The Clang developers are currently working on porting the HIP extensions upstream to the mainline Clang compiler.
https://www.amd.com/en/products/professional-graphics/instin... Why is it a separate repo rather than being contributed upstream to TF? What is ROCm, and why is it poised to shake up the whole HPC industry? Next ». Near-automatic conversion options eliminate time barriers for CUDA shops looking for a more flexible and a less restrictive solution. Likewise for game consoles, Windows and now Apple graphics APIs. WebIDL, abstractly e.g. Probably never will. Also i notice MIopen is not 100% compatible with cuDNN..
ROCm even provides tools for porting vendor-specific CUDA code into a vendor-neutral ROCm format, which makes the massive body of source code written for CUDA available to AMD hardware and other hardware environments.
In the past, GPU vendors developed their own dialects and drivers to activate GPU-based optimizations for their own hardware.
I haven’t heard of large-scale use of it.
It's AMD's job to make machine learning work in their GPU's. So the main challenge for AMD at the moment is to work with maintainers of frameworks and produce good enough solutions to be accepted as contributions.
Khronos is to blame by focusing too much on C, without convient tooling. Khronos is far more lightweight of an organization than, say, ISO's C++ committee. From what I've heard, TensorFlow is what is holding them back, that's the most used platform. Not literally. It's funny though because I remember Raja talking this down a bit, not sure what presentation it was. Then they made a bad standard to start with. > They can be defined in an IDL kind of way, like e.g. Cookies help us deliver our Services. It's in our interest to get this off the ground!
Developers can use any tools supported by the CUDA SDK including the CUDA profiler and debugger.
NVIDIA GPUs via CUDA; AMD GPUs via HIP/ROCm; The following image illustrates how hipSYCL fits into the wider SYCL implementation ecosystem: The philosophy behind hipSYCL is to leverage existing toolchains as much as possible.
As a proof of concept, the ROCm team ported the whole Caffe machine-learning framework (with around 55,000 lines of code) from CUDA to HIP: 99.6 percent of the code went unmodified or was automatically converted, and the remaining 0.4 percent took less than a week of developer time to tie up loose ends.
I hate the Cuda monopoly in machine learning right now. ROCm also integrates multiple programming languages and makes it easy to add support for other languages. The HIP runtime implements HIP streams, events, and memory APIs, and is a … It doesn't exactly provide the same API capabilities as CUDA yet (there are some unsupported functions) and no one has realistic comparisons of benchmarks but the consensus is that there's some performance gap between similar chips from Nvidia and AMD.
New comments cannot be posted and votes cannot be cast, Investor strategies and discussion relating to AMD, Looks like you're using new Reddit on an old browser. Not great if you ask me.
As shown in Figure 2, a CUDA header is all that is needed to prepare the HIP code for the NVIDIA tool chain and the NVCC compiler.
This is what is supposed to make adding support for AMD hardware a piece of cake. The ROCm HIP compiler is based on Clang, the LLVM compiler infrastructure, and the “libc++” C++ standard library. ROCm even provides tools for porting vendor-specific CUDA code into a vendor-neutral ROCm format, which makes the massive body of source code written for CUDA available to AMD hardware and other hardware environments. ROCm also integrates multiple programming languages and makes it easy to add support for other languages. It's their job to make standards, not tooling. It is the must have standard library for nearly every ML-framework out there.But when you look deeper into MIOpen (which is the key to AI-sucess) there are still some bigger parts missing.
5 This article was updated on November 18, 2019. The sooner they support it the better. However, the ROCm developers are well aware that lots of CUDA code is already out there in the world, so ROCm provides an easy path for porting CUDA code to the vendor-neutral HIP format automatically. https://gpuopen.com/compute-product/hip-convert-cuda-to-portable-c-code/.
Over the past three years, ROCm developers have contributed many new features and components to the ROCm open software platform.
You just need to support those frameworks. Internet RFCs, or define mappings to all major languages in the GPGPU field, namely C, C++ and Fortran. PyTorch has AMD support in the main repo: AMD is planning to upstream their work and some is already there, but are still behind in versions they support. But under Tensor Flow AMD wants to go another route and use their LLVM for just in time compiling. Lots of people say nVidia's core competitive advantage in AI is CUDA, but from what I read it's really quite easy to convert CUDA into AMD's HIP: https://gpuopen.com/compute-product/hip-convert-cuda-to-portable-c-code/. The Perl script is often easier to use, especially for smaller jobs. They need to go all-in as the AI market will only grow from here.
