HIP
AdaptiveCpp
HIP | AdaptiveCpp | |
---|---|---|
29 | 19 | |
3,453 | 1,040 | |
1.2% | 2.2% | |
8.9 | 9.7 | |
6 days ago | 8 days ago | |
C++ | C++ | |
MIT License | BSD 2-clause "Simplified" License |
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.
HIP
- Hip: Runtime API and Kernel Language for Portable Apps for AMD and Nvidia GPUs
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Open-source project ZLUDA lets CUDA apps run on AMD GPUs
Is it perhaps because they want people to use HIP?
> HIP is very thin and has little or no performance impact over coding directly in CUDA mode.
> The HIPIFY tools automatically convert source from CUDA to HIP.
1. https://github.com/ROCm/HIP
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AMD's Next GPU Is a 3D-Integrated Superchip
AMD has released HIP and a tool called HIPIFY which kind of behaves like this but at the source level¹. Rather than try and just translate CUDA to work on AMD compute they are more focused on higher level tooling.
Currently they seem to have a particular focus on AI frameworks and tools like PyTorch/Tensorflow/ONNX. They have sponsored and helped with a lot of PyTorch development for example, so PyTorch support for AMD is much better than it was this time last year².
¹(https://github.com/ROCm/HIP)
²(https://pytorch.org/blog/experience-power-pytorch-2.0/)
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Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
> what would be the point for someone to add ROCm support to various pieces of software which currently require CUDA
It isn't just old cards though, CUDA is a point of centralization on a single provider during a time when access to that providers higher end cards isn't even available and that is causing people to look elsewhere.
ROCm supports CUDA through the included HIP projects...
https://github.com/ROCm/HIP
https://github.com/ROCm/HIPCC
https://github.com/ROCm/HIPIFY
The later will regex replace your CUDA methods with HIP methods. If it is as easy as running hipify on your codebase (or just coding to HIP apis), it certainly makes sense to do so.
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Nvidia on the Mountaintop
AMD's equivalent is HIP [1], for sufficiently flexible definitions of "equivalent". I can't speak to how complete/correct/performant it is (I'm just a guy running tutorial/toy-level ML stuff on an RDNA1 card), but part of AMD's problem is that it might not practically matter how well they do this because the broader ecosystem support specifically for the CUDA stack is so entrenched.
[1] https://github.com/ROCm-Developer-Tools/HIP
- Stable Diffusion in pure C/C++
- Would love to hear your information and knowledge to simplify my understanding on AMD's positioning in the AI market
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Ask HN: C++ still dominates on GPUs, why not Rust?
From what I know, modern GPUs are still programmed with C++ exclusively. See CUDA [0] for Nvidia and ROCm [1] for AMD.
Why is this? Why Rust is not loved there?
[0] https://docs.nvidia.com/cuda/
[1] https://github.com/ROCm-Developer-Tools/HIP
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[P] RWKV C++ Cuda library with no dependencies, no torch, and no python
Go ahead and try to ship ROCm code that works on multiple consumer graphics cards on Linux, MacOS, and Windows. As an example of how much AMD cares about it, the installation notes linked to in the readme returns a 404.
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Someone found a ROCm 5.5 RC Docker Container that works on 7000 series GPUs
The big whoop for ROCm is that AMD invested a considerable amount of engineering time and talent into a tool they call hip. Basically, it's an analysis tool that does its best to port proprietary Nvidia CUDA-style code - which due to various smelly reasons rules the roost - to code that can happily run on AMD graphics cards, and presumably others. Intel has a similar thing going with OneAPI. They've done this whilst working on porting a lot of their code base to the linux AMGPU open source kernel driver, as well.
AdaptiveCpp
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What Every Developer Should Know About GPU Computing
Sapphire Rapids is a CPU.
AMD's primary focus for a GPU software ecosystem these days seems to be implementing CUDA with s/cuda/hip, so AMD directly supports and encourages running GPU software written in CUDA on AMD GPUs.
The only implementation for sycl on AMD GPUs that I can find is a hobby project that apparently is not allowed to use either the 'hip' or 'sycl' names. https://github.com/AdaptiveCpp/AdaptiveCpp
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AMD May Get Across the CUDA Moat
Not natively, but AdaptiveCpp (previously hiSycl, then OpenSycl) has a single source single compiler pass, where they basically store LLVM IR as an intermediate representation.
https://github.com/AdaptiveCpp/AdaptiveCpp/blob/develop/doc/...
Performance penalty was within ew precents, at least according to the paper (figure 9 and 10)
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Offloading standard C++ PSTL to Intel, NVIDIA and AMD GPUs with AdaptiveCpp
AdaptiveCpp (formerly known as hipSYCL) is an independent, open source, clang-based heterogeneous C++ compiler project. I thought some of you might be interested in knowing that we recently added support to offload standard C++ parallel STL algorithms to GPUs from all major vendors. E.g.:
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AMD's HIPRT Working Its Way To Blender With ~25% Faster Rendering
In fact SYCL was initially called hipSYCL because it is based on AMD's ROCm/HIP. AMD had hipSYCL code running on the Frontier supercomputer four years ago at least and continues to support it.
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hipSYCL can now generate a binary that runs on any Intel/NVIDIA/AMD GPU - in a single compiler pass. It is now the first single-pass SYCL compiler, and the first with unified code representation across backends.
Apple Silicon support through Metal is something that is actively discussed in hipSYCL. See https://github.com/illuhad/hipSYCL/issues/864 https://github.com/illuhad/hipSYCL/issues/460 (loooong discussion)
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Bringing Nvidia® and AMD support to oneAPI
But really, the DPC++ part of oneAPI (which is many APIs) is just SYCL + extensions, and there are several other SYCL implementations which have already featured CUDA and Hip (AMD) support for a long time. The most popular and widely-used is hipSYCL, which we've been using in an HPC context on NV hardware for over 4 years now.
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Intel oneAPI 2023 Released - AMD & NVIDIA Plugins Available
Unfortunately, the AMD and Nvidia plugins are proprietary. AMD users are probably better served with hipSYCL, if they somehow find an application using SYCL...
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There is framework for everything.
Also, you might want to take a look at an implementation like hipSYCL :)
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The Next Platform: "Intel Takes The SYCL To Nvidia's CUDA With Migration Tool"
Yup. SYCL is the future: https://github.com/illuhad/hipSYCL
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Phoronix: "Intel's Vulkan Linux Driver Adds Experimental Mesh Shader Support For DG2/Alchemist"
ROCm is completely independent from these. It's a compute stack containing an OpenCL implementation for Radeon GPUs, plus a CUDA-like language called HIP which can be compiled to either device code for Radeon GPUs or to PTX to work with Nvidia GPUs. However, some researchers also created hipSYCL that allows SYCL to run atop HIP; you can think of it like DXVK - the program contains the DirectX/SYCL API, and DXVK/hipSYCL converts it to Vulkan/HIP (with one difference - DXVK does the conversion at runtime, while hipSYCL does it at compile time).
What are some alternatives?
ZLUDA - CUDA on AMD GPUs
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
futhark - :boom::computer::boom: A data-parallel functional programming language
HIP-CPU - An implementation of HIP that works on CPUs, across OSes.
kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
triSYCL - Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group
ginkgo - Numerical linear algebra software package
cuda-api-wrappers - Thin C++-flavored header-only wrappers for core CUDA APIs: Runtime, Driver, NVRTC, NVTX.
rocm-arch - A collection of Arch Linux PKGBUILDS for the ROCm platform
cuda_memtest - Fork of CUDA GPU memtest :eyeglasses:
gpuowl - GPU Mersenne primality test.