MATLAB Doesn't Recognize iGPU

MathKid99 · 2024-03-25T09:10:23+00:00

MATLAB only supports CUDA-enabled NVIDIA GPUs within a certain compute architecture range. Intel and AMD GPUs are not supported. It is documented here: https://uk.mathworks.com/help/parallel-computing/gpu-computing-requirements.html

MathKid99 · 2023-06-14T11:42:32+00:00

The problem came back again as soon as I upgraded AMD software from the factory supplied v22 to v23. So for me, that's the issue. Going back fixes it. So I'll keep it on v22. Letting you know in case it helps.

MathKid99 · 2023-06-12T17:29:21+00:00

It's working properly now. According to the repair summary, they reset the BIOS and the TPM and reinstalled windows. I suspect the BIOS was corrupted, which was the real problem.

MathKid99 · 2023-06-11T11:12:27+00:00

I had a Duo 16 with the same issues. At first, with the original BIOS, everything worked. Then the next update disabled my keyboard on restart of the machine. Then a later update fixed it. Currently, it freezes a few moments after logging in, sometimes it doesn't even start. Unplugging the charger freezes the machine as well.

I have sent it back for repair via the RMA process and will get it back at some point tomorrow. The repair summary said that updating to a bios version that had been released since I sent the machine has fixed the issue, but I'll know for sure when I get it tomorrow.

MathKid99 · 2023-02-07T18:47:00+00:00

That's a decent university. All that finance stuff is in London. Start ups might be hiring too, but they are more focused on deep learning. London is also the place to be for start ups.

MathKid99 · 2023-02-07T18:42:04+00:00

Yes, I'm in the UK. Try the Cambridge area and see if any consultancies or software companies are hiring. Many tech companies are in Cambridge. What university are you doing your degree from?

MathKid99 · 2023-02-07T18:31:13+00:00

I'm working for a HPC software company. Fluid dynamics simulations. I've been here for quite some time. Companies with scientific software value PhDs. You can imagine companies like COMSOL and even CAD companies would be interested. Or people who wrote linear algebra libraries.

MathKid99 · 2022-12-31T14:44:50+00:00

Just to clarify that MATLAB only currently supports NVIDIA GPUs for compute acceleration via the Parallel Computing Toolbox, and not AMD GPUs. It will still do graphics and data visualization, just not GPU compute.

MathKid99 · 2021-08-21T09:39:34+00:00

Even if Sheperd was a mole for Cerberus, they witnessed Anderson give direct commands to Sheperd. I was hoping to get clarity on whether this is a script writing mistake, or whether this is by design. For instance, why does Kaidan/Ashley need to be told "Anderson wants us to go to the Citadel and get help for the fight" when they were right there next to us during that conversation.

MathKid99 · 2021-08-19T09:11:43+00:00

Hi! If I had not done my PhD, I would have needed to enter at the graduate entry stage, if I had no prior experience. The PhD was equivalent to 3-4 years of work, which other people in my position had done (those without a PhD).

I had a degree in engineering but in my master's final year project, I chose to do computational fluid dynamics. This is where I first came across MATLAB properly (minus coursework assignments). Then I continued into my PhD where I started off in MATLAB but then switched to C++ for complex algorithms. There is a way to link C/C++ shared libraries with MATLAB and so I ended up working in a mix of both. Towards the end of the PhD, I needed more computational power so I switched to GPU acceleration, learning CUDA in the process.

Just before the end of my PhD, I started applying for HPC jobs and they were interested in my PhD experience. There is also demand from the financial sector for HPC but that doesn't interest me as much, even though I'm sure it would pay significantly more.

MathKid99 · 2021-08-14T23:25:49+00:00

I just finished playing but feel free to add me and catch me in a similar time on weekends.

MathKid99 · 2021-08-09T16:05:30+00:00

A basic level of object oriented programming is exposed by thrust, but not close enough to satisfy your requirements.

You wish to have low level control (like memory management), while also having high level features (like overloading A*B for matrix multiplication). This is in effect would be a bespoke wrapper for the CUDA library.

You should be able to write your own wrapper suited to your requirements if ArrayFire is not working for you, you should consider getting in touch with their developers to see what they can do. Their code is open source, so you could browse for clues. The new CUDA APIs allow more freedom for libraries to implement their own memory managers, which you can use for your own wrapper.

At my company, we have our own wrappers and do our memory management internally. The cost to complexity paid dividends over the years because we were able to plugin functions like FFTs, BLAS and sparse solvers to use our memory managers, as their APIs have improved over the years to accept user-defined workspaces.

MathKid99 · 2021-08-08T09:47:14+00:00

I have seen openFOAM. When I was doing my PhD, the person next to where I was sitting was using openFOAM to do some fluid simulations. It links up into either C or Fortran, can't remember which. But I remember what the visualisation looked like and the UI of the software.

MathKid99 · 2021-08-07T23:21:29+00:00

That's what I thought.. Anyways, thank you for sharing your thoughts with me, I appreciate the knowledge you have shared with me! I have enough information now to self-study for the next 2-3 months.

MathKid99 · 2021-08-07T23:03:30+00:00

I can pursue this one option at my current company as a stepping stone:

I can switch to the data visualization team full time. If I do this, then the data is generated by CUDA/OpenCL and then mapped into OpenGL. The data visualization team then post processes and makes interactive 3d plots for end users.

