Hi, and welcome to the Neil Ashton Podcast.
In each episode, we explain some of the fascinating ways
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episode. Hi, welcome back to the Neil
Ashton Podcast. I thought I'd do something
interesting today, which I was.
I was looking back at the previous episodes and I saw that
almost a year ago today I did an episode called the Future of
CFD Five key trends to watch. It was season 2 episode 2 and I
thought I will try and do another version of that and
basically see a year on, was I right or was I wrong?
So what I had as the five were the rise of GPUs in CFD, AI and
machine learning, shift to cloud computing, digital
certification, and I think I had the last one which was mergers
and acquisitions. OK, Now this was before I joined
NVIDIA. Oh, I should just say.
So obviously NVIDIA's you know, creates GPUs.
So the rise of GPUs in CFD. Can I answer that while being
completely unbiased? Probably not, but it's true.
Basically, there has been an unbelievable rise in GPUs for
CFD. I.
Can say that even if I wasn't working for NVIDIA, I think
people in the community know that to be the case.
Has that accelerated in the past year?
Yes, most definitely it is. Yeah.
Just the number one question. And again, I know I work for
NVIDIA, so I'm going to say that, but it's true.
I go to conferences and I see it without me even talking to
them. You know, they're showing slides
saying, OK, it's 10 times faster on a GPU.
I did not get involved in that study whatsoever.
I'm just sat there listening to it.
So it it is absolutely the case.
And as anything there was, it was slower at the beginning.
People were like, OK, is this really true?
You know, is this marketing? I think now there's been so many
independent studies showing it that people are fully getting
behind. And I think what's also made the
difference is 1, the hardware's obviously keeps
getting better and better. The pace of GPUs, because
obviously AI has helped. This is far out pacing.
CPUs in terms of like generational generation, you
know, like the improvement that you get each generation is, is
like this compared to CPUs, which is, you know, a bit
slower. So you're, by moving to it, you
keep getting better and better. So the reason to move has
probably accelerated. I think the, there's been a
healthy competition between different CFD companies each
seeing GPUs to be part of that differentiating thing.
And that has created a, a sort of an acceleration, you know,
because there's one vendor releases a GPU version, everyone
wants to make sure they also have one.
So there's been a little bit of that, but for good reasons.
It's not purely, you know, just for the sake of it.
It's because people are genuinely seeing a speed up
customers liking it and and therefore they're doing it.
And I think most interestingly, which is I think something I
commented on is there's a lot of start-ups now who are
specifically focusing entirely on being GPU native.
And that has also definitely created a shift because no
surprise if you write code from scratch with a sort of
software–hardware co-design, you're going to create something
really good. And so because of those
start-ups, they have shown in some ways the like most optimal
solution and has really shown some very interesting
performance. What I find more at a technical
level interesting is where the whole like unstructured implicit
finite volume, which I guess was the industry standard does
that. Is that the most optimum
numerical approach on GPUs? It could be, but because they're
so much more powerful and because of that, you can start
to do more transient high fidelity LES, then people are
going, well, if I'm going to do an LES and I'm going to have to
run lots of time steps and I've got this GPU, should I maybe
now go back to explicit that previously wasn't maybe the
optimum way of doing it, but now with the GPU it is.
And then maybe could I do a, a wall-modelled approach if I'm
doing a wall-modelled LES, and does that mean I should maybe
use the immersed-boundary method because I don't need to
resolve the wall? And so that it's this interplay
between the hardware and the software is creating some really
interesting changes. And and finally, one of the
interesting was a high order side, You know, I think high
order, without going into too much of the technical details,
there's some interesting stuff around like tensor cores,
etcetera. So the whole high order thing is
coming back in to play. So yeah, was my prediction of
key trends for CFD correct, GPUs and CFD, I'd say yes.
And is it still for 2025/26? Definitely.
It's only accelerating and I think you'll find that it
continues to accelerate whilst there is so much more learnings
to be had in terms of optimal architectures.
And there's like a crossover point when your compute gets
higher, you can go to a different modeling paradigm.
So for example, and not wanted to speak about this for too
long, you've gone from, let's say a wall-resolved RANS, like a
low-y+ RANS, where you wanted to resolve the wall and where
you need a certain gridding strategy to be able to resolve
the wall to a wall-modelled LES where you say, oh, well, I don't
need to resolve the wall because I'm modelling it.
So maybe I can go to a different gridding paradigm and I can go
to explicit. But if you go to the next stage,
which is the wall-resolved LES, that maybe say, well,
actually now my gridding paradigm that was good for
wall-modelled LES may not be good for wall-resolved LES.
Maybe I need to do something slightly different.
So it's interesting how these waves happen and the, like I
said, doesn't interplay. So what I find most interesting
is a year ago or two years ago, the most bold predictions was
more towards hybrid RANS/LES, wall-modelled and everyone said
oh, wall-resolved is too far away.
