Discussion in 'Architecture and Products' started by DSC, Mar 19, 2013.
I can only think of it meaning:
Power of sram + sys ram > Power of sys ram
I guess, but energy is what matters, and you're going to use much less of it while fetching something from an SRAM cache than from off-chip DRAM in all cases that I know of.
Yes, but they are talking about an special type of applications. And my assumption is that the processor does have L2 cache, while the additional SRAM functions as an L3. I'm guessing that such L3 acts just an intermediate step that simply consumes energy while doing almost nothing in the cases that they are describing.
Volta steps up:
"ORNL researchers have figured out how to harness the power and intelligence of Summit’s state-of-art architecture to successfully run the world’s first exascale scientific calculation. A team of scientists led by ORNL’s Dan Jacobson and Wayne Joubert has leveraged the intelligence of the machine to run a 1.88 exaops comparative genomics calculation relevant to research in bioenergy and human health. The mixed precision exaops calculation produced identical results to more time-consuming 64-bit calculations previously run on Titan."
Faster and smarter. Nice work, IBM, NV and Volta.
"ORNL scientists were among the scientific teams that achieved the first gigaflops calculations in 1988, the first teraflops calculations in 1998, the first petaflops calculations in 2008 and now the first exaops calculations in 2018."
I sense... a pattern (although I am pretty sure first gigaflop system went up in 1985).
Were the previous records all on double precision?
NVIDIA announced the TITAN V CEO Edition at the Computer Vision and Pattern Recognition conference yesterday. 20 of these GPUs were given away at the conference, but there is no general release or pricing information at this time.
I honestly thought the name was a joke when I first saw it (from a secondary source).
The TITAN V CEO Edition has specs similar to those of the Tesla V100:
32 GB memory,
125 Tensor Core TFLOPS.
AnandTech has a spec table and speculation here.
I wonder if bandwidth was a big reason for the CEO Edition. From the AnandTech article, "bandwidth-bound scenarios are more common than one might think, as the regular Titan V can fully saturate its memory bandwidth on compute alone and still come up short," which is not surprising to me after reading posts on Beyond3D. If this product gets a wider release in the future then the TITAN line would have a higher bandwidth option.
The 12GB Titan V is around 9-17% slower in Amber for Solvent FP32 compute than the 16GB V100 PCIe.
Gives a bit of an indicator, but only partially helpful.
Deep Learning SDK Documentation
June 18, 2018
New GPU-Accelerated Supercomputers Change the Balance of Power on the TOP500
AI Can Now Fix Your Grainy Photos by Only Looking at Grainy Photos
Researchers from NVIDIA, Aalto University, and MIT developed a deep learning based method that can fix photos by simply looking at examples of corrupted photos only.
The NVIDIA Titan V Deep Learning Deep Dive: It's All About The Tensor Cores
July 3, 2017