All right, so get this.
Those amazing graphics you see in games like Cyberpunk 2077,
that's your graphics card churning out
an insane number of calculations every second.
How many?
Like a mind-boggling 36 trillion.
Wow, 36 trillion every second.
That's incredible.
To put that into perspective, Mario 64 on the Nintendo 64
only needed about 100 million calculations per second.
Oh, wow.
And even Minecraft back in 2011 was only around 100 billion.
That's a huge jump in processing power, right?
Absolutely, a massive leap.
Yeah, so in this deep dive, we're
going to be using the sources that you provided
to really unpack how graphics cards evolved
into these computational behemoths.
It really is a fascinating evolution.
We'll be exploring everything from the physical components
to the really mind-bending architecture that's
behind all those calculations.
And you actually provided some really interesting research
on the fundamental differences between a CPU,
the brains of your computer, and a GTU.
Right, yeah, it's a common point of confusion.
They both handle calculations, obviously.
But they do it in very different ways.
A CPU is like a highly skilled artisan, very fast,
very versatile, but can only work on one or a few tasks
at a time, whereas a GPU is like a massive factory
with thousands of workers all doing relatively simple tasks,
but simultaneously.
So when you need to render millions of pixels in a game,
the GPU's brute force approach of parallel processing
wins out.
Exactly, it's all about tackling what we call embarrassingly
parallel problems.
Things like processing image data or simulating physics.
Where you can break the task down into millions
of independent calculations.
OK, so let's peek inside one of these GPU factories.
What are we looking at here?
Well, at the heart of it all is the graphics processing
unit or the GPU.
The GPU, yeah.
And we often refer to the whole graphics card as a GPU.
But it's actually just one component on a circuit board
with its own power delivery system, cooling,
and of course, memory.
I see.
And in the sources you shared, some schematics and diagrams,
one thing that really stands out is this GA-102 chip,
the brain of the graphics card.
Yes, this chip alone can contain over 28 billion transistors,
which is just an astounding feat of engineering.
Wow, that's more than.
To give you an idea of scale, that's
like fitting the population of the entire planet
onto your fingernail.
Oh, wow.
And all those transistors are organized
in a hierarchical structure, right?
Yes, that's right.
You have these larger units called graphics processing
clusters, which are further divided
into streaming multiprocessors.
And within each of those, you have the individual cores
that are doing the actual calculations.
So remind me again, what are the different types of cores?
And what do they actually do?
So the workhorses for most gaming tasks
are the CUA cores.
Think of them as the general purpose workers
in our factory analogy.
Then you have tensor cores, which are specialized
for matrix math.
They're particularly crucial for AI applications
because of their ability to process huge matrices, which
are the building blocks of many AI algorithms.
And then there are ray tracing cores,
which I know are all the rage in gaming these days.
Yes, ray tracing is a computationally intensive
technique that simulates the path of light
to create incredibly realistic lighting and reflections
in games.
It's been around for a while in other industries like film,
but only recently have GPUs become powerful enough
to handle real-time retracing in games.
Now, here's something I found really interesting
in the materials you shared.
The concept of binning.
It seems like the same GA-102 chip
can end up in a variety of different graphics cards,
like the 3080, the 3090, and their tie variants.
The binning is a fascinating process.
Essentially, not every chip manufactured is perfect.
Some might have tiny flaws or defects.
The chips with the fewest flaws are
sorted into the higher-end cards, like the 3090 Ti, which
boasts a full complement of CUDA cores and higher clock speeds.
Cards like the 3080 might have some cores deactivated
due to these minor flaws.
So it's a way to maximize the yield
from the manufacturing process and create a range of products
at different price points.
But how do they actually test and sort
these billions of transistors?
It's a highly automated process that
involves running a series of tests
to identify any defects or performance limitations.
The chips are then categorized based on their capabilities.
And the best ones go to the top tier cards.
OK.
So we've got these different cores doing their thing.
But they need a constant stream of data to work on, right?
Absolutely, yes.
That's where graphics memory comes in?
That's where it comes in.
GDDR6XSDRAM is the latest generation of graphics memory.
And it plays a crucial role in seeding the GPU with the data
it needs.
Think of it as a high-speed conveyor belt,
delivering raw materials to our factory workers.
And it's not just about the amount of memory, right?
But also the speed at which data can be transferred.
Yes.
Bandwidth is the term that keeps popping up.
Bandwidth is crucial.
It's a measure of how much data can be transferred per second.
GDDR6X uses some clever techniques,
like PAM3 and PAM4 encoding, to cram more data
through the same physical connections.
Instead of just using zeros and one,
they use multiple voltage levels to represent more information.
