Hi, and welcome to the Neil Ashton Podcast.
In each episode, we explained some of the fascinating ways
that science and engineering are changing the world around us.
We talked to leading engineers from elite level sports like
cycling and Formula One to some of the world's top academics to
understand how fluid dynamics, machine learning, supercomputing
are bringing in a new era of discovery.
We also hear some of their life stories, their career advice,
the lessons they've learned on the way that I hope will be
helpful to you too. So sit back and enjoy this
episode. Hi, and welcome back to the Neil
Ashton Podcast. So today's episode is just me
and I wanted to go through what I thought were five of the top
important trends in CFD, let's say over the now and over the
next 5 or so years. I'd love to know whether you
agree with what I'm going to say.
These are sort of more, I guess, high level trends rather than
getting into the, you know, specific details.
Very happy to do an episode diving a bit deeper into one of
these, but I thought I would maybe stimulate some discussion
and get you thinking about some of these.
And I have to also give a, a bit of, I think that I'm coming from
a certain angle here. I guess my experiences in
everything I've done will bias me.
And I just want to admit that that bias.
I come more from an external aerodynamics background in terms
of CFD, you know, I currently work for a cloud computing
company. I'm obviously going to see
things through the conversations I have and the lens I see, but
I'd like to think that I see enough of what's going on in
different sort of verticals and and different areas that gives
me a reasonable understanding of what's going on.
So yeah, let's see if you agree. So yeah, five things that I
think are important for CFD over the next five years or so, not
necessarily in chronological or like in in important order, but
number one for me is GPUs and based processes.
Why do I say this? It was the case when I let's say
back in 2013 or something like that, I remember going to a path
CFD conference. So this they still run parallel
CFD. It's a great conference to go to
sort of that blend between high performance computing and CFD.
And I remember there like 10 years ago, this was at Barcelona
Supercomputing Center, really great venue right on the
Ramblers sort of strip. Anyone who's listening who went
there will know it was great conference organised by the
Barcelona people and I remember seeing presentations showing
about GPUs and I can't believe that was like 10 years ago now.
And there was a real sense in the room of I remember someone
said that you can only get good performance out of it if you
write the code from scratch. I remember someone saying that
and there being a bit of a debate and I remember there
being sort of sensational claims, you know, that GPUs are
this much faster and, and a little bit of a, oh, you know,
ignore them sort of thing. And obviously at that time
NVIDIA was doing a lot of work to invest in in CUDA and sort of
giving funding to research groups.
And I remember seeing this and yeah, it, it, it was, it
registered on my radar. But a lot of people were, were,
were debating whether it could be possible, whether there'd be
enough memory on the GPU's. Was it really possible to
rewrite a code? You know, people said they had
millions of lines of code. I'm never going to be able to do
this. Fast forward to now and I would
say for me it's incredibly clear that GPU's are going to be the
the dominant computing platform for CFD.
But as I'll say later, things move very slowly, especially the
industrial CFD. And so the adoption is going to
take time. But I have seen enough to make
me believe that and I'll give for several reasons.
One, there are published publications out there that show
it. I've certainly showed it in
public settings and it's the Isvs and software companies have
done it. And in fact just for auto CFD,
this automotive workshop that that I created back in 2018 in
19 with Gary Page Loughborough and now it's in its fourth
edition. I think the slides are now up
and the video is on the website. So if if you go to autocfd.org
and you Scroll down and you go to the HPC presentation, Herbert
Owen from Barcelona Supercomputing Centre did a
great job of taking everybody's submissions from CPU and GPU and
then looking at what type of GPU, what type of CPU, and doing
a really good, good job of looking at several different
metrics. So the speed, you know, per
degree of freedom, per time step, looking at the cost.
So we took a sort of a notional cost from AWS.
It could have been any cloud provider.
