hipBLAS
HIP
| hipBLAS | HIP | |
|---|---|---|
| 3 | 30 | |
| 100 | 3,488 | |
| - | 2.2% | |
| 9.3 | 8.9 | |
| 6 days ago | 3 days ago | |
| C++ | C++ | |
| GNU General Public License v3.0 or later | MIT 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.
hipBLAS
- A 100x speedup with unsafe Python
-
How do you build Apps with hipblas using CMake?
For example: the simple SGEMM example can be manually compiled in either platform simply as:
-
How do you build hipblas using CMake?
'm trying to build the SGEMM example in the library using CMake v3.16, but I'm just stuck ...
HIP
-
Porting HPC Applications to AMD Instinct MI300A Using Unified Memory and OpenMP
>ROCm or HIP?
I'm not sure that's even the right question to ask. Afaik ROCm is the name of that entire tech stack and HIP is AMD's equivalent to CUDA C++ (they basically replicated the API and replaced every "CUDA" by "hip", they have functions called "hipmalloc" and "hipmemcpy").
The repository is located at https://github.com/ROCm/HIP.
- Hip: Runtime API and Kernel Language for Portable Apps for AMD and Nvidia GPUs
-
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
-
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/)
-
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.
-
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
-
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
-
[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.
What are some alternatives?
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!
ZLUDA - CUDA on AMD GPUs
futhark - :boom::computer::boom: A data-parallel functional programming language
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.
ginkgo - Numerical linear algebra software package
rocm-arch - A collection of Arch Linux PKGBUILDS for the ROCm platform
HIP-CPU - An implementation of HIP that works on CPUs, across OSes.
Trixi.jl - Trixi.jl: Adaptive high-order numerical simulations of conservation laws in Julia
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
HIPIFY - HIPIFY: Convert CUDA to Portable C++ Code [Moved to: https://github.com/ROCm/HIPIFY]
nvidia-patch - This patch removes restriction on maximum number of simultaneous NVENC video encoding sessions imposed by Nvidia to consumer-grade GPUs.
lfort - A Fortran language frontend for LLVM