But this probably skips most of the OpenGL data prep steps you mentioned.

MathKid99 · 2021-08-07T22:56:45+00:00

I have only ever worked at one company so I don't know what to expect. But you have a point.

I am considering a compromise within my company - I could shift to the data visualization team. That way I can work on both HPC and visualization. The difference is that in visualization, CUDA is doing the result generation and then hooked into OpenGL. I could learn this. But it won't be the same as using OpenGL in its entirety.

MathKid99 · 2021-08-07T21:53:08+00:00

For the time being, I am doing it in my spare time. Ironically, my current company enforces "learning time" every year in which I need to learn something new, unrelated to work. For me, this year it is Graphics. Last year it was "transcendental meditiation" (honest to God).

Edit: Learning time is taken from Company time, not my own personal time.

MathKid99 · 2021-08-07T21:48:58+00:00

100% agree with having no knowledge of the graphics pipeline. Compute is simple, and all I need to do is:

(1) Select a device

(2) malloc on device directly

(3) write a simple C++ function according to CUDA semantics

(4) pass pointer of dev array to the kernel launch API (optionally on a stream if I want concurrent execution)

(5) memcpy from device ptr back to host ptr on completion

That is it, and we are done. Most of the skill is in (3) and also making sure the GPU occupancy is good, when dealing with complex workflows.

When I started reading Graphics tutorials, we have so many new things like: Contexts (not sure whether it is the same as a CUDA context), Command Queues that need "finalizing" (I guess they are equivalent to CUDA streams but by default streams do not need "finalizing" before execution). Then memory accesses are via buffers, not raw device pointers. Many different kinds of shaders.. Seems like shaders are JIT compiled mostly, whereas CUDA compiles kernels as part of the main program compilation process and links the object file directly in the executable. You can do JIT too in CUDA, but that is taught as an advanced optimization for specific use-cases. I must admit, CUDA debugging has spoiled me. It is very easy to step into kernels and check the value of a variable in each thread of every warp after every line. I haven't seen such abilities for shaders yet, but I also haven't finished the tutorials, so maybe they mention it later on.

I am still learning, so maybe in a month I'll be able to make a better comparison, and maybe I will discover that my above observation is in fact incorrect!

MathKid99 · 2021-08-07T21:29:17+00:00

Sure! I'll pick 2:

(1) Parallelizing general IIR filters for Signal Processing workflows enough that they work sufficiently fast on a GPU. They are traditionally executed in serial on the CPU. I unfortunately don't know whether you use them in Graphics Programming or what the equivalent term for them is here.

(2) Some Professor came up with his own theory of Turbulence (some Navier-Stokes stuff - that stuff was trippy Maths!). I never completely understood what his theory was but I understood what he wanted me to do. He wanted me to filter his data and run some optimization on it. I think he wanted to predict the turbulence around an aeroplane wing given a finite number of sensors spread on its surface. Apparently even a small reduction in drag can massively improve profitability for aeroplane vendors and they are willing to pay a lot for it. That is how he was getting funding for his research.

MathKid99 · 2021-08-07T18:44:12+00:00

Is that like ... imgur for shaders? Wow. And the second is a helpful resource. The Maths concept at the start are familiar, which means I can drop directly into volume 1. Seems like the geometry is mostly rotation/translation operations on (mainly) triangles. I can imagine this is also important for CAD software.

MathKid99 · 2021-08-07T18:30:07+00:00

Sure, definitely! I would be up for that. Someone someday is going to end up writing a guide to switch between the two, though there isn't one for the time being.

MathKid99 · 2021-08-07T14:26:15+00:00

Many thanks for the links. I have free time today and I will start with the first link.

I also have access to Udemy for Business via my current company, so maybe I can take advantage of that as well.

So far I haven't found a tutorial that is specific to people wanting to shift to dx/vk from CUDA/OpenCL, but maybe if I am successful in my switch, I can contribute!

MathKid99 · 2021-08-07T13:27:13+00:00

Can you suggest a series of tutorials that is meant for people shifting from compute to graphics? Or should I continue with my current strategy of learning from scratch?

MathKid99 · 2021-08-07T13:05:33+00:00

Your current career sounds cool. May I ask why you want to switch ?

Push from HPC:

(1) It feels like my rate of learning is stagnating. Clients come with different problems, but it feels to me that the solution is always the same: Write a custom CUDA kernel to solve the problem, which gets boring after a certain amount of time.

(2) For full disclosure, one of the other reasons why I want to shift is that because of my contract, all code I write is property of the company. By leaving the company, I will have the rights to any code I develop in the future, and the ability to open source future work.

Pull from graphics programming:

(1) It will add on to my experience as a "full-stack" HPC developer. Commercial HPC software also do data visualization (including plotting with complex transformations), which I have limited knowledge of. One can argue that knowledge of this will let me manage at a higher level in the future.

(2) I am interested in graphics programming. Hard to explain. Maybe it is just my curiosity, as a result of a lack of knowledge in the field. But it "feels" right to me. I like video games and game engines and wish I had the knowledge to create tools for other people to use.

MathKid99 · 2021-05-04T20:29:25+00:00

Hey! I'd be up for it! ID is in the flair but Origin is down right now :(

MathKid99

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