🥇

ZLUDA be like:

yes mom, I need a 4090 to run CUDA.

Damn you really put that rtx4090 through hell

So this is sponsored?

Underrated comment

He ran the seconds in parallel with Cuda.

Serious question, why are these videos never 100 seconds?

Because it's just the name of the series. A catchy title, really. I don't think anyone cares if they're exactly 100s.

To be fair, he explained it in 90 seconds, the rest is building an app.

Like PyTorch and Tensorflow

"model.to("cuda:0") is the only cuda you need to know unless you're developing new algorithms or doing something truly wacky

some (x) mostly (o)

yeh thats why pytorch and tensorflow exist, i have parallelism and HPC both this sem, writing openmp and MOI codes, truly a pita

There are a few geniuses who write libraries and then there are thousands of devs who build products out of them....

And so is Apple.

Dell in the server space too

Cisco as well

yall forgetting about aws? 😂

No, Nvidia is an open computing platform dedicated to the development of democratized development and open standa--- Pfff 🤣🤣🤣 hahdahha!!

fr, this 3 seconds animation was better in explaining it than most other explanaitions, and he didnt even spoke about it really.

The best thing that had ever happened to me was figuring our what matrices actually represent (a linear transformation) and I've been able to do matrix multiplication without any memorizing simply because its just intuitive now. Try this also because schooling has failed us

I think that is taken from @3blue1brown, @Fireship ??

​@@alvinbontuyan8083 can you give a quick example on what you mean with this? I'm not that smart, thanks!

lmao fr, those 3 seconds are extremally helpful

Yes, my eyes got wet when I saw that

Mom be like: I am proud of you, my son

Wait, where?

Where? What did you watch in this video then, lol. @@kamikaze9271 Here: 1:45, 2:53

​@@kamikaze9271 2:52

When you use TFLOPs, is it single precision or double precision? Because I see double precision here.

Gives me PTSD from my master's thesis. Had to modify 4 flags in clang to get acceptable results. Took me a while to figure out.

​@@cloudytheconqueror6180Single precision. Double precision is often much slower, though the rtx 4090 is just able to get into the teraflop range for f64

Which college and course?

The teacher probably sucked like most academic teachers. If you had fireship it would be a hundred times easier

I hope that was graduate level, cause otherwise that is horrific

I would argue that people were not really "designed" to think in any specific way... neuroplasticity for the win... same way that most programmers can think of code. Practise makes perfect.

@@duckbuster1572 It's common for it to be a course in your last year of undergrad... I dont see why it would be horrific.

AI is definitely worth mentioning.

​@@2099EKPlease, can we just don't? Physics models (for example) are much more interesting (in my opinion) than curve fitting on steroids. (Just a matter of avoiding a cliche and showing a greater range of GPU computing applications)

​Why, fitting so much complex curves that reflect reality is indeed worth mentioning ​@@upolpi3171

It’s more like zero minutes 😂

@@anon8510You're literally on a technology channel, you Twitter drone.

wait until he drops some existential crisis type content lol

thread count is not depended on GPU model (max 1024 threads per block), total block size and number of cores are depended on number of SMs and cuda computability.

Sounds like the "fun" was actually "fun boilerplate but it's still just boilerplate". Correct? Or... are you being _purely_ sarcastic?

@@BrahvimBoth actually. It was fun in the beginning, but with more complex projects/tasks it became harder to understand how to use it correctly (espeically kernel launch configs with the dimensions, etc). Mabye, with more experience, it would be easier for me today than it was at that time. But don't get me wrong, they also showed how to do the same thing with OpenCl and the amount of boilerplate code for this to run was way more than with Cuda. And when they allowed using thrust for the final project, most of the boilerplate code was gone because thrust abstracts that away. It was more fun to work with an API that offers host and device vectors and a standard library for common tasks. But, thrust also abstracts away the launch configurations for kernels etc, so you loose control (which was fine for me because I struggelded with the more advanced concepts). But I guess you will loose some speed/memeory effeciency like with all abstractions.

@@KoaIa200you are right. I am sorry. The more advanced kernel launch configs with block size etc was quite hard for me and I haven't used Cuda in years now. But I remeber struggeling with the concepts after the initial easy tasks

@@BrahvimNo, it actually was fun, but it is also hard. And if you compare to OpenCL it is actually much much less boilerplate code. In the beginning, exercise were quite easy but with more complex tasks, it became much harder. For the final project we were allowed to just thrust which is a library that makes things much easier. E.g. it provides host and device vectors and it also handles all boilerplate stuff. However, you will loose control because it is a abstraction and probably some speed. But today, if I would need to do Cuda again it would be with thrust (at least in the beginning)

Can I read your master's thesis?

same , LMK when you get it@@klekaelly

Could you do it in 192 seconds??

