Cray versus Raspberry Pi

https://www.aardvark.co.nz/daily/2025/0611.shtml

by flyingkiwi44 • 1 year ago

173 130 1 year ago

aardvark.co.nz

hoppp• 1 year ago

The cray 1 did look futuristic like something out of star trek.

It kinda reminded me of the trash can mac. I wonder if it was inspiration for it

Mountain_Skies3• 1 year ago

If I ever have reason to build a Pi cluster, I'm putting in a Cray X-MP shaped case.

v9v• 1 year ago

Related: a Pi Pico cluster that looks like a Cray computer https://hackaday.com/2023/04/09/parallel-computing-on-the-pi...

einsteinx2• 1 year ago

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.

delichon• 1 year ago

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.

Mountain_Skies3• 1 year ago

Someday real soon, kids being shown episodes of 'Knight Rider' by their grandparents won't understand why a talking car was so futuristic.

sublinear• 1 year ago

Was that point not almost a decade ago?

KineticLensman• 1 year ago

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.

heelix• 1 year ago

The self driving aspect, amazingly, is already here and considered mundane.

dizhn• 1 year ago

Kitt was funny though. (For its time)

Havoc2• 1 year ago

Tried explaining what a Tamagotchi was to someone recently. Looks of utter bewilderment

tsoukase• 1 year ago

I grew up watching Kitt and when I watched it again a few days ago, I didn't feel anything. Much less my kids.

hulitu2• 1 year ago

Someday real soon, kids being shown episodes of 'Knight Rider' by their grandparents won't understand why a talking car was so futuristic.

Maybe in 100 years. The talking car was more intelligent than Siri, Alexa or Hey Google.

It is not that we are not able to "talk" to computers, it is that we "talk" with computers only so that they can collect more data about us. Their "intelligence" is limited to simple text underestanding.

qgin2• 1 year ago

It’s impossible to explain to kids now why it was funny on Seinfeld when Kramer pretended to be MoviePhone and says “why don’t you just tell me the name of the movie you selected!”

rahen6• 1 year ago

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.

adwn2• 1 year ago

a small LLM, say, with 200–300K weights

A "small Large Language Model", you say? So a "Language Model"? ;-)

Such an LLM could have handled grammar and code autocompletion, basic linting, or documentation queries and summarization.

No, not even close. You're off by 3 orders of magnitude if you want even the most basic text understanding, 4 OOM if you want anything slightly more complex (like code autocompletion), and 5–6 OOM for good speech recognition and generation. Hardware was very much a limiting factor.

fentonc• 1 year ago

I think a quad-CPU X-MP is probably the first computer that could have run (not train!) a reasonably impressive LLM if you could magically transport one back in time. It supported a 4GB (512 MWord) SRAM-based "Solid State Drive" with a supported transfer bandwidth of 2 GB/s, and about 800 MFLOPS CPU performance on something like a big matmul. You could probably run a 7B parameter model with 4-bit quantization on it with careful programming, and get a token every couple seconds.

JdeBP• 1 year ago

You should have been watching lots of SciFi, too. (-:

I have a Raspberry Pi in a translucent "modular case" from the PiHut.

* https://thepihut.com/products/modular-raspberry-pi-4-case-cl...

It is very close to the same size and appearance as the "key" for Orac in Blake's 7.

I have so far resisted the temptation to slap it on top of a Really Useful Box and play the buzzing noise.

* https://youtube.com/watch?v=XOd1WkUcRzY

Obviously not even Avon figured out that the main box of Orac was a distraction, a fancy base station to hold the power supply, WiFi antenna, GPS receiver, and some Christmas tree lights, and all of the computational power was really in the activation key.

The amusing thing is that that is not the only 1970s SciFi telly prop that could become almost real today. It shouldn't be hard -- all of the components exist -- to make an actual Space 1999 commlock; not just a good impression of one, but a functioning one that could do teleconferencing over a LAN, IR control for doors and tellies and stuff, and remote computer access.

Not quite in time for 1999, alas. (-:

* https://mastodonapp.uk/@JdeBP/114590229374309238

Cheer21713• 1 year ago

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.

