It’s kind of lame that they need to junk the entire apparatus after only a decade. I get that processor technology moves on apace but we already know it does that so why doesn’t a universal architecture exist where nodes can be added at will?
It’s more of an operating cost issue. It’s almost decade-old hardware. It was efficient in its day, but compared to new hardware it just costs so much to run you would be better served investing in something with modern efficiency. It won’t be junked, it will be parted out. If you are someone that wants a cheap homelab with infiniband and shitloads of memory you could pick up a blade for a fraction of what it would otherwise cost. I fully expect it to turn into thousands of reasonably powerful servers for the prosumer and nerd markets instead of running as a monolithic cluster.
One of the reasons why I work in industrial controls. A good day is me sneaking in tech that came after the year 2000. Employment for life and I get to branch out to related stuff. Employer is paying me to take ME and chem-e classes now.
I don’t know why anyone would spend their life chasing the newest fad tech when you can pick a slow moving one, master it, and master the ones around it. Would much rather be the person who knew how the entire system works vs knowing the last 8 programming languages/frameworks only 1 of which is relevant.
But hey glad there are people who decide on that lifestyle I like having a better cellphone every year.
If you have too many “slow” modes in a super computer you’ll hit a performance ceiling where everything is bottle necked by the speed of things that are not the CPU: memory, disk for swap, and network for sending partial results across nodes for further partial computing.
Source: I’ve hang up too much around people doing PhD thesis in these kinds of problems.
I would imagine it’s very difficult to make a universal architecture but if I have learnt anything about computers it’s that the manufacturers of software and hardware deliberately created opaque and monolithic systems, e.g. phones. They cynically insert barriers to their reuse and redeployment. There’s no profit motive for corporations to make infintitely scalable computers. Short sighted greed is a much more plausible explanation.
When you get to write and benchmark your own code you’ll see technology has limits and how they impact you.
You can have as many raspberry pis as you want, and accomplish faster computation if you can use the same budget with Xeon on dozens of MB in cache and hundreds of gb in ram with gigabit network cards.
10 years from now these Xeon will be like rpi compared to the best your money can buy.
All of those things have to fit in a building, not a desk. The best super computers look like Google’s data centers, but their specific needs dictate several tweaks done by very smart people. Super computers are supposed to solve 1 problem with 1 set of data at a time, not 100 problems with 1000,000 data set/people profiles at a time which are much easier to partition and assign to only 1000th of your data center at a time.
It’s kind of lame that they need to junk the entire apparatus after only a decade. I get that processor technology moves on apace but we already know it does that so why doesn’t a universal architecture exist where nodes can be added at will?
It’s more of an operating cost issue. It’s almost decade-old hardware. It was efficient in its day, but compared to new hardware it just costs so much to run you would be better served investing in something with modern efficiency. It won’t be junked, it will be parted out. If you are someone that wants a cheap homelab with infiniband and shitloads of memory you could pick up a blade for a fraction of what it would otherwise cost. I fully expect it to turn into thousands of reasonably powerful servers for the prosumer and nerd markets instead of running as a monolithic cluster.
A decade is a lifetime in technology. Moore’s law had just ended when this was put together.
One of the reasons why I work in industrial controls. A good day is me sneaking in tech that came after the year 2000. Employment for life and I get to branch out to related stuff. Employer is paying me to take ME and chem-e classes now.
I don’t know why anyone would spend their life chasing the newest fad tech when you can pick a slow moving one, master it, and master the ones around it. Would much rather be the person who knew how the entire system works vs knowing the last 8 programming languages/frameworks only 1 of which is relevant.
But hey glad there are people who decide on that lifestyle I like having a better cellphone every year.
If you have too many “slow” modes in a super computer you’ll hit a performance ceiling where everything is bottle necked by the speed of things that are not the CPU: memory, disk for swap, and network for sending partial results across nodes for further partial computing.
Source: I’ve hang up too much around people doing PhD thesis in these kinds of problems.
I would imagine it’s very difficult to make a universal architecture but if I have learnt anything about computers it’s that the manufacturers of software and hardware deliberately created opaque and monolithic systems, e.g. phones. They cynically insert barriers to their reuse and redeployment. There’s no profit motive for corporations to make infintitely scalable computers. Short sighted greed is a much more plausible explanation.
When you get to write and benchmark your own code you’ll see technology has limits and how they impact you.
You can have as many raspberry pis as you want, and accomplish faster computation if you can use the same budget with Xeon on dozens of MB in cache and hundreds of gb in ram with gigabit network cards.
10 years from now these Xeon will be like rpi compared to the best your money can buy.
All of those things have to fit in a building, not a desk. The best super computers look like Google’s data centers, but their specific needs dictate several tweaks done by very smart people. Super computers are supposed to solve 1 problem with 1 set of data at a time, not 100 problems with 1000,000 data set/people profiles at a time which are much easier to partition and assign to only 1000th of your data center at a time.