Really, I thought Opencl will do this just fine. Funny thing is ALL GPU's are designed to be parallel computers and AMD in actually more massively parallel than Ngreedia. He didn't describe anything that is just cuda specific, did you really not get that when writing your thesis?

@klekaelly thank you, but it was on cuda version 1.0, which is really outdated from both software and hardware perspectives. Furthermore it is not in English. But I really appreciate your interest!

Yeah man, he monitored your web traffic, saw that you wanted to learn about cuda, and then made this video as fast as he could since he knew you would watch it.

Now I'm scared about tomorrow's video

I was thinking to learn about CUDA. He is a mind reader

That's classified.

literally doing an homeword in cuda rn

yeah, Nvidia dominates in parallel computing because software engineers only know CUDA.

@@vladislavakm386 You got that backwards, but ok.

Who asked you?

@@etrestre9403 me?

​@@etrestre9403 Not allowed to speak their mind?

@@Mkrabs yeah I was just wondering who asked them

@@etrestre9403 sorry for your mental illness

no, complier

Gotcha moment😀

Marcomplier

I did not expect this... I'm calling Miguel.

Yet misses the fact this is NOT cuda specific.

Or your lecturer set you up well to follow this very basic, high speed summary. Like a reader of the LOtR series can see meaning in the film series' long, dreary shots.

Can I see the course?

Yeah, you can probably keep on dreaming about that. Memory management is the primary contradiction that you must solve if you want your CUDA program to go fast. Either you need to get all of the data in the register file / shared memory or you have Too Much Data and have to do horrible things and maybe even have some of that data out of core and it will go much slower than it could. There's no cache coherence protocol so if you need it you have to move things around manually and do some synchronization. Fun stuff.

💀💀💀

Yes when you center a div in CSS, the browser uses your GPU for rendering the pages on your browser

center div exit vim I use arch btw hmm yes, very original "I've been programming for two weeks" joke

Which CFD software has CUDA acceleration? Just Ansys Fluent right now right?

@adialwaysup8184 not really, we performed some testing on A100s and H100s and offloaded omp was WAY slower. Sure it's portable, but acc is still getting love. It's also syntatically easier and cleaner in my opinion.

@jaiveersingh5538 take a look at research code. Nek5000 uses CUDA, and as well as NekRS if I remember well. Our own code started as CUDA Fortran but we eventually moved to OpenACC. Easier to use and explain to other users. Quite a few libraries behind research soft also uses CUDA, or even OpenCL. For matrix free SEM methods, CUDA might be a bit hard to implement, but it's as fast as it gets.

@@lucasgasparino6141 For us, omp was performing 2% slower than acc and 6-8% slower than cuda. Though, the performance was much worse on clang than nvhpc

@@lucasgasparino6141 In my experience, currently, there's a major discrepancy in how well a compiler optimizes code for accelerators. The is doubly important when it comes to nvidia, since the nvptx backend is far from perfect. But if the same tests are done on nvidia say with nvhpc. I found an overall 2-3% gap between openmp and openacc. I do agree with your second point, openacc is much cleaner to write and integrates well, but at that point you're backing up in a corner with nvidia's hardware. Openacc might be an open standard, but no one except nvidia gives it a serious consideration. If you're going all in with nvidia anyway, why bother with openacc and just move to cuda.

He didn't get paid for that.

id love to see that

Savage comment 😁@@noanyobiseniss7462

OpenCL for the win! Same performance as CUDA, yet runs on literally every GPU from Nvidia, AMD and Intel.

Hardware videos 💀

I mean he explained how to get started with it and clarified how it's different to programming on the CPU. Also I'm pretty sure the > syntax is specific to CUDA so you wouldn't be able to just run this anywhere. And GPUs in graphics are usually just dealing with essentially a 2D array of pixels rather than 3D like here.

@@Aoredon AMD's ROCm also uses the > syntax and I kinda agree with OP, this would've been good if it was titled "GPUs in 100 seconds" but as things stand it's hardly anything CUDA-specific

This is a summary channel, not overly detailed.

Correct and well said!