So PAM3 is the newer and more efficient encoding scheme, right?
That's right.
PAM3 was adopted for its better signal integrity
and efficiency, even though it technically
uses fewer voltage levels than PAM4.
It's a trade-off between speed and reliability.
There's a constant push and pull between these factors
as engineers strive to squeeze every bit of performance
out of these systems.
OK, so we've covered the basics of the GPU.
Yes.
It's cores and how data flows in and out.
But now we need to dive into the real magic,
like the architecture that allows these billions of transistors
to work together in parallel.
That's where things get really interesting.
Yeah.
We'll be exploring concepts like SIMD and SIMT,
which are fundamental to understanding how GPUs achieve
their incredible performance.
Let's take a break there.
OK.
And come back to unravel those acronyms.
That's good.
In part two.
All right, all right.
This stuff is truly mind-blowing.
It is.
Welcome back.
So last time, we were exploring the physical heart
of a graphics card.
Right, all those components.
Yeah, and now we're going even deeper.
Oh, wow.
Deeper.
Yeah, to the level of the instructions
that tell all those billions of transistors what to do.
So like the software almost.
Yeah, kind of, yeah.
OK.
So are you ready to untangle SIMD and SIMD?
I am, and I think our listeners will
find this particularly fascinating.
These concepts are really key to understanding
how GPUs achieve such mind-boggling performance.
OK, so let's start with SIMD.
OK.
Single instruction, multiple data,
sounds kind of like doing a lot with a little.
That's the essence of it, yeah.
OK.
Imagine you have a recipe.
But instead of making one dish, you
could make dozens simultaneously using the same instructions.
SIMD works on that principle.
So instead of processing one data point at a time,
a SIMD instruction can operate on a whole chunk of data
simultaneously.
Exactly.
Think about a simple operation, like adding five
to every number in a list.
A CPU would have to loop through each number individually.
A GPU using SIMD could add five to, say, 32 numbers at once,
depending on its architecture.
OK, that makes sense.
Yeah.
But how does this translate to the complex world
of graphics rendering?
OK, let's go back to the example of transforming
vertex coordinates, which defines the shape of objects
in a game world.
Each vertex has an x, y, and z coordinate
to place that vertex correctly.
You need to apply a matrix transformation.
OK.
With SIMD, the GPU can perform that same transformation
on multiple vertices at once, right,
drastically speeding up the process.
Yeah, I'm starting to see why this is so crucial.
Right.
For handling millions of pixels and polygons.
It's exactly.
In real time.
Yeah, it's all about leveraging parallelism.
But SIMD has its limitations.
OK.
It assumes all data needs the same operation applied to it.
OK.
That's where SIMD comes in.
OK.
Single instruction, multiple threads.
And from what I gathered in the research that you provided,
yeah.
SIMD seems to be more flexible.
You nailed it.
OK.
SIMD allows different threads within a group.
Right.
To execute instructions at their own pace.
OK.
Even if they're all technically working
from the same instruction set.
Right.
This is essential for dealing with real world scenarios.
Right.
Where not all data needs the same treatment.
So if some threads need to take a detour,
like in their calculations.
Right.
They don't hold up the entire group.
Precisely.
OK.
Think about rendering a complex scene with lots of objects.
OK.
Some might be visible to the player.
Right.
While others are completely obscured.
Right.
Behind something.
Exactly.
With SIMD, the GPU can intelligently allocate resources.
Right.
So that threads working on hidden objects
can essentially skip.
Oh, wow.
Unnecessary calculations.
I'm so smart.
Making the whole process more efficient.
It's like having a smart manager.
Yes.
Like directing traffic within the GPU.
Right.
Making sure things run smoothly.
Yeah.
Keeping everything optimized.
Yeah.
Even with lots of complex decisions happening.
That's a great way to put it.
But why wasn't SIMD always the standard?
That's a great question.
Like if it's more flexible.
Right.
Why even bother with SIMD?
So early GPUs were simpler.
OK.
And relied on SIMD because it was easier
to implement in hardware.
Oh.
Keeping all threads in lockstep.
Right.
Simplified control and management.
Gotcha.
But as games and graphics became more complex.
Yeah, of course.
The need for flexibility led to the development.
Right.
And adoption of SIMD.
So SIMD is like the natural evolution.
It is, yeah.
Of SIMD.
Yeah.
You can think of it that way.
Offering more control and efficiency for modern workloads.
Yeah.
For those more demanding tasks.
It's amazing how these seemingly small changes
in architecture.
I know.
Have such a huge impact on performance.
It really is a testament to the constant innovation
happening in the field of computer graphics.
And speaking of innovation.