It's just, you know, it's the only way of getting an actual
price for these things because obviously the cloud gives you
the all in cost. Whereas if you were to find a
website that would give you just the sort of the GPU or the CPU
that wouldn't be the actual entire cost including the power
and the the network etcetera. And what it showed is that for
external aerodynamics of cars, GPUs were more cost effective,
at least 23510. It depended on the code or even
more cheaper than an equivalent CPU at the scale that it was
being one for a realistic problem, not sort of toy
benchmark problem. And they were faster, but that's
a slightly harder one because it depends what the parallel
scaling of the code is and and you know, how many CPUs or GPUs
do you throw at it. Whereas the cost, you know,
obviously it's going to change a little bit on linear regime.
You know, if you're really badly scaling, the cost is going to go
up relative. But essentially what it showed
was the cost. Now I have repeated this same
thing myself personally with startup codes and ISV codes.
And I have seen the same thing, which means that for a certain
classification of workloads and I can only speak what I've seen
and personally, so I'll speak more, let's say in plain design,
car design, but I've seen it and I believe it to be so in other
areas. It is.
You're ultimately going to run your simulation cheaper on a GPU
than a CPU. So forget about speed, it's
going to be cheaper. And why is it cheaper?
Ultimately, these are things like power efficiency, energy
efficiency. So even though the GPU looks
more expensive to buy for one node, when you actually look at
how long does that node need to run for, you know, it's easier
like I said, to calculate with cloud because it paid, pays you
good pricing, it is cheaper, and as soon as something is cheaper
economically it makes sense it is going to gain widespread
industrial adoption. Now where's the new ones to this
is what GPU there is in some ways a divergent, well, not
divergent, but there is a, there is a market for machine learning
GPUs with a particular focus on, you know, certain precision,
lower precision ultimately than what most CFD codes would need.
And so you're going, you know, from A1 hundreds to H1 hundreds,
H2 hundreds, etcetera to Blackwell that are driving up,
you know, more and more performance.
And CFD absolutely can take advantage of that performance.
And it's great because there's a, we can jump on the bandwagon
essentially of machine learning. But there is also another
classification of cards that quite often are more designed
for inference, but these sort of L40SR TX type cards that
actually have a very, very good price performance.
So that raw performance may not be as high as those sort of more
top end GPUs, but for CFD, they actually are often when you do
the maths, even better price performance.
And that's what we showed of this, this auto CFD.
Now the reason I'm saying that I think the GPUs will become the
main is because if you look at the trajectory of GPU
development in terms of generation after generation
improvement and you look at the same on the CPU front.
I think it's hard to deny that again, because of the investment
from machine learning, there is a much greater boost in
performance from generation to generation on GPUs than there is
on the X86 CPUs. And the fact that there is
rising competition now between NVIDIA and AMD.
And also maybe there's room for more, you know, startup or
emergent technologies that's only going to drive more and
more focus. Now, I say ARM 64 as well
because that has emerged as a, at first maybe more of a niche
architecture that was harder to get people to convince.
And I know speaking from being AWS, we have our Graviton ones
and you know, we struggled for people to think, well, why
should I bother to pull my code, you know, to, to harm.
But now with more and more companies with, you know, the
other hyperscalers also releasing obviously with video,
with the Gray system sort of linking the two, there is far
more and there's far more reason for a major code to port to it.
And as soon as that happens, the ecosystem jumps behind it.
And again, the the power efficiency and the performance
numbers make it extremely attractive.
That's not to say that people aren't going to be running on,
you know, AMD or Intel CPUs, of course.
And as I said, the CFD market and engineering in general or
any use of CFD tends to be slow. And so if from 2013 to now
there's been that sort of movement, it's not going to
happen overnight. But what I can see is every
single one of the major Isvs has or is porting their codes.
The issue is that not all the features in the codes are
available. So yeah, some of the multi
physics, some of the options are not yet done.
So you may not be able to use the GPU version today because it
may not have the feature that you need if you're doing some
more sophisticated modeling, but it's probably on the road map.
And more importantly is the codes.
And these are more the start-ups like luminary or Volcano or Flex
compute. And there are others who are
really who have really focused on a GPU native code.
