How AI and Accelerated Computing Reshape Manufacturing

I mean, it makes a lot of sense.

And you've touched on AI, but we're going to hold that thought for a quick second, right? And you're right.

I mean, there's a lot of, you know, really, I think anything in life can always be improved upon, right? And nothing is ever perfect.

And I want you to touch on something that you talked about, kind of the accelerated compute of, you know, NVIDIA and kind of the underneath kind of CUDA layer of that.

And I'm going to remember a conversation that we had, and hopefully I'm getting it right, or we're going to have to stop this recording and rerecord this part.

But I remember you were telling me, I think, Sean, your boss, Sean Young, was there as well.

And you were kind of talking about kind of the simulation piece was all very CPU based in kind of the past.

Right.

And, you know, and then now it's moving to more of kind of a GP or not everyone, but it's starting to move kind of more towards GPU compute.

So can we one kind of talk about that for a second before we get to AI and then also and I want to call that out because it's not just the efficiency within AI that is bringing even just traditional processes are being accelerated by Nvidia and Dell Pro Max and Nvidia RTX GPUs.

But can you talk a little bit about that change from CPU to GPU processing for simulation?

Absolutely.

So, you know, going back to the previous question, we discussed this process, right? How products are developed, products are designed before they go to manufacturing, right? So it goes through this step-by-step process of requirements gathering, R&D, conceptual design, detailed design.

And then, you know, once the design goes through reviews and approvals, it will go to manufacturing.

But during this phase of conceptual design to detailed design, there is a very important step of simulations, engineering simulations.

And what it does is the product is going to experience in its operating environment, it's going to experience some forces, pressures, temperatures, a lot of these different physical processes, right? So we need to make sure that the process or the product is able to withstand all of these external factors that I mentioned, temperatures, pressures, vibrations, all of that, and perform the way it is supposed to perform without any failure.

So that is what engineers simulate digitally on a computer before the product is manufactured.

They want to capture the performance, the behavior early on in the design cycle.

Now, this process has traditionally been done on CPUs, and it takes a long time to do these simulations.

It can take hours, in some cases, it can take weeks.

But what has happened over the last five to 10 years is a lot of these ISVs or independent software vendors who develop these applications where you can do these simulations, they have ported these solvers to GPUs.

So the solvers that were entirely running on CPUs previously are now doing these computations on GPUs.

And the main difference between GPU calculation and CPU calculation is on GPUs, there are thousands and thousands of cores that are doing these simulations in parallel.

So the GPU solvers are much, much faster as compared to CPU solvers.

So previously, the simulation that would have taken hours or days is 10x, 20x, or even 50 to 100x faster depending on the type of application and what is being simulated.

I mean, it makes a lot of sense.

And you've touched on AI, but we're going to hold that thought for a quick second, right? And you're right.

I mean, there's a lot of, you know, really, I think anything in life can always be improved upon, right? And nothing is ever perfect.

And I want you to touch on something that you talked about, kind of the accelerated compute of, you know, NVIDIA and kind of the underneath kind of CUDA layer of that.

And I'm going to remember a conversation that we had, and hopefully I'm getting it right, or we're going to have to stop this recording and rerecord this part.

But I remember you were telling me, I think, Sean, your boss, Sean Young, was there as well.

And you were kind of talking about kind of the simulation piece was all very CPU based in kind of the past.

Right.

And, you know, and then now it's moving to more of kind of a GP or not everyone, but it's starting to move kind of more towards GPU compute.

So can we one kind of talk about that for a second before we get to AI and then also and I want to call that out because it's not just the efficiency within AI that is bringing even just traditional processes are being accelerated by Nvidia and Dell Pro Max and Nvidia RTX GPUs.

But can you talk a little bit about that change from CPU to GPU processing for simulation?

Absolutely.

So, you know, going back to the previous question, we discussed this process, right? How products are developed, products are designed before they go to manufacturing, right? So it goes through this step-by-step process of requirements gathering, R&D, conceptual design, detailed design.

And then, you know, once the design goes through reviews and approvals, it will go to manufacturing.

But during this phase of conceptual design to detailed design, there is a very important step of simulations, engineering simulations.

And what it does is the product is going to experience in its operating environment, it's going to experience some forces, pressures, temperatures, a lot of these different physical processes, right? So we need to make sure that the process or the product is able to withstand all of these external factors that I mentioned, temperatures, pressures, vibrations, all of that, and perform the way it is supposed to perform without any failure.

So that is what engineers simulate digitally on a computer before the product is manufactured.

They want to capture the performance, the behavior early on in the design cycle.

Now, this process has traditionally been done on CPUs, and it takes a long time to do these simulations.

It can take hours, in some cases, it can take weeks.

But what has happened over the last five to 10 years is a lot of these ISVs or independent software vendors who develop these applications where you can do these simulations, they have ported these solvers to GPUs.

So the solvers that were entirely running on CPUs previously are now doing these computations on GPUs.

And the main difference between GPU calculation and CPU calculation is on GPUs, there are thousands and thousands of cores that are doing these simulations in parallel.

So the GPU solvers are much, much faster as compared to CPU solvers.

So previously, the simulation that would have taken hours or days is 10x, 20x, or even 50 to 100x faster depending on the type of application and what is being simulated.