But again, you’re assuming that I am going to run out of RAM though.
That’s clearly NOT the case here.
Again, I have 169 GB of RAM free and an additional 271 GB that’s held by buff/cache that can be freed up for a grand total of 440 GB of RAM that can be freed.
I’m nowhere remotely close to running out of RAM.
Dunno about the memory being idle when about 324.4 GiB is currently in use. The rest of it, sure.
System LA is about 38 right now.
“It has no way of knowing if you’re going to have an inbound memory request for 300 GB of memory imminently.”
By that logic, it would be swapping out CONSTANTLY, since it will never know what it’s about to hit it.
(The last time I tried, it took somewhere between like 28-33 seconds to load a 400 GB sparse matrix into RAM. Despite the theoretical bandwidths, sparse memory allocation doesn’t really happen all that quickly. Dense matrix memory allocation is even slower.)
It really depends on how much RAM your system has installed and what you’re using/doing.
When you’re working with direct, sparse matrix solvers (for finite element analysis) all day, the scratch disk file alone was like 100 GB for a model that only had something like 500k elements. So, relatively speaking, it’s a pretty small model, but it scratches out a TON of data, during the solve process.
If you’re just browsing Facebook, you can probably have it last well over 100 years.
I killed 7 drives in 7 years which averaged out to killing a drive annually.
It’s really NOT that difficult to do, depending on what you’re doing.
(A single Monte Carlo simulation can consume 2% write endurance on an E1.S drive. It’s really not that hard, but again, it really depends on what you’re doing.)
CFD writes a lot of data at the end, but not as much during the iterative solution process.
FEA, when you’re using the direct sparse solver, craps out a TON of data, during the solution process, such that it wasn’t uncommon for me to be scratching about 100 GB per run, and swap behaving as it does, burned through a SSD annually, on average, for me.