All stock codes associated to this product
900220800000000, 900-22080-0000-000, 11LTESLAK80M, 11LTESLA-K80M, 4710918137922
Overview
For the past two years, Nvidia has launched high-end supercomputing hardware
at the International Conference for High Performance Computing, Networking,
Storage and Analysis (sensibly abbreviated as SC). Today, at SC14, Nvidia is
following that trend, except this time its bringing a new dual-GPU product to
the table. The new K80 is based on a revised version of the GK110 chip, dubbed
GK210, with 13 of Keplers 15 SMXs enabled and a 300W TDP. This may allow the
card to hit higher Boost frequencies than the desktop-oriented Titan Z,
whichstruggled to deliver equivalent scalingto two Titan Blacks
in an SLI configuration.
Meet GK210
Nvidias GK210 is a fairly significant alteration to the GK110/GK110B. While
the maximum number of stream processors remains the same, at 2880, the register
file size has doubled (512KB, up from 256KB) and the L1 cache is 128KB up from
64KB. Throughput and performance per clock both appear to be somewhat higher on
the new card, Tesla K80 offers roughly 2x the maximum single and
double-precision of Tesla K40 (8.74TFLOPs vs. 4.29TFLOPs and 2.91TFLOPs vs.
1.43TFLOPS) despite having fewer cores per chip (2496 vs. 2880 on a full Tesla
K40).
What does all this mean? Massive scientific computing horsepower more than
any other company has ever brought to market. The 24GB of RAM on the card is
divided between both GPUs, but there should be a difference between how this
memory pool is tapped for scientific workloads vs. gaming
The entire point of having a hardware accelerator in a supercomputing
environment is that you can keep data stored locally rather than waiting on the
system to deliver new information over the achingly slow PCIe 3.0 bus. Nvidias
NVLink is expected to address part of this problem when it eventually
arrivesin concert with IBM, but until that solution is ready,
the company is stuck with the relatively pokey bandwidth and latency of PCI
Express 3.0. That means you can only solve problems as complex as you can hold
in local memory
When you build a dual-GPU system, the data in RAM is always duplicated. In
graphics, a dual-GPU card with 2x4GB of RAM isnt the same as a card with 8GB of
RAM because all graphics data is copied across both frame buffers to ensure that
the game can be rendered smoothly. Thats not automatically the case in
scientific computing, where the two GPUs could be assigned two completely
different tasks or theoretically given two halves of the same problem (provided,
obviously, that the workload can be split in that fashion)
The big unknown is whether or not the GPU can run at its boost clock of
875MHz for sustained periods of time, as opposed to its base frequency of
562MHz. While even this frequency gives it a 50% advantage in raw throughput
over a single K40, a boost frequency of 875MHz would make it literally twice as
powerful as Nvidias previous top-end supercomputing solution. This is the first
card with a full dynamic GPU boost solution in the HPC space, so how that will
impact performance over time remains to be seen
Don’t expect consumer options
The big difference between this card and the previous workstation or
scientific computing behemoths that Nvidia has launched is that we dont expect
to see a 28nm variant of this chip taking over the high end of the market.
Kepler may have addressed Nvidias highest-end consumer needs quite well for
several years, but the twin GK210 chips that K80 offers would be beaten by a
brace of GTX 980s likely at much lower prices. Maxwell has taken over
the high-end consumer market, and its not going to give that space
back
For now, the Tesla family will remain Kepler-only. As of this writing, Nvidia
hasnt specified when it plans to roll out midrange consumer Maxwell-based
solutions, or whether it will wait for 20nm to deploy those cards. Doing so
could make good financial sense it wouldnt be the first time that a company has
debuted a midrange part on a new process to iron out the manufacturing issues
before transitioning high-end products at a somewhat later date
This new card should be a monster in its intended
environment. AMDs efforts in the HPC and scientific computing space
remain fairly minimal; the company has done a limited amount of proof-of-concept
work with APUs and other HPC projects but hasnt put a sustained push behind
scientific computing.Intel has its own Many Integrated Core
chip, of course, but Knights Landing wont ship until the second half of
next year. For now, that puts Nvidia firmly in the drivers seat.