Cheer21713• 1 year ago

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.

moffkalast• 1 year ago

I was more about to point out that the 10x per 15 years for hardware hardly holds anymore for silicon and it's ridiculous to expect that to continue.

Y_Y• 1 year ago

it stands to reason that

The upper-class "trust me bro"

bombcar• 1 year ago

What happened to the programs/problems the Cray 1 solved? If anyone can do it on commodity hardware - is it being done? Is it all solved?

criddell• 1 year ago

Most are not solved but modern systems can generate better solutions. Think about problems like forecasting weather or finite element analysis of mechanical systems.

Cheer21713• 1 year ago

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.

acidburnNSA• 1 year ago

We still use a lot of the same software for nuclear reactor simulations. They just run a lot faster.

cratermoon2• 1 year ago

Work in computational fluid dynamics is limited by computing power. Bigger and faster computers give more accuracy and speed.

whartung• 1 year ago

A “famous” instance was the use of a Cray to render the collapse of Jupiter in the movie “2010”. A very early example of CGI in cinema.

johannes1234321• 1 year ago

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 ...

benob• 1 year ago

Cray1 should be compared to nowadays raspberry pi pico 2 / rp2350 which has similar specs (using external ram).

jgalt212• 1 year ago

I won't rest until the average microcontroller in an optical mouse is more powerful than a Cray 1.

1oooqooq• 1 year ago

try as you may, but that mouse will never work as a lounge center piece.

kdndnrndn• 1 year ago

I'm not aware of any optical mouse using a general purpose MCU, to my knowledge they are all using ASICs

qooiii2• 1 year ago

A lot of touchscreens meet that requirement. Turns out it's often cheaper to solve problems with algorithms than avoid them by design.

zouhair• 1 year ago

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.

cratermoon2• 1 year ago

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.

kayodelycaon• 1 year ago

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.

giantrobot• 1 year ago

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.

ajsnigrutin• 1 year ago

Hardware has gone a long way...

...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.

adgjlsfhk14• 1 year ago

otoh a pi running stockfish would beat deep blue 100-0

lawik• 1 year ago

No benchmarks. Hard to take this seriously.

_fat_santa• 1 year ago

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.

maxerickson• 1 year ago

Do you think they would have run experiments that have been missed in the meantime? Why?

mikewarot• 1 year ago

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.

Aardwolf• 1 year ago

Give it also an hdmi screen and usb keyboard, what more do you need to type code and see the result

dottedmag• 1 year ago

Serial port

Cray 1 was released 1975, teletypes were old tech at that time.

username2232• 1 year ago

What sort of crazy experiments would that have spawned?

Scientists then (at least a lot of them) knew what they wanted to do, and it required faster computers rather than more of them. A lot of that Cray power at the national labs was doing fluid simulation (i.e. nuclear explosions), and with the computers they had in the 80s, it was done in one or two dimensions, relying on symmetry. Going from n^2 to n^3 grid cells was the obvious next step, but took a lot more memory and CPU speed.

Havoc2• 1 year ago

Finding more & more that power efficiency is what's driving me towards new gear rather than lack of horsepower.

A few niche uses aside (gaming, llm) a vaguely modern desktop is good enough regardless of details.

dgacmu• 1 year ago

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.

nereye2• 1 year ago

The memory in the Cray was external and there are RP2350 boards with 16MB of QSPI flash, here’s one of them:

https://www.olimex.com/Products/RaspberryPi/PICO/PICO2-XXL/o...

mrheosuper2• 1 year ago

you can add psram to rp2350, which brings it close to cray

omega3• 1 year ago

Are there any details or examples of computational work the Cray 1 used for?

dahart3• 1 year ago

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.

magicalhippo• 1 year ago

1 Cray per pixel would be 49 Tera flops. A 4080 GPU has just shy of 50 Tflops peak performance

Interesting, wonder how it compares in terms of transistors. How many transistors combined did one Cray have in compute and cache chips?