Yes.
You shared some articles about how GPUs are being used
for tasks.
Right.
Far beyond gaming.
Yes.
Absolutely.
One area that caught my eye was scientific simulation.
Oh yeah.
It seems like GPUs are being used to model everything.
Right.
From protein folding to the evolution of galaxies.
Amazing, isn't it?
Who thought your gaming rig.
Right.
Could unlock the secrets of the universe.
It's truly remarkable.
Yeah.
Many scientific problems involve crunching massive data
sets.
Right.
And running complex simulations.
Yeah.
GPUs with their parallel processing power.
Yeah.
Excel at these tasks.
Yeah.
They're like supercharged calculators for scientists.
And then there's the world of artificial intelligence.
Which seems almost inseparable from GPUs these days.
Absolutely.
We already touched on tensor cores.
Yeah.
Which are specifically designed.
Right.
For the matrix math that underlies AI algorithms.
Right.
Everything from image recognition.
Right.
To natural language processing relies on these powerful cores.
Yeah.
I know we can't do a full deep dive into AI here.
Right.
That would be a whole other series.
But it's mind blowing to think.
It really is.
That the same technology powering immersive game worlds.
Yeah.
Is also driving advancements in robotics, self driving cars.
Absolutely.
And talentless other fields.
It really speaks to the versatility of the GPU architecture.
It's no longer just about pretty pixels.
Yeah.
It's about solving some of the most complex and challenging
problems humanity faces.
OK.
Before we get to philosophical.
OK.
There's one more crucial component we need to discuss.
Great.
Graphics memory.
Yes.
Graphics memory.
We mentioned it briefly earlier.
Right.
But let's dive deeper into what makes GDDR memory tick.
It's the unsung hero of the GPU world.
Yeah.
Without fast and efficient memory.
Right.
All that processing power would be bottlenecked.
Right.
Starved for data.
The articles you shared talk about GDDR6x
being the current state of the art.
That's right.
What makes it so special?
It's all about speed and bandwidth.
OK.
GDDR6x uses a wider memory bus.
Right.
Meaning more data can flow between the memory
and the GPU simultaneously.
Right.
OK.
And the memory chips themselves operate
at incredibly high clock speeds.
Further boosting data transfer rates.
So it's like having a wider highway with faster cars.
That's a great analogy.
Ensuring a steady flow of data.
Yes.
To the GPU's processing units.
Exactly.
And as we mentioned earlier.
Right.
It also uses those advanced encoding schemes.
Yeah.
Like pay-am-three.
Like pay-am-three to squeeze even more data
through the same physical connections.
That's right.
Now even with all this advanced technology.
Mm-hmm.
There are still trade-offs and challenges.
Of course.
Always.
Memory capacity.
Power consumption.
Heat dissipation.
Right.
These must all be carefully considered.
You're absolutely right.
Yeah.
Designing a high performance graphics card
is a delicate balancing act.
Engineers are constantly pushing the boundaries
of what's possible.
Right.
But they have to work within the constraints.
Right.
Of physics and manufacturing processes.
It's like a high stakes engineering puzzle almost.
Yeah.
Trying to optimize for speed, efficiency, and cost.
It really is.
I'm guessing that's why we see such a variety of graphics
cards on the market.
That's exactly right.
Each tailored to different needs and budgets.
Absolutely.
From entry level cards for casual gamers.
Right.
To behemoth GPUs for professional workstations.
Right.
There's a whole spectrum of options out there.
Yeah.
And the technology keeps evolving at a rapid pace.
Yeah.
So what's considered high end today.
Right.
Might be mid-range tomorrow.
Yeah.
It's an exciting time to be following
the world of graphics cards.
It is.
They're not just about gaming anymore.
Not at all.
They're driving innovation.
Yeah.
In countless fields.
That's right.
And speaking of the future.
Yeah.
We haven't even touched on some of the cutting edge
research happening.
Yeah.
In areas like neuromorphic computing
and quantum computing.
OK.
Both of which could have a profound impact.
Wow.
On the evolution of GPUs.
That sounds like a whole other deep dive.
It does.
Doesn't it.
Yeah.
But before we get ahead of ourselves.
OK.
Let's wrap up this exploration of current generation
GPUs.
Right.
And Ponder what they tell us about the future of computing.
I'm looking forward to that discussion.
Yeah.
The advancements we're seeing today
are just the tip of the iceberg.
Welcome back.
You know for the final part of our graphics card deep dive.
It's been quite a journey.
It really has.
We've gone from dissecting the hardware
to exploring how GPUs are transforming fields.
Like far beyond gaming.
Yeah.
It's incredible to see how far they've come.
