What Is Specialized Hardware and Why Open Source Will Drive Adoption | Hacker News Reader

What Is Specialized Hardware and Why Open Source Will Drive Adoption

metal.equinix.com

76 pointsmrmrcoleman14 comments

This article didn't mention PIM (processing in memory) which is a way of keeping the established general purpose CPU model and accelerating it by simply adding processors directly into RAM. The compute power scales with the size of your dataset. You also benefit from greater memory bandwidth and lower power consumption.

Here is an existing implementation: https://www.upmem.com/

https://en.wikipedia.org/wiki/Computational_RAM and http://iram.cs.berkeley.edu/

I ran into one of these companies, in 2015? at a big data conference. The founders claimed to have not known about iram, ;) It feels like PIM comes up every couple years, but ram is expensive already, and these chips are going to be some multiple more expensive. There is some inflection point ,memory bandwidth, power, something that will enable PIM to finally get traction.

The immediate problem was tooling, os support, supply, scale, etc. I think it would make a lot of sense to get this stuff installed sitewide after your code has been tuned for support. So it needs a good simulator or it needs an interposer so one can code to the ABI of the hardware.

I could see a big super computer initiative being used a test bed, probably one designed for genomics. I think we overestimate how effective and easy to use PIM (processing in ram) could be. Where it will really start to shine is when the processor can retire instructions into the PIM, pre-materialize data streams from the PIM, do a scheduled reduction and then push compute bundles back to memory. The computational ram needs to be integrated with cpu, it can't just be an accelerator.

Computational disks might be way way easier to implement. We will know how that is going as soon as Western Digital tells us.

https://github.com/chipsalliance/Cores-SweRV

Thanks for the references sitkack

Presumably your computing power scales with the number of 'data processors' rather than the size of your dataset per se. In the same way that you might say "if you use GPUs your computing power scales with the size of your dataset" (as you buy more GPUs to get more memory, thus get more compute).

But how does this differ to a CPU or GPU? Both are just DRAM with something bolted onto the side, and both have a limited bandwidth between the DRAM and the processors. The difference with putting the processors on the DIMMs is that you now have an extremely restrictive thermal envelope to work within (and if you put it on the same die, which I hope you're not, then a CMOS technology that's supremely ill suited to computation).

Hey, thanks for the link! This post will be followed up with a series of articles that will go into more detail about the various technologies. I'll be sure to include PIM in my research.

> Kubernetes and its family of cloud native projects revolutionized computing in 4 short years.

This strikes me as wild hyperbole. It's a new management layer for server-side computing -- nothing compared to the changes brought by microprocessors, or even by minicomputers like the DEC PDP line.

Even more so if you can cram more and more processor cores into a chip, or more and more sockets into a board. It's easy to envision and multi-socket ARM server making more than a few micro service based architectures unnecessary.

I guess the most relevant question within an open source context is, "okay, but when/how do we get to play with this fancy new hardware?"

OpenPOWER remains a massive niche - as in, it's big, but it's still a niche - because nobody (in the sense of "just anybody", ie the long tail) can really get their hands on it. It's nontrivially difficult to maintain access to IBM POWER8 kit, and the Talos is also certainly way beyond what I could personally afford from a "directionless tinkering/learning" standpoint.

If I understand this article correctly, cloud-native means there's not likely to be a Talos equivalent for sale, and it's all remote access only.

Also, OpenPOWER is, like, an entire CPU, with a design is extremely old and can be expected to stay around for a long time, and even with that sort of centralizable focus opportunity it's still a niche.

I get the impression this is suggesting the creation of custom components with somewhat shorter design lifecycles - years, certainly, but not multiples of decades, and maybe only months for individual hardware revisions.

If this really wants to attract developers from outside of the immediate focus of the relevant industries... how are the discoverability and accessibility equations going to be solved?

Of course, the more potential cooks you attract to the kitchen the more overheads you have to deal with, but I wonder if is a necessary element to maintain interest and familiarity with what would apparently prefer to be a fast-changing environment.

"how are the discoverability and accessibility equations going to be solved?" - this is the whole challenge.

I referred to the WorksonARM project in the blog: https://www.worksonarm.com/

WorksOnARM solved this problem for ARM by making machines available through Equinix Metal's API for development and testing.

Hardware manufacturers can ship boxes to developers, give them access through an API like Equinix Metal's, or some other approach, but one way or another developers are going to need access.

I am unclear of the article's target.

Your article implies we are reading about HW manufacturers that have prioritization & work load issues, but then you mention Apple, AWS, et al. These HW designs are all directed work to the HW manufacturer. There is no concerns of prioritization, or work load. They get paid handsomely for making the right choices.

It is a not so hidden message to industry about what Equinix is doing in the cloud server space.

Does anyone have insight on what implications follow from Jim Keller's Tenstorrent graph processing chip? He speculates the future may have a $5 add-on chip to a child's toy which will have more intelligence than a human on #162 Lex Fridman Podcast. I get how the single threaded raster pixel gpu architecture is less evolved than a graph processor.

I saw that, his comment was more around what the future could look like in 20-30 years. I don't think he was talking about their next product. Think Sturgeon and PKD vantage points and timelines.

https://en.wikipedia.org/wiki/Explicit_data_graph_execution

And now I have fallen back down the Epiphany [1] rabbit hole. The main thing that graph processors get you is being able to avoid the von Neumann Bottleneck [2]

[1] https://en.wikipedia.org/wiki/Adapteva

[2] https://en.wikipedia.org/wiki/Von_Neumann_architecture

On the "intellectual bottleneck", perhaps we will see exponential improvement in the next 10 years now that aesthetes in the political realm see the value in a TSMC.

The EU has a draft paper out for 2030.

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