My former boss (Steve Parker, RIP) shared a story of Turner Whitted making predictions about how much compute would be needed to achieve real-time ray tracing, some time around when his seminal paper was published (~1980). As the story goes, Turner went through some calculations and came to the conclusion that it’d take 1 Cray per pixel. Because of the space each Cray takes, they’d be too far apart and he thought they wouldn’t be able to link it to a monitor and get the results in real time, so instead you’d probably have to put the array of Crays in the desert, each one attached to an RGB light, and fly over it in an airplane to see the image.
Another comparison that is equally astonishing to the RPi is that modern GPUs have exceeded Whitted’s prediction. Turner’s paper used 640x480 images. At that resolution, extrapolating the 160 Mflops number, 1 Cray per pixel would be 49 Tera flops. A 4080 GPU has just shy of 50 Tflops peak performance, so it has surpassed what Turner thought we’d need.
Think about that - not just faster than a Cray for a lot less money, but one cheap consumer device is faster than 300,000 Crays.(!) Faster than a whole Cray per pixel. We really have come a long, long way.
The 5090 has over 300 Tflops of ray tracing perf, and the Tensor cores are now in the Petaflops range (with lower precision math), so we’re now exceeding the compute needed for 1 Cray per pixel at 1080p. 1 GPU faster than 2M Crays. Mind blowing.
> but then again if you'd showed me an RPi5 back in 1977 I would have said "nah, impossible" so who knows?
I was reading lots of scifi in 1977, so I may have tried to talk to the pi like Scotty trying to talk to the mouse in Star Trek IV. And since you can run an LLM and text to speech on an RPi5, it might have answered.
No need for an RPi 5. Back in 1982, a dual or quad-CPU X-MP could have run a small LLM, say, with 200–300K weights, without trouble. The Crays were, ironically, very well suited for neural networks, we just didn’t know it yet. Such an LLM could have handled grammar and code autocompletion, basic linting, or documentation queries and summarization. By the late 80s, a Y-MP might even have been enough to support a small conversational agent.
A modest PDP-11/34 cluster with AP-120 vector coprocessors might even have served as a cheaper pathfinder in the late 70s for labs and companies who couldn't afford a Cray 1 and its infrastructure.
But we lacked both the data and the concepts. Massive, curated datasets (and backpropagation!) weren’t even a thing until the late 80s or 90s. And even then, they ran on far less powerful hardware than the Crays. Ideas and concepts were the limiting factor, not the hardware.
Like James Bond's Aston Martin with a satnav/tracking device in 1964's Goldfinger. Kids would know what that was but they might not understand why Bond had to continually shift some sort of stick to change the car's gear.
Oh really? What vehicle can I buy today, drive home, get twice the legal limit drunk, flop in the back alone to take a nap while my car drives me two hours away to a relative's house?
I'd really like to buy that car so I await your response.
That's a jurisdiction problem, not a technology problem. No tech is foolproof, but even with the current technology someone would be much safer (for others, too) in the back seat than trying to drive tired, borderline DUI at night in unfamiliar town. Which many folks regularly do, for example on business travel.
The reason I cannot do this today is laws, not technology. My 2c.
You can do all that in a Waymo except for the “buy” part. When asked about that Sergey said “why do you want to own a car? You have to maintain it, insure it, park it at home and at work. Don’t you really just want to get where you’re going and have someone else figure out the rest?”
This was back before google ate the evil pill. Now their philosophy is more like “don’t fall asleep, we can get a good deal on your kidneys, after that we’ll sell your mom’s kidneys too”
Not really. My 1983 Datsun would talk, but it couldn't converse. Alexa and Siri couldn't hold a conversation anywhere near the level KITT did. There's a big difference. With LLMs, we're getting close.
Reading this I wonder, say we did have a time machine and were somehow able to give scientists back in the day access to an RPI5. What sort of crazy experiments would that have spawned?
I'm sure when the Cray 1 came out, access to it must have been very restricted and there must have been hoards of scientists clamoring to run their experiments and computations on it. What would have happened if we gave every one of those clamoring scientists an RPI5?
And yes I know this raises an interface problem of how would they even use one back in the day but lets put that to the side and assume we figured out how to make an RPI5 behave exactly like a Cray 1 and allowed scientists to use it in a productive way.
First of all, how would they talk to it? You'd have to give them an RPI5 with serial console enabled, and strict instructions not to exceed the 3.3 volt limits of the I/O. Now it's reasonable that you could generate NTSC video out of it, so they could see on the screen any output.
When you then explained it was just bit-banging said NTSC output, they'd be amazed even more.
Comparing against a raspberry pi 5 is kind of overkill. While a Pico 2 is close to computationally equivalent to a cray 1 now (version 2 added hardware floating point), the cray still has substantially more memory - almost 9MB vs 520k.