Yeah.
And I think what's really struck me
is how much of this technology is still evolving.
Absolutely.
We're not just talking about incremental improvements.
Right.
It feels like we're on the cusp of some truly groundbreaking
advancements.
I totally agree.
The line between a GPU and a general purpose processor
is blurring.
Right.
You know we're seeing GPUs being used
for scientific simulations, machine learning,
even cryptography.
Yeah.
It's becoming like a fundamental tool
across so many disciplines.
One area that really piqued my curiosity
and the research you shared.
Yeah.
Is the growing role of AI.
Right.
In graphics processing.
Yeah.
It's not just about tensor cores anymore.
No.
It's about using AI to fundamentally change
how games look and feel.
Exactly.
Imagine a game where the lighting, the shadows,
and the textures are dynamically adjusted in real time
based on your actions.
That would be incredible.
You know AI powered character animation.
Yeah.
That's indistinguishable from reality.
Right.
These aren't just pipe dreams anymore.
Yeah.
These are active areas of research and development.
So instead of relying on pre-baked animations
or limited physics simulations, games
could become truly dynamic and responsive.
Exactly.
Almost like interactive movies.
And on the development side, AI could assist artists
and designers.
Oh, absolutely.
Automating tedious tasks and allowing
for more creative freedom.
I think that's one of the most exciting aspects of it.
It really is an exciting time to be working in this space.
It is.
But wouldn't all this AI processing
require even more powerful GPUs?
Well, it's a classic feedback loop.
Yeah.
As software developers create more demanding applications.
Right.
Hardware engineers are pushed to create more powerful
and efficient GPUs.
Yeah.
And those advancements, in turn,
unlock new possibilities for software.
So it's like a constant arms race.
In a way, yes.
Between software demands and hardware capabilities.
You could say that.
So what are some of the key areas
where we're likely to see breakthroughs?
OK.
One major area of focus is memory technology.
OK.
As we discussed, GDDR6X is the current standard.
Right.
But there are already newer technologies,
like high bandwidth memory or HPM.
Right.
Thanks, Ria.
Nimbroging HPM offers significantly higher bandwidth.
Wow.
But it's also more complex and expensive to manufacture.
So it's a trade off between performance and cost.
Always, as with most things in engineering.
Yeah, but I'm curious.
Yeah.
Are there any alternative approaches
to increasing performance?
That's a great question.
That go beyond just cramming more transistors onto a chip.
Right.
Or pushing memory speeds higher.
There certainly are.
OK.
One area that's garnering a lot of attention
is neuromorphic computing.
Neuromorphic computing, OK.
Which aims to mimic the structure and function
of the human brain.
OK.
Instead of relying on traditional transistors
and logic gates, neuromorphic chips
use artificial neurons and synapses
to process information in a more parallel and energy
efficient way.
So instead of brute force calculations,
it's more about mimicking the way our brains process
information.
Right.
Like pattern recognition, learning, and adaptation.
Wow.
That sounds almost like science fiction.
It is a radical departure from traditional computing
architectures.
Yeah.
And it's still early days.
Right.
But the potential is immense.
Yeah.
Especially for tasks like artificial intelligence
and machine learning.
Right.
Where current GPUs, while powerful,
can still be quite energy hungry.
And I know we've barely scratched
the surface of quantum computing.
Oh, yeah.
Which seems to defy the rules of classical physics
altogether.
It really does.
Could that have implications for the future of GPUs?
Quantum computing is kind of a wild card.
Yeah.
It operates on principles that are fundamentally different
from classical computing.
All right.
While it's unlikely to replace traditional GPUs anytime soon.
Yeah.
There's potential for hybrid systems
that combine the strengths of both approaches.
It's mind boggling to think about the possibilities
we could be talking about solving problems that
are currently intractable for even the most powerful
supercomputer.
Absolutely.
It makes you wonder what the gaming landscape will even
look like in 10 or 20 years.
Yeah.
Will we even need physical graphics cards in the future?
Right.
Or will processing power become so ubiquitous and accessible
that it's just seamlessly integrated
in our everyday lives?
Maybe we'll be streaming photorealistic game worlds
directly to our neural implants.
Who knows?
It's an exciting time.
It is.
To be thinking about these things.
But I think one thing's for sure,
the journey of the GPU is far from over.
I wholeheartedly agree.
Yeah.
It's a testament to human ingenuity, our constant drive
to push the boundaries of what's possible.
And I, for one, can't wait to see what the future holds.
Me too.
Thanks for joining us on this incredible deep dive
into the world of graphics cards.
It's been a pleasure.
We hope you've enjoyed the ride as much as we have.