And in the case of, for example, Volcano taking advantage of GP
US like designing it for GP us Cartesian methods and there's
boundary, etcetera. And there are others as well
doing this and, you know, hiring super talented people who have
come out of universities and institutes with a deep, deep
understanding of how GPUs work. Programming it from the base
from the beginning with the latest, you know, programming
languages and and and knowledge of how to extract the maximum
performance is really only going to accelerate it further.
And I'll talk about this in the last point.
This emergence of new start up codes is also invigorating
because simply, if you look through the eyes of a commercial
company, why change? If you are the main company,
main ISV provider, why would you change?
You're earning good money from your licenses.
You are you've got customers and if your competitors are all
basically doing the same thing, so none of them, let's say five
years ago, six years ago, OK, none of them are moving to GPS.
Well, why should we spend all the effort doing?
Is it really that important? Is it really going to make a
difference? Are we going to lose customers
from it? And of course the technical
reasons, you know, was there enough memory?
Is there enough performance to gain from it?
The one thing that drives a company to change is
competition. And so with the emergence of
alternative providers, many of whom who are specifically
marketing their code around being GPU performance, it forces
companies to do it. And you've seen that with, you
know, with ancestors sort of rewriting their their solver and
you know, showing a very, very strong performance.
And at the auto CFD workshop, we saw pretty much all of the Isvs
specifically focus on their GPU performance and their speed,
which three years ago was not the case.
So everything feels like the right momentum.
Now the tricky thing is the whole ecosystem needs to to
work. So is are the third party
meshing tools going to work on GPS?
And I mention this because we get to another point, which is
around the sort of high performance computing, because
obviously if you're going to procure a cluster, if one code
works amazing on GPUs, but all the restaurant CPUs, you then
need to figure out, well, what's my ratio of compute options to
buy? And, and so this links it to one
of the third points later that the other reason for GPUs is
national supercomputers. OK, what do I mean by that?
Well, if you are, let's say, in Spain and you want to show to
your government that you as a country are pushing the
boundaries of science, of R&D, and you're attracting
entrepreneurial entrepreneurialism,
supercomputing has traditionally been a great way.
It's a sort of, you know, a big lighthouse.
And the US is a more obvious one for that.
But I'll pick a country other than the US, your supercomputer
obviously a lot of that is then based on its performance and the
top 500 list is often you know the metric like what is the
total performance of the system. You know the the sort of exaflop
race to try and get an ex scale computing.
A lot of this has driven to GPU's because the total
performance of the system is much higher for a given power
output with GPU's, and arguably it is also newer technology.
So more novelty, more differentiation can enable
better science because there's more, you know, throughput can
be produced. And secondly, even more
importantly, secondly, is because of machine learning,
because machine learning is now being integrated and is a key
research topic as well. And so if you're a national
super computing centre, you want to be able to do ML research and
you want GPU's. What does that mean?
It means that if you're an academic and you want access to
these supercomputers to do your research, like people do in
Spain, let's say, you will have to write your code to fit on the
system. And so sure enough, that
motivated researchers in Spain. And now let's broaden it to the
UK, to the US, to port their code to GPUs.
Essentially, they can run on the systems that they need to.
And so there's been a push and pull.
There's been a push because they think it is the right
architecture to deliver better performance.
Or maybe that's the pull. But there's also a push because
the system they want to run on has GPS.
So I should say that has also been the drive towards GPUs.
There is a, once a decision is made that the big supercomputer
are going to have GPUs, you almost are then having to, well,
I'll just put my code, you know, to, to go to it.
And so that's more the balance to the industrial side and the
academic side, why both are now meeting.
And again, remember, many of these startup codes emerged out
of more academic or government work where they had moved to
GPUs because one, they're just more aware of the the science.
And I would say this is what companies like NVIDIA have also
put a lot of groundwork in investing in helping people to
understand CUDA and helping them to pull their codes.
And that has now moved through into the startup.