ROCm provides two different alternatives for converting CUDA code to HIP format: • hipify-perl – a Perl script that you can run on the source code to convert a CUDA program to equivalent HIP code.
https://hub.docker.com/r/rocm/tensorflow/ but is MI25 competitive? AMD’s ROCm platform brings new freedom and portability to the GPU space. Since then, the company has continued to refine its bold vision for an open source, multiplatform, high-performance computing (HPC) environment. cuDNN is Nvidias gem für AI-Programmers.
Three years ago, AMD released the innovative ROCm hardware-accelerated, parallel-computing environment [1] [2]. Tensorflow has ROCm backend support.
What AMD have done (here is my understanding, it might be incorrect as I haven't looked at the code): they released MIOpen (part of ROCm), which closely mimics CUDA API, HIP is another part of ROCm, which allows to substitute calls to CUDA for calls to MIOpen. CUDA is an example of a proprietary language designed to work with only one hardware vendor.
This vendor lock-in caused inefficient programming practices and limited the organization’s ability to seek a long-term, cost-efficient solution. And tools still seem not to be on par with what NVidia offers. We'll put a couple of guys on it, no problem.
So the main challenge for AMD at the moment is to work with maintainers of frameworks and produce good enough solutions to be accepted as contributions. At the center of the ROCm environment is a technology known as Heterogeneous-Compute Interface for Portability (HIP) [4]. Just wonder what do you think about opencl? It was necessary to loose the race for them to focus on C++ and come up with SPIR and SYSCL. Defining standards does not mean they must provide a lowest denominator single implementation. A modular design lets any hardware vendor build drivers that support the ROCm stack [3].
Radeon Open Compute Platform (ROCm) has existed for years but apparently it's not good enough. ROCm supports a number of programming languages and is flexible enough to interface with different GPU-based hardware environments (Figure 1). Developers jumping on CUDA due to lack of C++ and Fortran support on OpenCL proves how good that decision was. There are some efforts to improve the situation: Not really, at least not yet. > Then they made a bad standard to start with.
2 For HC and C++AMP, assume a captured tiled_ext named “t_ext” and captured extent named “ext”. Nonsense. The result was a tangle of proprietary specs and incompatible languages. And the pull request to Pytorch (another framework, which is gaining significant traction) repository - there's just not enough collaboration in my opinion, the maintainers essentially have been ghosted after they gave feedback to AMD devs. AMD maintains a special version of the open source Clang compiler for preparing and compiling HIP code.
Seems porting cuda to opencl is not that difficult but opencl is not optimised and performance is pretty disappointing. AMD has a translator (HIP) which may help you port CUDA code to run on AMD. CUDA had C, C++ and Fortran support since the early days, followed by the PTX bytecode format for any compiler vendor that wanted to support CUDA on their languages. But he was like, oh it's not really a lot of work. No clue but if consumer vega is competitive. HIP is another part of ROCm, which allows to substitute calls to CUDA for calls to MIOpen This is what is supposed to make adding support for AMD hardware a piece of cake.
3 They can be defined in an IDL kind of way, like e.g.
Figure 2: ROCm’s HIP format lets the vendor write the code once and compile it for different hardware environments. Pretty disappointing if there hasn't been much progress.
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ps. I have never heard of it.. Why it can be big?
The best way to get familiar is to look inside. So I just wonder if anyone can tell me.. why it seems AMD is still behind nVidia in ecosystem, what's the biggest gap and how AMD is doing to close the gap?
4
Khronos is a standards group.
CUDA supports only NVidia GPUs.
The ROCm developers knew the HPC industry needed a universal solution that would end the problem of proprietary specs and incompatible languages, so they built ROCm as a universal open platform that allows the developer to write the code once and compile it for multiple environments. That's already done.
Also.. Can you talk a little bit more on the LLVM thing? Figure 1: ROCm is designed as a universal platform, supporting multiple languages and GPU technologies.
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