The extension of the file was .cu tho

If conditions are usually translated to compute discard. But they give false appearances, and also if the if condition is difficult to compute that adds to the runtime cost.

warp divergence does not matter if the other threads are doing nothing in the first place... just dont have if else and you are fine.

Better add those bounds checks, don't want to crash with access violations...

@@starsandnightvision not a native speaker but ty for pointing it out

Yes, but where is the fun in that

@@yogsothoth00 If you think that is fun you would probably get hired by Nvidia to write more libraries for them

He did a 100 seconds video on PyTorch. So, he probably expand on this too. This video is specifically about CUDA.

Yes , if you can install them and find the right version

So basically: "when writing code one uses libraries." Thank you Capt. Obvious.

You must be new here

Which is what he should be making a video on but you don't get free 4090's for that content.

@@noanyobiseniss7462 NVIDIA doesn't wanna let go that sweet sweet monopoly type proprietary stuff.

@@demonfedor3748 Pretty anti competitive company that bleeds users dry. I have no clue why its userbase is so filled with gaslit fanbois. I guess it comes down to the misery likes company mantra.

@@noanyobiseniss7462 Every big company wants to get as much profit as the next guy. NVIDIA does it through proprietary stuff, AMD does it by open standarts to claim the moral high ground. Pros and cons to each approach but the goal remains the same. NVIDIA has a lot of fans because they innovate a lot and are trailbrazers in multiple areas. Real time hardware ray tracing, DLSS, G-SYNC, frame generation, GPGPU aka CUDA, OPtiX, just to name a few. I know most of this stuff is proprietary and/or hardware locked but it's still innovation. I don't mean that AMD doesn't innovate. Mantle that subsequently led to Vulkan was a big deal, chiplet GPU and CPU design, 3D-Vcache on CPUs and GPUs, SAM. There's no clear winner, however NVIDIA is currently performance king. Intel wants in the game for over 15 years but they got big shoes to fill. Was a big blow when Larrabee failed.

@@noanyobiseniss7462 Every big company wants to get as much profit as the next guy. NVIDIA does it through proprietary stuff, AMD does it by open standarts to claim the moral high ground. Pros and cons to each approach but the goal remains the same. NVIDIA has a lot of fans because they innovate a lot and are trailbrazers in multiple areas. Real time hardware ray tracing, DLSS, G-SYNC, frame generation, GPGPU aka CUDA, OPtiX, just to name a few. I know most of this stuff is proprietary and/or hardware locked but it's still innovation. I don't mean that AMD doesn't innovate. Mantle that subsequently led to Vulkan was a big deal, chiplet GPU and CPU design, 3D-Vcache on CPUs and GPUs, SAM. There's no clear winner, however NVIDIA is currently performance king. Intel wants in the game for over 15 years but they got big shoes to fill. Was a big blow when Larrabee failed.

I doubt AMD will pay him a 7900XTX to do it.

Honestly, it's a rather low-level API, so it CAN get excessively complicated. That being said, you'd mostly use the basics of CUDA, and complexity would come from making the algorithm you're trying to implement parallel itself. Of course, the real magic is that you can optimize the SHIT out of it, I.e. overengineer the kernel 😅 but yeah, trust me when I say he covers only the intro bits about CUDA, this thing is a rabbit hole.

"Data scientists don't use CUDA, they use CUDA" :D

​The guy above you doesnt knows what the word abstraction means lmao​@@el_teodoro

@@el_teodoroor rocm? or vulkan? or metal?

lol, holy wow this really is a noob channel

Getting it to run the "official" way, from Visual Studio, is not much of a problem. Now, getting CUDA-related libraries in a Python environment to correctly run neural networks - THAT's a challenge. Especially with how much of a bother Conda is.

Lots of ML stuff doesn't have good support on windows. Probably good idea just to run an Ubuntu VM if you plan to do much locally.

What course do you offer

@@acestandard6315 where do u study?

@@SalomDunyoIT Nunya University

Leaving a dot here for a captain to show up.

I also would like to know this. Anyone?

Your not too bright are you.

Google ZLUDA my friend ...

I wouldn't recommend nvidia to anyone, their CEO is crazy!!

And the alternative is what? Hospitals, the garbage collection, fire departments, etc aren't open source either, but you're kinda forced to use them. Nvidia has got us all by the balls. Your balls are firmly placed in Nvidia's hands. God speed your efforts to come up with a freedom alternative.

@@MrCmon113 the alternatives exist! In case of CUDA, OpenCL is the alternative that works on all GPUs. And in case of gaming, AMD cards preform very well (and their drivers are open source)