And as I'll speak about later, almost inevitably many of these
startups are going to be acquired by the larger
companies. There's an interesting sort of
ecosystem and flywheel effect. So the next one is on AIML And
you know, again, you're, you're typically really for AIML or
you're really against it. This tends to be the case of any
new technologies. The reason I'm more bullish on
AML and that's through my own personal experience actually, at
least I would encourage anybody else on, you know, looking at
code, writing code, doing experiments, publishing,
organizing, really trying to immerse themselves.
And I would actually make it similar to the way that I saw
GPUs back in, let's say the 2000 and 10s, where again, I saw a
similar dynamic of some people really being against it and
saying, oh, it's just all hype. You know, there's no way they're
going to be able to do it. I'm not going to be able to
pull. It's not, you know, the
technology is not going to be there to others who are like,
it's just a matter of when I see the AI in a similar way, but
with some more nuances, which is what exactly can AI be used for
within CFD? Well, the first point I would
say is the commercial pull. And you know you may if you come
from a more pure background, you may hate this sort of speak, but
this is just the way the world works.
When the large ISV companies, many of them who are
stakeholders or boards to please people invest in their companies
in the current climate because of perception of how much are
they embracing and leading when it comes to innovation,
particularly around AI. And so a major ISV has to show
to their board and stakeholders and shareholders that they are
fully embracing and are doing something about it.
And I would say in some ways, many of them have had to catch
up. Now, I don't know that, you
know, working to the companies, maybe they've had many proposals
beforehand. But certainly I would say the
whole ChatGPT moment has, you know, caught some of them off
guard and made them really have to double down and decide what
to do and what and if they can release it.
That's means that there's a lot of sort of R&D and focus within
these major CFD and CE companies.
But this is also fed a lot of startup market.
So for a similar reason that many of the startups and there
are many many to list, you know the Navistos, the Physics X
beyond math. I'm probably going to insult
people by not mentioning them. I can't think I'll probably list
them more out newer concepts. Yeah, I can't remember.
There's, there's loads of them. So I, I, I'm sure I'm offending
some of them. The point is that or keyword as
well. And yeah, OK, I'll try and make
sure I mention most of them so I don't offend people.
They have got VC funding because a lot of the VCs are seeing.
Could this be the next big thing?
And so there are to my Alaska at least 10 start-ups looking at
doing AIML specifically for CFD. And quite often for things like
I spoke about card design or plate design.
The fact that they have a lot of money does not necessarily mean
that it will turn into success, but it does show that there's a
lot of concerted effort. And when there are hundreds of
people or thousands of people collectively working on this,
that often leads to advancement. And when I talk about
advancement, I typically mean in terms of surrogate modelling.
So what some people would call reduced order modelling, but
obviously more sort of sophisticated version of that
were essentially, and This is why it's particularly
interesting from an ISV or commercial point where you may
essentially not need the CFT solver during the solve step.
So you train a model on some data and then once you have the
model, you do inference and that's completely outside the
CFT code, which obviously is worrying for an ISV that thinks
we could be losing some of our, you know, potential license or
revenue or, you know, customers because they could be trying to
use this other tool that we don't have.
So and just speaking commercially now that means
those IS VS really need to figure out is this an important
technology that could transform? And if so, we either need to
develop it ourselves or we need to go and buy a start up.
And that's really the the key thing that's driving a lot of
these companies to then, you know, have a look at these
start-ups. Why economically does it work as
well is because on the one hand, the end user just as GPUs.
So, so what's the reason for the end user to be interested in
AIML for GPUs? The main reason was lower cost
computing and potentially faster computing, you know, GPUs if if
the pace continues, you know, we're going to see potential of
the next five years 5 to 10X speed up in in stuff.
So it not only is a lot faster and also talk a minute about
this leads to more like high fidelity transit methods to
become possible even in a day or less than a day for AIML.
It's that the inference can be both cheap and fast and that is
something that brings up a lot of potential for for companies.
So for example, it means that real time CFD has long been
spoken about and GPU's were sort of billed as potentially
enabling real time CFD if you go back 5-10 years ago.