I'm actually seeing now people be a bit bolder and say, well,
you know what with the latest GPUs, maybe wall-resolved LES,
it started to become possible. And that's interesting.
I think not for everyday applications perhaps, but now
that that is coming up, some other stuff starts to get
interesting like just the sheer number of time steps you need to
do. And yeah, so still important.
OK, AI, machine learning. Well, duh.
Yeah, that's clearly still a key trend.
Has things moved up in the last year?
Absolutely. I'd say one of the big
breakthroughs I personally seen is probably a year ago or a
little before a year ago, one of the bottlenecks seemed to be
volume prediction using AI surrogates.
Stuff was coming out around like graph neural networks and being
able to predict things, but all the examples were always on
smaller cases. And one of the things that I did
with some colleagues was to create that DrivAerML data set,
which was really put out there as a data set to give
realistically sized meshes and hopefully encourage people to
see, right. Can my method take the entire
volume and a year or certainly two years ago, it looked like
not many people could do it. Everyone was just doing the
surface or maybe a few slices and every time you saw
something, you know in marketing online, it was always like the
surface of the car or a slice of the car.
What I found was a breakthrough in the past year is that various
methods have come out and I guess the most, most most recent
will be like Transformers that seem to be the most
computationally efficient. Efficient for doing full volume
predictions as in training on a full volume and then doing an
inference and being able to get a full volume out.
I feel like that really accelerated the realization that
some of these AI methods could give you back a flow field that
was more similar to a traditional one.
And yeah, that transition from from graph networks to maybe
neural operators and Transformers for me has been a
really interesting 1. And I'm certainly excited to see
what happens in the next year. I think we'll continue to see
new architectures, new evolution of, of AI.
So that is definitely a trend that's continuing.
Yeah. And they're obviously linked,
right. So if you have a faster solver,
you can generate data more easily.
You can then do the training. I think one of the big
discussions now is on the right code architecture.
Sorry, like programming environment, because online
training is definitely a hot topic.
You know, how can you take advantage of the transient
nature of the flow? So at the moment, I'd say the
state-of-the-art is normally that you would just generate
those data and even if it was with a hybrid RANS or
wall-modelled LES, you'll just take a time average and dump
that and train on that. But I think what's emerging now
is this interesting, well, can I use some of the transient data
that's being created anyway, you know, dump them out at
checkpoint and train on that. So I think the whole AI will
continue and continue. The one thing that I would
probably add to that AI bucket is the agentic AI side.
So let's say you've got surrogates that's in one market
and you've got LLMs for just general productivity gains and
coding on the other. I put agentic AI somewhere in
the middle, which is how can you have multiple agents working
autonomously through some sort of master agent that can go off
and do tasks for you. So instead of you having to
manually click run this simulation or write a Python
code that goes and runs, you'd said, give a text prompt saying
I would like to go and run this simulation with these parameters
and please create me a report. And you'll have a workflow where
an agent will interpret that request will then go and let's
say create a simulation setup for you.
But crucially also then run the simulation, let's say an HPC
cluster, use a separate, you know, agent could be a surrogate
model essentially, you know, to go run it or to do the
meshing, then another LLM, maybe do the analysis of the
results. And the point being is that you
have to kick that all off with a text prompt.
That is the most cutting edge example.
And that's what I feel is probably in the next 12 months
will be the next big jump. And some start-ups are already
doing it as some companies, but I feel that's probably one of
the big next steps. I should add, by the way, for
GPUs I didn't mention, I think precision is a very interesting
topic now. I think there's huge gains if
CFD can use lower precision for for obvious reasons, you know,
that there's so many FLOPS in in the lower precision because of
AI generally only needs, you know, FP8 or FP16.
So it doesn't mean that you can't do 64 or 32, but if you
can find a way to harness that power, then you can really
unlock the next big accelerator.
So the first one I had was the shift to cloud computing.
Now, you know, people who know me or listened to this before
know that you know, I currently work from NVIDIA, but I used to
work for Amazon Web Services, which is obviously a massive
cloud computing company. And there is no doubt whatsoever
that cloud computing has totally transformed the IT
sector and the HPC sector. When I left AWS.
I definitely saw an increasing momentum in people adopting the
cloud cause of the fact that they could essentially focus on
the stuff that mattered to them and not on the sort of
manual data centre stuff. I still see that increasing, but
probably the bit that maybe I was not brainwashed because I'm
joking aside a little bit. But you I definitely, I'm seeing
I'm still a more hybrid sense where on one hand people are
absolutely seeing the value of the cloud, you know, through AWS
or GCP or Microsoft or you know, all the cloud players.
