2 ms·

My first thought when I saw this was Dan Luu's recent blog post on futurism and how to accurately predict the future (https://danluu.com/futurist-predictions/ h

My first thought when I saw this was Dan Luu's recent blog post on futurism and how to accurately predict the future (https://danluu.com/futurist-predictions/ https://danluu.com/futurist-predictions/). And on that scale of futurists, the one presented here does seem to be on the less accurate scale of things--the understanding does seem to be closer to cocktail party (or perhaps more accurately "I once saw a technical talk") than the deep domain expertise that appears to be necessary for accurate predictions. I'm only a compiler developer, so my knowledge of computer architecture, operating systems, and formal methods aren't all that great--but it does appear stronger than the author. Like the statement of how processors work today in prediction 4 is just plain wrong (as is the analysis of OS complexity in prediction 7), and my experience in exotic accelerator hardware paradigms makes me lol at prediction 3.

deleted 4y ago
[deleted]
drpixie 4y ago
Yeah - I look at the last few decades of computing and see the great god of compatibility driving almost everything. It's all very well the author seeing some cute ideas and running with them, but not many are in a position to discard everything, so we will do as we've done for the last few decades and pick the technology that lets us build on what we've got. You don't have to look far for examples. Remember Intel Optane memory - a genuine improvement to memory. But because it doesn't match well with existing OS and apps, Intel struggled to do much with it, and seems to have largely dropped it :( I'd love if even a few had the opportunity to tidy-up our decades of history and mistakes, but I don't see it happening.
- api 4y ago
  This can be summarized as “hardware and software coevolve.” Neither can deviate too terribly from the constraints and requirements of the other. Of the two software can perhaps deviate more, but at the cost of performance. Software that deviates too far from hardware ends up effectively being an emulator for alien hardware. Hardware is more constrained. If there is no software nobody uses it. There is perhaps a way out in the form of JITs and translators and emulators but it’s very difficult to make that work well. The current crop of x64 to ARM64 translators found on Mac and Windows ARM PCs are one of the few cases where I have seen that work. Intel with Optane should have just presented it as a crazy fast hard drive specializes for databases. If you can get some use case going the hardware can find a niche and then software makers will start exploring what else it can do.
  - imachine1980_ 4y ago
    i feel hardware evolve and we now can use more of we already develop decades ago but was inpractical, i fell not as much progres is made in the software side of thing dont get me wrong we got better compilers, and software piplelines. but i feel most of our stack isnt more productive, we are moving in circuls dont get me wrong cloud is cool but is 90% hardware, the same ci/cd and deep learning therese some spark of brilaince software desing surrrealdb for example but is small and sparce and in generals this type of ideas doesnt steak even if they are better
  - 3pt14159 4y ago
    Exactly. This is all an economic question that involves humans, how plentiful they're trained in different topics, the availability of different resources, security requirements (Cold War 2.0 ups security costs, for example), and many other unknown factors like relative breakthroughs in AI, physics, quantum, etc. But that said, I think point 2 (much more bizarre hardware) is basically guaranteed. As OSS and cloud provided services (e.g., RDS) centralizes our approaches to common problems to a small set of commonly used libraries the common functions of those libraries will continue to get baked into hardware and lower and lower overheads (not just chips, think busses between, etc). Basically TPUs and M2s are the pretremors of what is to come. I basically think of this all as a massive economic / search problem with highly aligned actors between their neighbouring layers. Though not necessarily beyond their neighbouring layers. For example, breakthroughs in mathematics or in AI driven mathematics could, say, 10x LLVM optimization for common tasks which would dampen demand for CPU in the short to medium term until the economic growth catches up to demand even more in the long term.
  - pbronez 4y ago
    I've also seen Optane recommended as cache for ZFS pools[0]. I wonder if Intel is sufficiently disillusioned with Optane to license it to another manufacturer. Particularly with the CHIPS act in place, perhaps there's room for someone to take another run at it. [0] https://www.servethehome.com/exploring-best-zfs-zil-slog-ssd-intel-optane-nand/ https://www.servethehome.com/exploring-best-zfs-zil-slog-ssd...
    - 4y ago
deepnotderp 4y ago
On the contrary, evidence has repeatedly shown that deep domain expertise results in tunnel vision and worse predictions. See eg https://www.smithsonianmag.com/smart-news/why-experts-are-almost-always-wrong-9997024/ https://www.smithsonianmag.com/smart-news/why-experts-are-al... or the fact that breakthrough papers get more citations outside their field at first than within it
galangalalgol 4y ago
There are several technologies that can make transistors that are too expensive to pursue in earnest until 2nm reaches commodity pricing which means multiple companies making fabs near that performance point. That will be a while probably, but it will be inside a hundred years, unless we end ourselves. Some of those technologies may exceed silicon performance and allow our continued growth. After 2nm hits commodity pricing, but before graphene or diamond or whatever hit feature parity, I wouldn't be surprised to see more intrinsics popping up to support common usecases in devoted silicon, but that just makes more work for you as a compiler developer. I do think in the nearer term, computing will get less abstracted and people will spend more time on optimization. Not because of moores law, but because the demand for new chips is far exceeding supply from bleeding edge fabs, and it will take a long time to catch up. This may get exacerbated by politics and shrinking manufacturing workforces. So we will have to do more with older tech. These exotic architectures the author posits have to get fabbed by someone, and they won't do that while they can use existing designs to print money.