But I would argue that that's not really turned out to be the
case because some problems are not.
They can be parallel in space, but maybe not parallel in time.
So doesn't matter how much compute you throw at it,
sometimes it just takes a while, particularly for transient
methods which you may need to do for accuracy purposes.
OK you could do maybe a Rand or lower fidelity, but truly real
time click of a button instantly get the answer.
You have to make so many accommodations on accuracy that
it ends up really not being worth the sort of the balance.
The AIML is the interesting bit because the inference itself
could be real time, but you could spend a lot of money in
computing time on training a model based on high fidelity
data that could, if the ML model was accurate enough, give you
quite a good answer in real time.
And so this opens up a lot of potential, particularly in
bringing CFD closer to the designer, closer to digital
twins, more into, you know, like Nvidia's doing with Omniverse,
into a sort of virtual world where you could be simulating
things almost in real time. The class example is the car
designer, you know who's not ACFD specialist who wants to
draw a car, click a button, see it, get a sense of what the drag
is and then continue iterating. Now I should say this is not in
the short term or even probably the medium term about replacing
high fidelity CFD. But I do think it has a very
potentially disruptive role in the lower fidelity conceptual
design phases that could really be important.
And so commercially, this could mean basically a key tool in the
market and this is driving a lot of start-ups because there is a
sense that there could be acquisitions or mergers from
some of the big Isvs or they could simply become big codes.
And the CA market is five, $10 billion at least and growing.
And it's growing because there is a desire for digital
certification. There's a desire to go more into
the virtual world because it's cheaper and faster than winter
testing or physical testing. So now there is still, and I
want to make this very clear in my mind, many challenges and
many areas that will most likely change dramatically in this
space. I do not believe that any of the
current methods published today will be the method that we end
up using in five years time. I could be wrong and maybe it'll
only just be iterations of it, but the field is moving so
quickly that I just find it hard to believe that what we have
today is the best we can do. Essentially, and I say this
because if you look at the CFD world, actually a lot of the
breakthroughs in terms of the physics or the modelling were
sort of done 20 years ago. You know, most of the warmer LES
or hybrid random LES or random modelling, it's actually 20
years old. And what's the novel bit is more
the computer science, the use of GPU's, etcetera.
But the actual breakthrough modelling, mathematical
modelling was done 20-30 years ago.
There's obviously some new developments, but most people,
if you're using DDS, you know, what's that 2004 or something,
depending on which version you're using of it.
If you're using K Omega, SST RANS, it's 1994.
So what's that 30 years ago? So it's a bit like going back to
1994. The K Omega comes out and it
gives good results. I'm thinking that's it.
I think we're in similar time, the AML that yes, there are many
startups and companies, but I don't think the actual modelling
or the accuracy today is anything like we'll see in the
future. And so that means there could be
a lot of change, a lot of disruption.
And that's why I think it's a really important area to get
into because there's no doubt there has still been major
issues improving scale. Can these AI methods, you know,
work on hundreds of millions of cell meshes?
Can they work in terms of the accuracy?
You know, have they really shown how accurate they are down to,
you know, a drag count difference?
There's been a lot of studies, but there's been limited real
concrete evidence of it away from just sort of marketing.
And the big question, of course, is around foundational models.
So most of what the startups and most people who talk about it
are doing is saying, we'll take the data you have as a company X
train a model, you train a model essentially, and then you do
imprints on it as opposed to ChatGPT like methods that take
huge data from the Internet. And now by paying providers,
they train a model for, you know, months and then all you're
doing is using it as inference. They've done the training and
they're recouping the cost from that through, you know,
subscription model or pay as you go through the inference.
Whereas in CFD we're basically saying you have to do the
training with your own data. The future is could there be a
way of a company pre training model, then you just do
inference. But and I'm, I, I will
definitely do a more of a deep dive on this.
And hopefully if you look in season 1, I've spoke to, you
know, 3 or 4 top ML people that, you know, I think they largely
agree that this is a super exciting space.
