AMD at a Tipping Point with Instinct MI100 GPU Accelerators
nextplatform.com
nextplatform.com
> The fun bit is that Arcturus is the first GPU accelerator to break through the 10 teraflops floating point barrier 64-bit precision – and AMD is doing it in a 300 watt thermal envelope for 11.5 teraflops, not a 400 watt one like Nvidia has with its Ampere A100, which weighs in at only 9.7 teraflops at 64-bit precision floating point
It is expensive
> Pricing on the Instinct MI100 was buried in the footnotes, and is $6,400 a pop in single unit quantities. We are going to try to see what the OEMs are charging for it.
You can connect them together in more efficient "hives"
> Having three Infinity Fabric pipes per Arcturus GPU allows a NUMA-like coupling of four GPUs and 128 GB of HBM2 memory into a much bigger virtual GPU, much like UltraPath Interconnect at Intel
Whereas before it was less efficient
> With only two Infinity Fabric ports on the Instinct MI50 and MI60 cards, banks of GPUs could only be hooked to each other in a ring topology and the larger the number of GPUs in the ring, the more latency between the devices.
I'm sure this is an incomplete list of highlights, but these all jumped out at me
But the issue is always software, ROCm is currently a joke.
Flawless Windows and Linux support.
Software updates released in line with GPU releases, and guaranteed continued support throughout their lifecycle.
Full support for consumer grade GPUs and APUs.
The a100 is theoretically $12,500 https://www.pcgamer.com/nvidia-ampere-a100-price/
At $6400 this is still only ~50% of the A100 price.
But as others have said, it does not really matter while ROCm is still so bad.
CUDA works because any NVIDIA GPU will run CUDA this means it's easier to learn, easier to prototype and easier to ship and the code you ship isn't limited to the datacenter.
What AMD needs to do to "win" an HPC GPU launch is to have an event which is 95% "How we fixed ROCm, and here is our full software roadmap and support guarantee for the next 5 years" and the remaining 5% "oh btw here is our new silicon, it's really fast and shiny".
> What AMD needs to do to "win" an HPC GPU launch is to have an event which is 95% "How we fixed ROCm, and here is our full software roadmap and support guarantee for the next 5 years" and the remaining 5% "oh btw here is our new silicon, it's really fast and shiny".
This is a great point. NVDA has worked on CUDA for years and has a great ecosystem of material and questions on places like stackexchange. AMD will have to work very purposefully to close the gap, but it seems like they are aware and headed in the right direction.
There are also whole "data sciences" divisions in bluechip companies that are running windows.
Case in point i work for a huge financial company we have CUDA powered excel add-ins/macros...
And no I'm not joking https://on-demand.gputechconf.com/gtc/2010/presentations/S12...
And engineering, sciences and medical consumer applications are also quite often than not Windows only or Windows first.
Then you have all the less-enterprisy stuff video and photo editing, filters, chess programs w/e...
And lastly the biggest point is that Windows and consumer grade hardware is where most developers and students live, good luck running ROCm on your laptop, and no I really mean it it's officially not supported and in reality even if you manage to get a moderately compatible chip you'll encounter more bugs than on Klendathu.
Don't underestimate the importance of software that runs everywhere and just works. Node.JS didn't became popular because JavaScript on the backend was something that was desperately needed, it became popular because you had a plethora of front-end developers that had little to no knowledge of server-side languages and frameworks.
Unlike what HN and recruiters would like you to believe most developers can't learn 10 languages and frameworks, and definitely not well sure some can but the vast majority of developers don't spend 9 hours working and 9 hours hacking, for every dev with a github account that needs it's own storage rack there are 10,000 that just do 9 to 5 and check out.
If on one hand you have a solution that forces you to pick from a narrow list of linux kernels and supported distros and an extremely narrow list of GPUs and still encounter bugs on every corner so you can maybe produce something that if it runs would only run on the same system as yours vs on the other hand a solution that would run on any OS that supports an NVIDIA GPU you'll pick the latter unless you are really really bored.
And that is before you entertain the marketability and job prospects of learning CUDA vs ROCm, one allows you to get a job at any place that ships something that runs on a GPU it doesn't matter if it's something that occupies 1000 racks and might become sentient or something that filters excel spreadsheets faster the other one doesn't.
Thank you for sharing the link and correcting my information bias. It sounds like the "workstation," compute world is a forest of deep niches.
You make a lot of good points about the staying power of Windows. I am excited about all the moves towards a complete Linux desktop, but am not imagining that it will be mainstream.
There are still more use cases for GPU compute on the edge than in the datacenter and that likely won’t change.
And for Linux on the enterprise desktop well then ROCm can’t run in WSL2, CUDA can so yet another reason to bloody support Windows...
Because WSL2 is ironically probably the way forward for Linux on the desktop for the majority of the computerized workforce.
