hipfort
gpufort
| hipfort | gpufort | |
|---|---|---|
| 1 | 2 | |
| 65 | 158 | |
| - | 0.0% | |
| 7.7 | 0.0 | |
| 7 days ago | 5 months ago | |
| Fortran | Fortran | |
| GNU General Public License v3.0 or later | MIT License |
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hipfort
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Joe's Live Coding Sessions - GPU Programming in Fortran : Verifying Spectral Accuracy in the Advection-Diffusion Solvers
HIPFort (GPU Programming API in Fortran) : https://github.com/ROCmSoftwarePlatform/hipfort
gpufort
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(Tutorial) Porting a simple Fortran application to GPUs with HIPFort
There is a gpufort project that provides something a bit more like what you're suggesting, but I'm not sure how useful it is in its current state.
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AI Seamless Texture Generator Built-In to Blender
https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni...
RadeonOpenCompute/ROCm_Documentation: https://github.com/RadeonOpenCompute/ROCm_Documentation
ROCm-Developer-Tools/HIPIFYhttps://github.com/ROCm-Developer-Tools/HIPIFY :
> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.
ROCmSoftwarePlatform/gpufort: https://github.com/ROCmSoftwarePlatform/gpufort :
> GPUFORT: S2S translation tool for CUDA Fortran and Fortran+X in the spirit of hipify
ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP:
> HIP is a C++ Runtime API and Kernel Language that allows developers to create portable applications for AMD and NVIDIA GPUs from single source code. [...] Key features include:
> - HIP is very thin and has little or no performance impact over coding directly in CUDA mode.
> - HIP allows coding in a single-source C++ programming language including features such as templates, C++11 lambdas, classes, namespaces, and more.
> - HIP allows developers to use the "best" development environment and tools on each target platform.
> - The [HIPIFY] tools automatically convert source from CUDA to HIP.
> - * Developers can specialize for the platform (CUDA or AMD) to tune for performance or handle tricky cases.*
What are some alternatives?
SELF - Spectral Element Library in Fortran
stable_diffusion.openvino
aomp - AOMP is an open source Clang/LLVM based compiler with added support for the OpenMP® API on Radeon™ GPUs. Use this repository for releases, issues, documentation, packaging, and examples.
ZLUDA - CUDA on AMD GPUs
SciFortran - A library of fortran modules and routines for scientific calculations (*in a way* just like scipy for python)
rcc-run - Continuous Integration and Continuous Benchmarking tools for Research Computing applications
clang-ocl - OpenCL compilation with clang compiler.
FLAP - Fortran command Line Arguments Parser for poor people
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
stdgpu - stdgpu: Efficient STL-like Data Structures on the GPU
stable-diffusion-webui - Stable Diffusion web UI