Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

the-diy-life.com

Post reply on HN
Print view View original

11–20 of 70 posts

Re: Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

#11

Post by tumblewit »

nielsbot wrote: post #7

I assumed it was the M1. (It's an Intel MacBook.)

same. the m1 would be much faster. also the water cooling is going to add cost which can be reused to add more Pi’s instead and I know i can run mine at 1800Mhz with a tiny heatsink via thermal paste and a 40mm fan with it not going above 65C so the cluster with a large fan would be very easy to cool

Re: Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

#12

Post by neatze »

m463 wrote: post #10
neatze wrote: post #9

Earlier quoted context omitted.

this is with a fixed multiprocessing script ?

is this what you mean? FindingPrimesMulti-1.zip

If you have mpi4py then I would try this one:

https://github.com/joshjerred/mpi4py-with-multiprocessing-Ch...

To me this would be more fair comparison between single cpu and pi cluster.

Re: Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

#13

Post by toyota86 »

The test script didn't share the load across processors so the author is rerunning the tests. It seems the graphs therefore are misleading (for now).

Nevertheless this is not as interesting as testing the M1 chip on the latest MacBook offering. I feel a bit misled but perhaps it was just my fondness for the M1 causing this bias.

Re: Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

#15

Post by artiscode »

I would really like to see how a raspi cluster fares with "real world" loads, like running an app or a distributed system, instead of calculating pi. I'm genuinely curious whether there would be any gains by running Docker containers, each on a separate Raspberry Pi. I personally am a web developer, therefore my technology stack is almost always the same - an app server, a background worker, a queue and a database. Often the app server and the background worker is the same process, therefore a cluster of 3-4 Pi's would be sufficent for such workloads. Theoretically the combined horsepower of all these Pi's should stack up and deliver better performance than writing code on my M1. Or perhaps I'm trying to solve a problem that doesn't exist.

Re: Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

#16

Post by b3lvedere »

artiscode wrote: post #15

I would really like to see how a raspi cluster fares with "real world" loads, like running an app or a distributed system, instead of calculating pi. I'm genuinely curious whether there would be any gains by running Docker containers, each on a separate Raspberry Pi. I personally am a web developer, therefore my technology stack is almost always the same - an app server, a background worker, a queue and a database. O…

I think that's a great idea! Personally i'd like a nice failover test using a Pi Cluster. So if Pi's when failing and getting replaced on the fly if processes would still be up and running flawlessly. Probably a pretty useless and expensive setup, but i think it'd be awesome to watch.

Re: Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

#17

Post by albanread »

toyota86 wrote: post #13

The test script didn't share the load across processors so the author is rerunning the tests. It seems the graphs therefore are misleading (for now). Nevertheless this is not as interesting as testing the M1 chip on the latest MacBook offering. I feel a bit misled but perhaps it was just my fondness for the M1 causing this bias.

I can confirm for you; that there is no rational reason to assume the authors own MacBook is a new M1 system.

Re: Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

#18

Post by tpkm »

m463 wrote: post #8

I tried FindingPrimesMulti.py on a 3970x: Find all primes up to: 10000 using 256 processes. Time elasped: 0.51 seconds Number of primes found 1229 Find all primes up to: 100000 using 256 processes. Time elasped: 36.71 seconds Number of primes found 9592 Find all primes up to: 200000 using 256 processes. Time elasped: 149.55 seconds Number of primes found 17984 EDIT: ran it again: Find all primes up to: 200000 using 2…

That's interesting - I just tried on my Ryzen 5 3600 and got the following:

10k using 48 processes: 0.82 seconds (using the original single threaded script this was actually faster at 0.65)

100k using 48 processes: 28.41s

200k using 48 processes: 99.68s

--- EDIT - looking at resource monitor python.exe is only using 7-8% of total available CPU resources

--- EDIT 2 - switching to ThreadPool from multiprocessing.dummy brought the 10k result down from 0.8 seconds to 0.3 seconds, but didn't impact the 100k or 200k results

Re: Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

#19

Post by danbruc »

There are only 1229 primes up to 10,000, not 1230 as the article says. Not sure whether this is a bug in the code or a typo in the article. I still remember this because 25 years ago as a teenager I spend quite some time making primality testing as fast as I could. My implementation was certainly not as naive as the one from the article - only testing up to the square root of n, only testing against primes I had found before - but not sophisticated in any way, for that I lacked the mathematical knowledge. I can not exactly remember how fast I got it but I am pretty sure it was sub-one-second, like 0.2 or 0.3 seconds maybe for the range up to 10,000. On a 50 MHz i486.

Re: Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?

#20

Post by thraway123412 »

artiscode wrote: post #15

I would really like to see how a raspi cluster fares with "real world" loads, like running an app or a distributed system, instead of calculating pi. I'm genuinely curious whether there would be any gains by running Docker containers, each on a separate Raspberry Pi. I personally am a web developer, therefore my technology stack is almost always the same - an app server, a background worker, a queue and a database. O…

> Theoretically the combined horsepower of all these [4] Pi's should stack up and deliver better performance than writing code on my M1.

If that is the case, then M1 should be the slowest CPU Apple has used in the past 10 years.

Post reply on HN