dahart3• 1 year ago

The Wikipedia article says the Cray-1 has 200k gates. I assume that would mean something slightly north of 2x the number of transistors? https://en.wikipedia.org/wiki/Cray-1#Description

200k * 300k Cray-1s would be 60B gates, whereas the 4080 actually has 46B transistors. Seems like we’re totally in the right ballpark.

hattmall• 1 year ago

Nice, but the ~40 year latency is kind 0f high.

msgodel• 1 year ago

Well that's the way parallelism goes.

nottorp• 1 year ago

But the Cray had a general purpose CPU while the GPUs have specialized hardware. Not exactly apples to apples.

monocasa• 1 year ago

The main part of the Cray was a compute offload engine that asynchronously executed job lists submitted by front end general purpose computers that ran OSes like Unix.

It was actually pretty close to the model of a GPU.

phendrenad2• 1 year ago

Whitted mentioned! Cofounder of the first 3d game engine company.

ForOldHack• 1 year ago

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.

lttlrck• 1 year ago

However Moores Law refers to the number of transistors. Not FLOPS.

grubrunner666• 1 year ago

This thread should be a MasterClass. Awesome reading. Seriously. -a gen x'er.

smcameron• 1 year ago

And you can 3D print a Cray YMP case for your Raspberry Pi: https://www.thingiverse.com/thing:6947303

jwr• 1 year ago

What I find somewhat puzzling is that these machines were used for the "really big problems". We used supercomputers for weather forecasting, finite element simulations, molecular modeling. And we were getting results.

I don't feel we are getting results that are thousands of times better today.

motorest• 1 year ago

I don't feel we are getting results that are thousands of times better today.

You are getting results that are way better than thousands of times. You just aren't aware where they are showing up.

To give you a glimpse, the same modelling problems which a couple of decades ago tool days to come up with a crude solution are now being executed within a loop in optimization problems.

You are also seeing multiphysics and coupling problems showing up in mundane applications. We're talking about problems that augment the same modelling problems that a couple of decades ago tool days to solve with double or triple the degrees of freedom.

Without the availability of these supercomputers the size of credit cards, the whole field of computer-aided engineering would not exist.

Also, to boot, there are indeed diminished returns. Increasing computational resources unblocks constraints such as being able to use doubles instead of floats. This means that lowering numerical errors in 3 or 4 decimal places comes for free at the expense of taking around 4 times longer to solve the same problem.

To top things off, do you think the results of two decades ago were possible without employing a great deal of simplifications and crude approximations? As legend has it, the F117 Nighthawk got it's design due to the computational limits of the time. Since then, stealth planes became more performant and with a smoother design. That's what you get when your computational resources are a thousands times better.

adgjlsfhk14• 1 year ago

We aren't getting results thanks of times better. we're getting results 10s of times better on problems with cubic (or worse) scaling. e.g. 3 day forecasts as of 2017 are more reliable than 1 day forecasts in 1990 https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2F...

ziofill• 1 year ago

It is a frequent fantasy of mine to bring tech back to historical figures, like to show my phone to Galileo or to take Leonardo da Vinci for a ride in my car. But I guess you don't need to go that far to blow minds.

prmoustache2• 1 year ago

Just show it to someone being released after 30 to 40 years in jail.

Animats2• 1 year ago

Back in 2020, someone built a working model of a Cray-1.[1] Not only is it instruction compatible, using an FPGA, it's built into a 1/10 scale case that looks like a Cray-1.

The Cray-1 is really a very simple machine, with a small instruction set. It just has 64 of everything. It was built from discrete components, almost the last CPU built that way.

[1] https://www.cpushack.com/2010/09/15/homebrew-cray-1a-1976-vs...

Animats2• 1 year ago

(2010, actually.)

_tom_• 1 year ago

The pi has a sub $100 accelerator card that takes it to 30 TFLOPs. So you can add three more orders of magnitude of performance for a rough doubling of the price.

dale_huevo• 1 year ago

the Cray had about 160MFLOPS of raw processing power; the Pi has... up to 30GFLOPS. Yes... that's gigaFLOPS. This makes it almost 200 times faster than the Cray.

