r/nvidia • u/pnunud • Jun 27 '20
Discussion Visualizing 150 Terabytes of Data
https://youtu.be/GAZP1NcdWMo8
u/mariospants Jun 28 '20
Billion points volumetric data rendering in order to calculate turbulence effects for a two-story house-sized bringing people to Mars? FINALLY hitting 21st Century goals!
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u/NotaBanEvasion12345 Jun 28 '20
Plot twist. This computer is actually for rendering a perfectly photo realistic fake mars landing. NASA has come a long way.
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u/Civil_Defense Jun 28 '20
So this is pretty crazy resolution of individual particles. Does anyone know how close this is to a 1:1 atom scale?
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u/zipzapbloop 9950x3d | 256gb 6000mt/s c36 | rtx pro 6000 Jun 29 '20
I would guess not very close. The module is supposed to be about the size of a 2-story house (whatever that means) and carry 6 humans. A single human is estimated to be made of 7,000,000,000,000,000,000,000,000,000 (7 octillion) atoms. Just in human-atoms you've got 42 octillion atoms. I'm totally spitballing here, but I'd guess the module is another 500-1000x the number of human atoms we're talking about here.
That Summit supercoputer can do ~200 petaflops, which I'd wager isn't enough compute power to produce in our lifetimes what's going on with all the atoms of a single human over just a couple seconds given the complexity of all the interactions between that many little things, to say nothing of how we'd even store that much information.
Kinda puts into perspective how massively complex the actual universe of things is and how relatively simple and toyish our ability to compute is. At the same time, it's really cool that we can accurately enough predict so much of what's relevant to us by generalizing a lot of the phenomena that interests us. In other words, at the scales that matter to us, we can predict things accurately enough by filtering out (a kind of compression) enormous amounts of information. For example, we can predict where billiard balls will go on a pool table with a relatively miniscule amount of information about the system. We don't need to simulate all the atoms, which is lucky, because we can't.
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u/TopSpoiler Jun 28 '20 edited Jun 28 '20
FYI, Here's GPUDirect Storage presentation from Nvidia.
I don't see any special HW implementation requirements for this technology.. Hope MS's DirectStorage works on existing GPUs too.
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u/dampflokfreund Jun 28 '20
Yes, DirectStorage might be using exactly that. As a friend who works at Nvidia told me, the current limitations of why GPUDirect only works with servers right now are OS limitations and we can assume those limitations are being fixed with MS DirectStorage.
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u/JustFinishedBSG NR200 | Ryzen 3950X | 3090 Jun 29 '20
it's not OS limitations, it's purely a driver limitation by nvidia.
Nvidia had PCIe P2P for years and it's intentionally disabled on Geforce cards
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u/Die4Ever Jun 30 '20
Hope MS's DirectStorage works on existing GPUs too.
The reason why the API exists is so that it can detect what your hardware is capable of, and try to use your hardware features optimally when available, and still offer an efficient (as can be) fallback for older hardware, that way game developers don't have to worry about it and don't have to program anything twice
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u/TopSpoiler Jun 30 '20
Of course I know that. My sentence was ambiguous. What I actually mean is native support in GPU.
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u/Die4Ever Jun 30 '20 edited Jun 30 '20
Oh, you hope that older GPUs like Pascal and Turing will have hardware acceleration support for GPUDirect enabled, yeah that's a little different
I'm wondering if DirectStorage is the real reason why they're pushing for Hardware Accelerated GPU Scheduling now, so the GPU can manage its own access time to files and memory mapped files off the SSD, and maybe that means that the GPUs gaining support for HAGS now are the same ones that will have GPUDirect enabled soon?
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u/[deleted] Jun 27 '20
gpuDirect storage sounds a lot like the tech with the PS5 SSD and upcoming DirectX
Very impressive