And by the way, there's an interesting angle now with all
these neoclouds like CoreWeave, etcetera, which is mainly I
guess to the AI and GPU angle, but I'm still seeing a hybrid
use. You know, there's still quite a
lot of on Prem, there's cloud and and I still think there's a
lot of room for improvements in the way to access cloud
resources. You know, I think SaaS from a
CFD point of view now SaaS products are still not fully
there. You know, if you ask most people
are they doing it software service?
No, they may use the cloud to have a cluster, but are they
doing it in a true SaaS like manner?
I think that it's slower than I thought people still seem to
prefer and there's good reason. So we'd have to get into of
having a local experience in that you're not just doing
through a web browser. And I think that's probably just
decades of engineers being used to just SSH-ing and stuff
and and having like, yeah, running scripts.
And it's something that you think would change, but it, it
does seem remarkably stubborn. And yeah, so I, I definitely see
the cloud continuing, but I'm still waiting for some company
to come out there and really make it easy from a workflow
point of view in like a true hybrid way or to just simplify
maybe the cloud experience. You still have to be quite an
expert to do it. So I am, I'm not changing that
prediction. I'm just seeing that it seems to
be a bit slower than I fully appreciated.
And maybe in hindsight, that's because I was seeing all of the
positive and success stories at AWS and maybe I was not hearing
the bits where people weren't, you know, doing it.
The 4th 1 I had was on digital certification through
higher-fidelity methods that, as I mentioned on the 3rd of it is
definitely the case. You know, you're having a huge
increase of people going to higher fidelity methods because
of #1 you know, the fact that you can access to compute more
easily. And that is definitely a topic
that I think will continue to increase sector by sector.
So the automotive has definitely pioneered that and all have
moved to higher fidelity methods.
I think now, like I said, they're, they're looking how can
I go from hybrid RANS to wall- resolved LES.
I think the aerospace sector is probably still trying to get
from RANS, you know, to hybrid RANS or wall-modelled LES and
the work that you know, we did in the last High Lift Prediction
Workshop has definitely helped to push that forward.
And there's some interesting work going on now to try and
push it even further again to like wall-resolved LES.
But I see that continually, you know, increasing and now it's
more around building up best practices and understanding of
those methods, you know, robustness that everybody knows
where they go wrong, where they go right.
So it probably will take time for that and for people to see
the value because it is undeniably more expensive to do
with the low fidelity. So I think there needs to be
success stories that have been created.
But I think probably one of the biggest challenges is
transition. Certainly I'm speaking more from
a CFD and external aero point of view, but one of the
challenges still with even going to wall-modelled LES to
hybrid is transition modelling.
And I, I feel that that's why the wall-resolved LES is still
very tempting because it doesn't fully solve it, but it, it
definitely helps to overcome some of those challenges.
So I think that will continue, but I would look more at how do
we make wall-resolved LES approaches more, more
affordable. I am going to be doing an
episode where we're going to talk more on the combustion side
of things. I do realise that this podcast
just tend to be a little bit focused on external aero.
That's basically my background.
So I am purposely trying to, yeah, diversify a little bit and
make sure that we're covering other areas.
And the final one was mergers and acquisitions and
innovations. And this has definitely kept
going. We have seen Ansys and Synopsys,
so two big companies merge. We have seen, you know Siemens,
Altair, Cadence, BETA CAE. So this has kept going and was a
trend that I predicted and is still true today that there is
so much interest in the community and engineering and
semiconductor businesses that, you know, it's a growing
business, really important. And big companies are realizing
the value of acquiring or merging with others to, you
know, bring some of their technology in and speed up.
You know, that that time. I think that will continue.
I would say the prediction for next year, it's not there's not
many other big companies that can merge now.
You know, it has consolidated a little bit to Cadence, Siemens
and, and Synopsys, but where I do see that being definitely
move is there's a lot of start-ups and I'm sure that the
many of those start-ups will be acquired by some of these bigger
companies. So they can start to really
bring those to a more enterprise level and integrate it.
So I, my prediction would be in the next 12 months that some of
the start-ups that you, you know, you may know of, I won't
mention all names, but the start-ups, you know, I think
you'll see some of them being acquired by high profile
companies just because that is, that's a tried and tested route.
You know, when I spoke to the
CTO of Ansys and he openly admitted it, he said, that's
what we do. We look for great start-ups.
We may even, you know, encourage them.
And once they reach a certain level, we might acquire it.
And it, that's a great way of developing new technology.
Sometimes it's better to let a start up do it.
You have a, you know, faster way of doing than a, let's say, a
big enterprise. But then they acquire them, they
bring them, and that's how customers get to use them.
So I think that will also still continue.
So where do I think things are going?
Well, I think actually those five topics will still continue
to be a thing for 25 and 26, and I'm excited to see how they
accelerate. So I hope that was interesting.
I thought I'd keep it short. I know these podcasts tend to go
to an hour or two hours, so I thought I'll keep this one short
and got a couple of really good guests coming up for the next
two episodes. So yeah, look out for those.
And for now, I hope you enjoyed this episode.