Imagine traveling back to 1977 and explaining to someone that in 2025 we've allocated all that extra computing power to processing javascript bundles and other assorted webshit.

usrnm• 1 year ago

That actually wouldn't be so bad, but in reality the number one usecase for raspberry pi is blinking leds for some time and collecting dust afterwards

darkwater• 1 year ago

Still a better user than crunching Javascript to show you ads and track you around.

hagbard_c• 1 year ago

No, the number one use case for Pies is being built into commercial hardware in the form of compute boards.

qingcharles• 1 year ago

In 2013 I'd just built a new top-spec PC. I looked up the performance and then back-calculated using the TOP500† and I believe it would have been the most powerful supercomputer in the world in about 1993. If you back-calculated further, I think around 1980 it became more powerful than every computer on the planet combined.

† https://en.wikipedia.org/wiki/TOP500

noobermin• 1 year ago

I guess I'm old because this hasn't really been that insightful of interesting observation just by itself anymore. People often talk about technological advancement of computing as if it is a force of nature whereas the amazing specs of say a rp2350 compared to the cray-1 is more of a story of the economies of scale as opposed to merely technical know-how and design. The reason a rp2350 is a few dollars is because of fabs, infrastructure, and institutional knowledge that likely dwarf the cost of producing a cray-1. I wouldn't even be surprised if someone bothered to do a similar calculation of the cost of infrastructure needed behind each cray-1 at the time that it could even be less what is needed to produce rp2350s today. The unit price of a rp2350 to consumers being so cheap (right now that fabs still want to make it) somewhat elides the actual costs involved.

Animats below said that the Cray-1 was made from discrete components. Good luck making a rp2350 from discrete components, it likely wouldn't even function well at the desired frequency due to speed of light and RF interference issues--it would likely be even worse for GHz broadcoms used in the rpi5. This means that in a post-apocolyptic future you could make another cray-1 given enough time and resources. In 20 years when the fabs have stopped making rp2350s there simply will not be any more of them.

adgjlsfhk14• 1 year ago

I think the really interesting post here is that a reasonably high level of computer is basically free. you can get a 32 but microcontroller with 16mb of ram at above 100mhz for well under $1. you can buy a USB cable and it has 2 full computers inside it.

mrheosuper2• 1 year ago

where do i find a MCU with 16MB ram for under $1 ?

username2232• 1 year ago

That was a weird turn to AI at the end, but otherwise an interesting reflection. I'm a little too young to have grown up in the era of the Cray-1, but even in the early 90s, processors ran at 90 MHz and hard drives cost $1 per megabyte. Back when personal computers ran at single-digit megahertz and had kilobytes of RAM, a Cray was mind-blowing.

The exciting part back then was that, while computers were never "good enough," they were getting noticeably better every few months. If you were in the market for a computer, you knew you could get a noticeably better one for the same price if you just waited a little while. The next model was exciting, because it was tangibly better. At some point personal computers became "good enough" for most people. Other than compensating for creeping software bloat, there hasn't been much reason for most people to be excited about new computers in a decade or more.

qgin2• 1 year ago

Yes but can you sit on your Raspberry Pi like this https://volumeone.org/uploads/image/article/005/898/5898/hea...

pjmlp3• 1 year ago

And then people burn all this progress shipping browsers with their application, because they can't be bothered to learn neither Web standards, nor native tooling.

nkotov• 1 year ago

You can see a Cray-1 at CHM. It looks so cool that pictures don't do it justice.

daotoad• 1 year ago

This little piece reminds me of a conversation I had about the power of modern mobile phones.

Given a modern flagship phone, what year was that phone equivalent to total world computational power?

For example, based on TFA, a Pi5 represents a around a thousand Cray 1 systems in 1977. Based on that seems likely that a single Pi5 outstrips total world supercomputer capacity in 1977.

We tossed some numbers around, but the rough consensus was that the were likely to have the 60s covered and most of the 70s as well. Given that this was a decade ago, I expect that we could move forward a few years.

ninetyninenine• 1 year ago

Let’s hope this happens for LLM hardware.

Cray versus Raspberry Pi

Related Stories