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Comment by bullen | Hacker News Reader

I find it fascinating that companies still do not mention the Gflops/watt/$ for their products?

I mean I understand why, because the raspberry pi 4 melts all competition into the ground, but why nobody is asking is my concern.

If you need atomic parallelism you should be fine with a 25W Atom 8-core machine that you can passively cool.

Also would be interesting to see how this performs in a atomic parallel scenario? My guess is my HTTP server would not perform so well because the selector thread would not be able to service 79 other cores but I might be wrong about that.

I'm pretty sure the RAM will throttle the 80 cores if they work on a joint problem though!

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If you care about GFLOPS/Watt/$ then GPUs beat everything. Real requirements are usually more subtle, including things like compatibility with existing software, latency (single-thread performance), and management overhead per node.

That is my impression, it would still be good be good have an at least semi/partly objective evaluation of all the parallel compute devices that one can see appearing.

Also, to have a number of benchmarks involve memory, caching, etc.

I’m somewhat ignorant here, and indeed also niche (I’m a NetBSD user), but:

> GPUs beat everything

that’s workload dependant, and also subject to support, no? Doesn’t GPGPU basically mean CUDA currently, and therefore beholden to Nvidia support for your hardware/software platform?

AMD GPUs are supported over their open-source ROCm builds. HIP (CUDA-clone) works with Vega and CDNA.

It seems like RDNA (5xxx and 6xxx cards) are less supported, but reports are that OpenCL kinda-sorta works (RDNA cards have a very different architecture than Vega / CDNA)

Nah, I was playing around with an AMD integrated gpu a while ago using openCL (I believe).

> Doesn’t GPGPU basically mean CUDA currently

Depends. If the programmers take the path of least resistance, sure it gonna be CUDA. Personally, I made quite a few things on top of DirectCompute i.e. vendor agnostic.

> I find it fascinating that companies still do not mention the Gflops/watt/$ for their products?

GPUs win in TFlops (like 20 or 40 TFlops these days), and TFlops/watt (A modest 20 TFlop GPU these days would be under 400W).

GPUs also have 1TBps memory bandwidth thanks to HBM2 (on the high end), or at least 500GBps (thanks to GDDR6 on the low end).

Any serious compute problem with "obvious" parallelism will run on a GPU these days. CPUs are for sequential problems... (and running many sequential problems in parallel: which GPUs are kinda bad at actually. No GPU would ever be able to serve web-pages like a CPU: branch divergence is just too high)

Yap,

Someone likely would just docker/vm to partition that 80 cores into micro-service handle httpd proxy and app, db backend.

Those app/VM can easily be converted to app running pi modules.

20 pi modules likely have much better DDR, SSD, Network bandwidth. Probably scale from 2 pi to 200 pi as easily as typical vm setup - and it comes with GPUs for free for those need them.

> 20 pi modules likely have much better DDR, SSD, Network bandwidth

20 pi modules of 8GBs is only 160GBs.

Ignoring that: Bigger nodes are better in all practical scenarios. With 768 GBs of RAM, this singular big server can likely keep in-memory a large collection of information (ex: all of English-Wikipedia likely fits inside of that RAM).

20x Rasp. Pi cannot access all of English-Wikipedia in RAM. This means that you can't index, you can't search, you can't analyze the pages. Even if you could: you'd need to have a collaborative external memory model, which is not easy to program.

80-cores with access to all 768GBs can do many, many more things than 20x 4-core Rasp. Pi working on only 32GB at a time.

All if the text of English Wikipedia is only ~12 Gb last time I checked a year or so ago. English Wikipedia with pictures is 10+ Tb, but your point stands about memory needed.

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