But there's still many questions and uncertainties around
including physics, not including physics.
And I go back to the statement that I just don't think the
methods out today are the ones that we will use.
But the breakthrough moment could be any time.
And that's why I think it's such a super exciting space.
So, so the second one is AML. And of course, why do I mention
AML? Because it's linked.
The first AIML needs GPUs and as a company if you're being asked
to train ML models then GPU is the only way to do it and
therefore it makes double the sense to procure a GPU type
cluster or predominantly GPU cluster because it can be used
for your ML and your CFD. Now the third one, which again
in I believe takes its evidence from these first 2 is cloud
computing or SAS type products. And again, I have to be, you
know, honest I, I'm clearly working for a cloud computing
company, but my belief is independent of working for, for
for that company. And I think, and I hopefully I
can give some evidence to back that up.
The reason I'm saying this is it is undeniably true that the
hyperscalers have huge investments in compute and are
able to give access to compute that most companies may not be
able to get at the size that they want and the frequency and
the access and the variety. And now there may be times when
there's a mixture of the two. And again, it's an evolving
thing. If you already have, you know, a
lot of on Prem work, then maybe the cloud for you is a sort of a
burst option or you know, a backup option, or it could be a
primary option. There's, you know, there's
different, different models and this will evolve over time.
But the fact that major, major companies around the world are
moving to the cloud because the way they see it is less of a
what's the cost of the compute, what's the cost of the hardware,
but more what can people do with it?
The point being is that there's a lot of the biggest
misconception of the cloud is that it's just service to rent.
That's maybe the way it was 10 years ago.
But now there's so many high level services added by all the
cloud providers that actually offering is not only to access
the compute, but many services that makes it easier to do
things, whether it's machine learning, whether it's, you
know, high performance computing or whether it's databases or
virtual or end user computing, many, many, many things that
makes it easier for your end users to do stuff or for your
sort of IT teams to do things that they would have to have
spent more time doing. So it's making them more
efficient. So often when people look at a
sort of a cost thing, the cost only really works when you take
the value of doing everything. And that's why many, many
companies are driving this. And the reality of what I've
seen at least is the top down is really pushing this.
So the CI OS driving this. And so it's becoming, you know,
a reality for many, many companies.
But like one and two cloud for many companies is still a
journey. And so they're not fully moving
to it. But the reason it it's appealing
is because if you want to get access to quite a different
number of GPUs or you want to test out new GPUs, the reality
is it's probably the easiest thing.
If you want to go and test GPU, it's probably easy just to, you
know, get an account on a cloud provider and do some testing.
Now, of course there are challenges sometimes with
capacity and things like that, but that's more that I guess a
transient effect. Given the still the things of
COVID and and supply chains, it makes sense to procure it
through a, through a cloud system.
And you're seeing that, you know, with, with most companies.
Now, the reason I'm sort of linking that to the others is
there was also a time when many of the Isvs and companies
start-ups said, oh, the cloud, you know, it's just fad.
But now many of them have turned around because they see it as an
easier way of delivering their software to you.
And this is where the SAS comes in, Software as a service.
Now we're used to software as a service because many
applications we use through an app, we don't, we just go to a
website and we use it. That is software as a service.
So, you know, one option used to be that you would download and
install something on your computer or you can go on to
something like, you know, over leaf I use for like writing
papers. It's a web-based platform where
you can write Latex documents and it's all stored there in the
cloud. You don't have to download
anything locally. Now that makes it a much easier,
nicer experience and it gives access to everything without you
having to have it. The analogy would be for the CFD
side is the current way of doing it is you would take a piece of
software, a binary, let's say, or open source and you get the
software from them, but you have to do all the the hardware.
So you wouldn't store it on your local computer, on your
hardware. But then what if you want a
bigger computer? What if you need more memory?
Well, you'd have to have to go and buy a bigger computer.
What if instead you could go through some website, access
that same CFD software or some, you know, through website or
application, and it sorted out the compute for you.