deleted 4y ago

[deleted]

drpixie 4y ago

Yeah - I look at the last few decades of computing and see the great god of compatibility driving almost everything. It's all very well the author seeing some cute ideas and running with them, but not many are in a position to discard everything, so we will do as we've done for the last few decades and pick the technology that lets us build on what we've got. You don't have to look far for examples. Remember Intel Optane memory - a genuine improvement to memory. But because it doesn't match well with existing OS and apps, Intel struggled to do much with it, and seems to have largely dropped it :( I'd love if even a few had the opportunity to tidy-up our decades of history and mistakes, but I don't see it happening.

api 4y ago
This can be summarized as “hardware and software coevolve.” Neither can deviate too terribly from the constraints and requirements of the other. Of the two software can perhaps deviate more, but at the cost of performance. Software that deviates too far from hardware ends up effectively being an emulator for alien hardware. Hardware is more constrained. If there is no software nobody uses it. There is perhaps a way out in the form of JITs and translators and emulators but it’s very difficult to make that work well. The current crop of x64 to ARM64 translators found on Mac and Windows ARM PCs are one of the few cases where I have seen that work. Intel with Optane should have just presented it as a crazy fast hard drive specializes for databases. If you can get some use case going the hardware can find a niche and then software makers will start exploring what else it can do.
- imachine1980_ 4y ago
  i feel hardware evolve and we now can use more of we already develop decades ago but was inpractical, i fell not as much progres is made in the software side of thing dont get me wrong we got better compilers, and software piplelines. but i feel most of our stack isnt more productive, we are moving in circuls dont get me wrong cloud is cool but is 90% hardware, the same ci/cd and deep learning therese some spark of brilaince software desing surrrealdb for example but is small and sparce and in generals this type of ideas doesnt steak even if they are better
- 3pt14159 4y ago
  Exactly. This is all an economic question that involves humans, how plentiful they're trained in different topics, the availability of different resources, security requirements (Cold War 2.0 ups security costs, for example), and many other unknown factors like relative breakthroughs in AI, physics, quantum, etc. But that said, I think point 2 (much more bizarre hardware) is basically guaranteed. As OSS and cloud provided services (e.g., RDS) centralizes our approaches to common problems to a small set of commonly used libraries the common functions of those libraries will continue to get baked into hardware and lower and lower overheads (not just chips, think busses between, etc). Basically TPUs and M2s are the pretremors of what is to come. I basically think of this all as a massive economic / search problem with highly aligned actors between their neighbouring layers. Though not necessarily beyond their neighbouring layers. For example, breakthroughs in mathematics or in AI driven mathematics could, say, 10x LLVM optimization for common tasks which would dampen demand for CPU in the short to medium term until the economic growth catches up to demand even more in the long term.
- pbronez 4y ago
  I've also seen Optane recommended as cache for ZFS pools[0]. I wonder if Intel is sufficiently disillusioned with Optane to license it to another manufacturer. Particularly with the CHIPS act in place, perhaps there's room for someone to take another run at it. [0] https://www.servethehome.com/exploring-best-zfs-zil-slog-ssd-intel-optane-nand/ https://www.servethehome.com/exploring-best-zfs-zil-slog-ssd...
  - 4y ago

deepnotderp 4y ago

On the contrary, evidence has repeatedly shown that deep domain expertise results in tunnel vision and worse predictions. See eg https://www.smithsonianmag.com/smart-news/why-experts-are-almost-always-wrong-9997024/ https://www.smithsonianmag.com/smart-news/why-experts-are-al... or the fact that breakthrough papers get more citations outside their field at first than within it

galangalalgol 4y ago

There are several technologies that can make transistors that are too expensive to pursue in earnest until 2nm reaches commodity pricing which means multiple companies making fabs near that performance point. That will be a while probably, but it will be inside a hundred years, unless we end ourselves. Some of those technologies may exceed silicon performance and allow our continued growth. After 2nm hits commodity pricing, but before graphene or diamond or whatever hit feature parity, I wouldn't be surprised to see more intrinsics popping up to support common usecases in devoted silicon, but that just makes more work for you as a compiler developer. I do think in the nearer term, computing will get less abstracted and people will spend more time on optimization. Not because of moores law, but because the demand for new chips is far exceeding supply from bleeding edge fabs, and it will take a long time to catch up. This may get exacerbated by politics and shrinking manufacturing workforces. So we will have to do more with older tech. These exotic architectures the author posits have to get fabbed by someone, and they won't do that while they can use existing designs to print money.