For parity, you have to move up to a raspberry pi zero 2, which costs $15 and uses about 2W of powerm
A million times cheaper than a cray in 2025 dollars and quite a bit more capable.
No. With more computing power the level of detail increased.
And some problems are even more complex.
My father spent his career on researching coil forms for Stellerator fusion reactors. Finding the shapes for their experiments then was a huge computational problem using then-state of the art machines (incl. cray for a while) and even today's computing power isn't there, yet.
Other problems we now solve regularly on our phones ...
It was pretty basic models for tasks like weather forecasting and simulating nuclear reactions. We've come a long way on both the software modeling and hardware front.
Most are not solved but modern systems can generate better solutions. Think about problems like forecasting weather or finite element analysis of mechanical systems.
> It kinda reminded me of the trash can mac. I wonder if it was inspiration for it
Ironically the trash can Mac actually looked strikingly similar in size and shape to actual small trash cans that were all over the Apple campus when I worked there. I’d see them in the cafeteria every day. They were aluminum though, but otherwise very similar. I always wondered if they had anything to do with the design of the computer, even if only subconsciously.
These comparisons are fun at all but a better one would be the difference between whatever "computer" a citizen lambda would have used back in the day and the cray1 and whatever on can use now and the current "cray" (or whatever humans use now) and see the difference of cost.
I did a little poking round and I think the modern equivalent to old super computers is a mainframe. Modern super computers take up entire warehouses, cost upwards of $100 million, and are measured in exaflops.
Cray 1 costs US$7.9 million in 1977 (equivalent to $41 million in 2024) (Source: Wikipedia)
I have no idea what IBM z-series mainframes cost but I think it would be less.
$41 million can buy you one or more thousands of rack-mounted servers and the associated networking hardware.
My rough guess would be the difference in 2024 iphones to mainframes is an order of magnitude more between them than Cray and anything else on the market at the time.
It’s also interesting to note how much software has changed. The actual machine code may be less optimized, but we have better algorithms and we have the option of using vast amounts of memory and disk to save cpu time. And that’s before we get into specialized hardware.
Mainframes aren't supercomputers. The point of a mainframe (anymore) is reliable transactions without downtime. They're not necessarily beasts at computation.
Supercomputers were and are beasts of not only computation but memory size and bandwidth. They're used for tasks where the computation is highly parallel but the memory is not. If you're doing nuclear physics or fluid dynamics every particle in a simulation has some influence on every other. The more particles and more state for each particle you can store and apply to every other particle makes for a more accurate simulation.
As SCs have improved in memory size and bandwidth simulations/modeling with them has gotten more accurate and more useful.
The first Cray-1 was installed at Los Alamos National Laboratory in 1976. That same year Gary Kildall created CP/M and Steve Wozniak completed the Apple-1.
> If AI systems continue to improve at the current rate and we combine that with improvements in hardware that are measured in orders of magnitude every 15 years or so then it stands to reason that we'll get that "super-intelligent GAI" system any day now.
Oh come off it now. This could have been just a good blog post that didn't make me want to throw my phone across the room. GenAI is a hell of a drug. It's shocking how many technical professionals fall into the hype and become irrationally exuberant.
Even if you are a GAI / super intelligence booster, the limiting factor is clearly software and data. If it is possible, the big tech AI labs already have all the compute they need to make one deployment work. Hardware is limiting for deploying at scale and at a profit.
While a cray could compute millions of things and did a bunch of usable stuff for many groups of people who used it back then, a raspberrypi today has trouble even properly displaying a weather forecast at "acceptable speeds", because modern software has become very bloated, and that includes weather forecast sites that somehow have to include autoplaying video, usually an ad.
Adjust the price of the Cray-1, for inflation, but not the power, for Moore's law? Need I get my napkin out for a few calculations? or do we just FORGET MOORE'S LAW ( that is mention no less that 4 times, without quantification? Cray-1 (1976 ). RPi ( 2012 ). 37 years of elapsed time. 24. 2/3 elapsed generations. 26,509,000 times increase in power. Cray 1 160Mf. In a 26M times faster, would yield 4,241Gf ( 4.2Pf) , while the PI1 is capable of 13.5Gf, so the RPi-1 ( 2012 ) is about 0.31% of where Moore's law power doubling is.
Now lets compare this to the top 500. ( see the point? )( do not speak of Moore's law, while ignoring the mathematical implications. ) ( and yes, 3/1000s is three thousandths ).
Top 500 is 1.7 Exaflops, but by Moore's law should be 4,241Gf or 4.2Xf. So the top 500 is not keeping up with Moore's law.
Another comparison that is equally astonishing to the RPi is that modern GPUs have exceeded Whitted’s prediction. Turner’s paper used 640x480 images. At that resolution, extrapolating the 160 Mflops number, 1 Cray per pixel would be 49 Tera flops. A 4080 GPU has just shy of 50 Tflops peak performance, so it has surpassed what Turner thought we’d need.
