ sudo bash # cd /opt/intel # mkdir oclfpgaemu- # cd oclfpgaemu- # tar xvfpz /tmp/fpgaemu-2022.15.12.0.01_rel.tar.gz # cd .. # mkdir oclcpuexp_ # cd oclcpuexp- # tar xvfpz /tmp/oclcpuexp- # cd ..
-
Cross Platform Computing Framework?
oneAPI includes an implementation of SYCL called DPC++. This implementation supports Intel, Nvidia and AMD GPUs (currently for Nvidia and AMD you need to build the support from the source) but oneAPI also includes some libraries too like oneDNN and oneMKL that use SYCL.
-
Does an actually general purpose GPGPU solution exist?
Yes, you can use multiple backends with the same compiled binary. For example you can use DPC++ with Nvidia, AMD and Intel GPU at the same time. ComputeCpp also has the ability to output a binary that can target multiple targets. Each backend generates the ISA for each GPU, and then the SYCL runtime chooses the right one at execution time. There is no ODR violation because each GPU executable is stored on separate ELF sections and loaded at runtime : the C++ linker does not see them. The code doesn't need to have any layers, the only changes you might (but don't have to) make are to optimize for specific processor features.
-
Why Does SYCL Have Different Implementations, and What Version to Use for GPGPU Computing(With Slower CPU Mode for Testing/No Gpu Machines)?
Intel LLVM SYCL oneAPI DPC++ - an open source implementation of SYCL that is being contributed to the LLVM project
-
How to set up Intel oneAPI?
I'm using intel cpu, and after reading this i'm just curious can i set this up with portage? Are there any ebuilds to build this? Do i need whole toolchain from intel site (3Gb+) or just 300 mb tar from their github?
- Benchmarking Division and Libdivide on Apple M1 and Intel AVX512
kompute
-
Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
The two I know of are IREE and Kompute[1]. I'm not sure how much momentum the latter has, I don't see it referenced much. There's also a growing body of work that uses Vulkan indirectly through WebGPU. This is currently lagging in performance due to lack of subgroups and cooperative matrix mult, but I see that gap closing. There I think wonnx[2] has the most momentum, but I am aware of other efforts.
[1]: https://kompute.cc/
[2]: https://github.com/webonnx/wonnx
-
[P] - VkFFT version 1.3 released - major design and functionality improvements
Great to see the positive momentum of this framework! Best wishes and upvotes from the Vulkan Kompute team :)
-
VkFFT: Vulkan/CUDA/Hip/OpenCL/Level Zero/Metal Fast Fourier Transform Library
To a first approximation, Kompute[1] is that. It doesn't seem to be catching on, I'm seeing more buzz around WebGPU solutions, including wonnx[2] and more hand-rolled approaches, and IREE[3], the latter of which has a Vulkan back-end.
[1]: https://kompute.cc/
[2]: https://github.com/webonnx/wonnx
[3]: https://github.com/openxla/iree
-
I'm Having Trouble Building this Library...
I look in an example and see similar instructions, stating that the build should be quite simple. But again, it doesn't work. It generates a bunch of folders with Visual Studio stuff, but no executables, no libraries, or anything like that.
I can't figure out how, and there are no tutorials. According to https://kompute.cc/overview/build-system.html I should simply run "cmake -Bbuild". But this doesn't output what I need, and when I look in the Makefile I get the sense that this is more an example Makefile... but then that contradicts the above tutorial.
-
How to properly construct an abstraction layer with Vulkan
Kompute is in my opinion good example to take inspiration for abstractions.
-
Vulkan for Image Processing? Good choice?
Currently, there's a few Vulkan compute frameworks floating around (like Kompute). I would work with those. Kompute simplifies a lot of the biolerplate and seems like you could benefit from using it.
-
Paralell computing project
Try Kompute, a project from the Linux foundation. It is quite simple to use, and does not require deep knowledge of graphics API. It’s a bit painful to setup, but it kinda works well (and I have a project going on on it)
-
Bootstrapping Vulkan for Scientific Compute Applications?
This so much.
-
[D] PyTorch is moving to the Linux Foundation
This makes alot of sense considering the Linux Foundation is also in charge of Kompute which is likely to be the basis of vendor agnostic GPGPU, and thus the basis of vendor agnostic GPU-based machine learning.
What are some alternatives?
pocl - pocl - Portable Computing Language
rust-gpu - 🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧
oneTBB - oneAPI Threading Building Blocks (oneTBB)
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
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!
VkFFT - Vulkan/CUDA/HIP/OpenCL/Level Zero/Metal Fast Fourier Transform library
meson - The Meson Build System
OpenCLOn12 - The OpenCL-on-D3D12 mapping layer
OCL-SDK
godot-proposals - Godot Improvement Proposals (GIPs)
featuresupport
VulkanExamples - Examples and demos for the Vulkan C++ API