So you just ran the simulation and in the back end it was
provisioning a bigger GPU or a bigger CPU, depending on what
you. You never needed to worry about
that. That is the value of the SAS and
it links to the cloud because they're all running on the
cloud. So it gives the cloud gives
those companies the ability that they don't need to go and buy
data centres, They don't need to go and buy a compute.
They can just pay a cloud provider.
And so you are are getting that and they're using the cloud.
And that's essentially how most the two ways that we see and I
see of using the cloud, you can either use the cloud and it's
essentially just a mechanism to get compute in a in a remote
fashion. And you don't really notice it.
You just SSH one to machine. You don't know whether it's in
your cloud account or where it's in some physical data centre
that you own. And of course you can use high
level services. And the advantage of that one is
you keep everything, let's say in your account.
So you have a native account, all your data's in there.
You're a big enterprise, but you have to know how to use Atbus,
You have to know how to do all the cloud.
The alternative option is you just say, well, I want to use
ISV software. A that's just, I'm going to say,
let's say, you know, star CCM has a SAS like, you know, a
version, you can just go in and then use that or could be any
other ISV. I'm just using it as an example.
You can go in and pick the platform that you want, the
compute that you want go in and they provision it for you.
And so you've not had to know any ADDF knowledge to get access
to that compute that is going to become more and more the
mechanic you look at luminary cloud cloud is in the offering,
right. They are a cloud first provider
of CFD with the exact thing I just said that it makes it
easier for you to run stuff without having to worry about
the compute. There are many of like SIM scale
or flex compute or there's, there's more and more, whether
it's startup or an ISV that are offering cloud based back end.
And that's why it's can be, in my mind, a very positive thing
because it's also, let's say, forget about the commercials.
I want you an academic code. If you want someone to use your
code or to give access to it, actually it might be great if
you build your own sort of SAS platform, you can give people
access to your code or collaborate.
And it could be private. Remember, it doesn't have to be
fully public. You could just do it through a
username password and they log on and you can collaborate.
There are many sort of openings to this that haven't been fully
realized. People can create codes and
products and serve them to people without being a sort of
delivering ACD to someone, you know, that they have to install
on the computer, the cloud we use everyday.
And it, it is a way of delivering things to you,
software to you that CFD has to date not fully embraced.
But that is something a bit like GPS and AML, which will only
increase. And I personally think it's a
good thing and it's something that you can explore yourself as
again, not saying that people buying their own desktops or
their own data centers is going to stop.
Of course it's going to continue, but I think the
percentage and the data shows us out that the growth of HPC for
the cloud is something like 15% year over year, whereas HPC on
premise like 8%. And I think that's Hyperion
third party analyst. The data shows it is growing.
And so yeah, in this episode it's more making you aware of
the trends. Whether you agree with them or
not doesn't really matter. It's it's they are in my opinion
anyway, the trends that that are emerging now.
What do these three things mean? GPU technology, AIML cloud.
In my mind, it means that companies can move to higher
fidelity transient methods. Or if your use case doesn't
allow you to do, let's say you know, scale resolving type
simulation, it could just mean bigger meshes.
If it's still steady state it, it could mean running the
simulation for longer conversion to even tighter tolerance.
It could use more equations for your chemistry model.
You know more physics in it. As soon as you have faster, more
compute and access to more of it, let's say through a cloud
like platform, you are going to see more use of it.
And why does this matter? Because ultimately it's about
accuracy. The biggest problem for CFD and
trust for CFD by very senior peoples, people in companies or
by academic research is, is accuracy compared to physical
testing, right. That's always going to be the
put down. The CFD looks good, but how
accurate is it? Well, as we all know, accuracy
is heavily linked to compute. And that's why I think this
really will set up a new wave of move to digital certification.
Digital certification, or certification by analysis has
long been a goal for aircraft and.
Car companies in any industry, but I would argue that compute
and the method you can use is a limiting factor.