Think about that - not just faster than a Cray for a lot less money, but one cheap consumer device is faster than 300,000 Crays.(!) Faster than a whole Cray per pixel. We really have come a long, long way.
The 5090 has over 300 Tflops of ray tracing perf, and the Tensor cores are now in the Petaflops range (with lower precision math), so we’re now exceeding the compute needed for 1 Cray per pixel at 1080p. 1 GPU faster than 2M Crays. Mind blowing.
Interesting, wonder how it compares in terms of transistors. How many transistors combined did one Cray have in compute and cache chips?
I was reading lots of scifi in 1977, so I may have tried to talk to the pi like Scotty trying to talk to the mouse in Star Trek IV. And since you can run an LLM and text to speech on an RPi5, it might have answered.
A modest PDP-11/34 cluster with AP-120 vector coprocessors might even have served as a cheaper pathfinder in the late 70s for labs and companies who couldn't afford a Cray 1 and its infrastructure.
But we lacked both the data and the concepts. Massive, curated datasets (and backpropagation!) weren’t even a thing until the late 80s or 90s. And even then, they ran on far less powerful hardware than the Crays. Ideas and concepts were the limiting factor, not the hardware.
I'd really like to buy that car so I await your response.
The reason I cannot do this today is laws, not technology. My 2c.
Also, my friend's father in the 80s was the driver of a French Consulate's member in Turkey. His car (a Renault) had speech functionality.
I'm sure when the Cray 1 came out, access to it must have been very restricted and there must have been hoards of scientists clamoring to run their experiments and computations on it. What would have happened if we gave every one of those clamoring scientists an RPI5?
And yes I know this raises an interface problem of how would they even use one back in the day but lets put that to the side and assume we figured out how to make an RPI5 behave exactly like a Cray 1 and allowed scientists to use it in a productive way.
When you then explained it was just bit-banging said NTSC output, they'd be amazed even more.
For parity, you have to move up to a raspberry pi zero 2, which costs $15 and uses about 2W of powerm
A million times cheaper than a cray in 2025 dollars and quite a bit more capable.
And some problems are even more complex.
My father spent his career on researching coil forms for Stellerator fusion reactors. Finding the shapes for their experiments then was a huge computational problem using then-state of the art machines (incl. cray for a while) and even today's computing power isn't there, yet.
Other problems we now solve regularly on our phones ...
It kinda reminded me of the trash can mac. I wonder if it was inspiration for it
Ironically the trash can Mac actually looked strikingly similar in size and shape to actual small trash cans that were all over the Apple campus when I worked there. I’d see them in the cafeteria every day. They were aluminum though, but otherwise very similar. I always wondered if they had anything to do with the design of the computer, even if only subconsciously.
Cray 1 costs US$7.9 million in 1977 (equivalent to $41 million in 2024) (Source: Wikipedia)
I have no idea what IBM z-series mainframes cost but I think it would be less.
$41 million can buy you one or more thousands of rack-mounted servers and the associated networking hardware.
My rough guess would be the difference in 2024 iphones to mainframes is an order of magnitude more between them than Cray and anything else on the market at the time.
It’s also interesting to note how much software has changed. The actual machine code may be less optimized, but we have better algorithms and we have the option of using vast amounts of memory and disk to save cpu time. And that’s before we get into specialized hardware.
Supercomputers were and are beasts of not only computation but memory size and bandwidth. They're used for tasks where the computation is highly parallel but the memory is not. If you're doing nuclear physics or fluid dynamics every particle in a simulation has some influence on every other. The more particles and more state for each particle you can store and apply to every other particle makes for a more accurate simulation.
As SCs have improved in memory size and bandwidth simulations/modeling with them has gotten more accurate and more useful.
Oh come off it now. This could have been just a good blog post that didn't make me want to throw my phone across the room. GenAI is a hell of a drug. It's shocking how many technical professionals fall into the hype and become irrationally exuberant.
The upper-class "trust me bro"
A few niche uses aside (gaming, llm) a vaguely modern desktop is good enough regardless of details.
...software... well, that's a different story.
While a cray could compute millions of things and did a bunch of usable stuff for many groups of people who used it back then, a raspberrypi today has trouble even properly displaying a weather forecast at "acceptable speeds", because modern software has become very bloated, and that includes weather forecast sites that somehow have to include autoplaying video, usually an ad.
Now lets compare this to the top 500. ( see the point? )( do not speak of Moore's law, while ignoring the mathematical implications. ) ( and yes, 3/1000s is three thousandths ).
Top 500 is 1.7 Exaflops, but by Moore's law should be 4,241Gf or 4.2Xf. So the top 500 is not keeping up with Moore's law.