Funny you mention ANSYS specifically as they seem to have pretty decent Linux support:
https://www.ansys.com/solutions/solutions-by-role/it-profess...
Although only on nVidia hardware if I'm interpreting it right
Windows still gets semi-decent support especially for their WS cards but Linux oh-boy...
With ANSYS in particular the question is who is doing the simulation. If the engineer is doing it, many engineering tools are windows only (although this has been improving) so it makes sense to run ANSYS under windows as well so you can be close to your modelling software. If the stress or EM guys are separate from the designers, then it shouldn't matter as much.
I would love to see all productivity tools move to Linux and things have been getting a lot better over the years. Personally I'm excited around the noise that Microsoft was exploring office for Linux, as office is the only reason I ever boot into windows. What a godsend it would be to be able to program and run all my productivity software at the same time.
Source: Struggled for months to get ANSYS to work even crappily on three different distros of Linux, two of which were clean installs of "officially supported" distros.
Eventually gave up, bought Windows, and installed it on that. Worked immediately.
AMDs linux support has been somewhere between no-assed to half-assed for the preceding decade. I think they believed that the world will return to windows for everything. That ship has sailed, long ago. The point about CUDA being usable up/down the HW stack is quite salient. When I develop GPU things, I start on my laptop GTX1060. Test on my deskside RTX2060, and run them on V100s. Code is in Julia, C, Fortran, so it should work anywhere with good underlying library support. I've got a zen laptop with integrated Radeon. No dice, can't do computing on it (yet).
AMDs function/library support is nascent, and will take years to get to a viable point for many.
I am hoping ... hoping ... that AMD sees this as an opportunity long term, and not a short term expense that must provide immediate ROI. SW ecosystems drive the HW purchases, but there is usually a lag of years before this engine really gets started.
AMD needs to be in this for the long haul.
[1] https://www.anandtech.com/show/15581/el-capitan-supercompute...
> The OLCF plans to make HIP available on Summit so that users can begin using it prior to its availability on Frontier. HIP is a C++ runtime API that allows developers to write portable code to run on AMD and NVIDIA GPUs. It is essentially a wrapper that uses the underlying CUDA or ROCm platform that is installed on a system. The API is very similar to CUDA so transitioning existing codes from CUDA to HIP should be fairly straightforward in most cases. In addition, HIP provides porting tools which can be used to help port CUDA codes to the HIP layer, with no loss of performance as compared to the original CUDA application. HIP is not intended to be a drop-in replacement for CUDA, and developers should expect to do some manual coding and performance tuning work to complete the port.
[1] https://news.ycombinator.com/item?id=20495637
[2] https://www.olcf.ornl.gov/wp-content/uploads/2019/05/frontie...
For example, rocmdocs.amd.com appears to be the official documentation, but the front page doesn't mention any popular AI framework. Opening the "Deep Learning" box in the sidebar, I see that there should be a TensorFlow 2.2.0-beta1 release. But on https://github.com/ROCmSoftwarePlatform/tensorflow-upstream the featured release is "2.0.0-rocm", the docker link says it's TensorFlow 2.3 and the "Official Builds" at the bottom of the readme point to CUDA versions ... The whole GitHub repo kind of feels like a bad copy&paste job. I manage to eventually download the TensorFlow 2.3.1 + ROCm source from GitHub, only to find that it doesn't compile. I need the hip_hcc library https://github.com/ROCm-Developer-Tools/HIP/ but apparently that one has been renamed some months ago, but the ROCm TensorFlow port has not been updated yet.
After spending half a day trying to install a ROCm-compatible TensorFlow, I gave up. Obviously, I wouldn't want to hope on those issues getting resolved when I buy an as-is unusable $6400 accelerator card. That said, the MI100 could be a great deal if I could get it to work. AMD hardware is a lot more affordable than comparable NVIDIA products, plus the MI100 has lots of fast RAM. I work on optical flow (think autonomous selfie drone) and that needs lots and lots of GPU RAM. Plus I'm very happy with my AMD CPU. But like I said in the beginning, I wouldn't dare buying an AMD GPU unless there was believable evidence that I can get my TF2.3 model running with ROCm in a reasonable timeframe.
I'm not trying to blame AMD for having bugs here. I have run into a fair share of issues with NVIDIA GPUs, too, like for example a RAM corruption bug inside CUDA 10.0 and a reproducible kernel freeze in CUDA 10.1. But for NVIDIA, the initial setup is quick and easy. And the CUDA documentation looks like they have a dedicated team that cares about developer productivity. For AMD's ROCm, on the other hand, even the marketing material reads more like someone hopes that the open source community will fix things for free. "first open-source software development platform for HPC/Hyperscale-class GPU computing" sounds great until you notice that in this context, "open-source" apparently means "no binaries + no support".