Well, once the compute starts to become relaxed, the methods you
can use better methods, which gives you more accuracy and
gives you more evidence to do less physical testing or even if
you do the same physical testing, just more virtual
testing. And that will deliver, in my
opinion, return on investment because companies can shorten
that time to release a product or science can be done at an
even greater scale. So, you know, DNS can be done at
much better resolutions largely simulations can be done for more
realistic problems. You can add more, you know,
multi physics modelling inside case because you have more
compute, you can do multi physics, so many more things.
So I think whether it's more physics or just simply things
like small time steps, laws and meshes, all of this will be more
accuracy and I really do think that is going to drive greater
use of CFD and CE. So I think those markets are
only going to increase because of 1-2 and three the adoption.
This is not in my opinion static.
This is a growing and will continue to grow because it's
value to a business will be beyond just pretty pictures and
actually meaningful improvements in, in, in design and making
things safer, hopefully safe and faster better than the usual
metrics. So what's my final fifth point
to this is this means in my mind mergers and acquisitions, which
is sort of interesting. And again, it's a slightly more
commercial angle, but I think this is interesting for any
people who are looking to find start-ups or you know, people
interesting inside of space. Whenever you have destructive
technologies like this, whenever you have the option of new tech,
new hardware, the emergence of AIML, the emergence of SAS
providing and the the thing that has and high fidelity transit
methods, it means it's perfect time for start-ups and we are
seeing the start-ups. Why now in the past two years
what why didn't we see you know, alumina cloud of volcano
effects, compute, etcetera 5-7 years ago?
Because I believe because of the GPU technology, because of the
cloud it it's set the right environment.
Why are we seeing 10 start-ups now with AIML methods?
Well, part of it is the technology, but it's also the
environment because of the huge Gen.
AI boom that's giving reason for these VCs to fund these sort of
companies. Well, as soon as you have lots
and lots of startups and lots of main players who own majority of
the market, that means there's going to be mergers and
acquisitions. For sure.
The big companies will acquire the smaller companies to keep
market, but there is always the potential for a disruptor that a
company does not just get bought out but actually rises to really
challenge these companies. I don't know which one it is
because the to date you know the large companies like Cadence and
Synopsis, the big EDA companies and people don't know EDA is
probably even bigger market than than the CAE.
They bought in to those. So you know Cadence have
acquired a number of companies, Synopsis have acquired Ansys and
Ansys have acquired a number of companies, Siemens obviously
Dasseau. So you have these players who
see the bigger picture, which is the digital certification, which
is the digital twins, which is the, so the way that it can
retransform manufacturing and the engineering space.
And for them, they see that simulation is a key bit of this.
And this is really the, the sort of the big picture that there is
A, it's a fantastic time in my opinion, to be in CFD and C it's
a fantastic time because there is real value that can be
created. And the dream of someone sat
there and, you know, virtually designing something and testing
it and seeing it in like a, you know, through some sort of
virtual glasses. It seems to be on the horizon.
So I think the next 5-10 years we'll see two things.
One, people doing the same they've always done.
But I see it's true, it doesn't matter.
There are some companies doing the same sort of modelling they
did 10 years ago. And fine, if there is a listing
technology that's nice, but I'm just going to keep doing it the
way I'm going to do it. For some that will happen, but I
think for others there will be a dramatic change, an acceleration
and, and I think this is only a good thing.
So that was my five main trends. I would love to know whether you
agree. It is totally just an opinion.
I'm sure I've missed many things out and I'm happy to go into
more details and other things and be corrected if I was wrong
and some. But I hope you found this
interested and interesting and it's got some of your brain, you
know, thinking of does this apply to me and my thing is this
just a more niche thing to certain segments.
But yeah, I hope you enjoyed it. We've got some great guests
coming up over the next couple of months, which I'm super
excited by. So I don't really like to say
this too much, but it apparently it helps.
So if you are watching this on YouTube, if you sort of like and
subscribe, it helps the YouTube algorithms and also lets you
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follow the podcast so that when something new comes out, you'll
get you'll get notified. All right, thanks very much.
Hope you enjoyed it. See you soon.
The.