![tesla p100 fp64 tesla p100 fp64](https://smgmedia.blob.core.windows.net/images/114178/1024/tesla-model-s-hatchback-e29f369f2d04.jpg)
Board Environment and Reliability Specifications. Straight Extender LIST OF TABLES Table 1. CPU 8-Pin Power Connector with Optional I/O Bracket. Board Dimensions with Optional I/O Bracket. Airflow Directions with Optional I/O Bracket. 9 Support Information Certificates and Agencies Certifications Agencies Languages GPU Accelerator PB _v01 iiiĤ LIST OF FIGURES Figure 1. The enthusiasts websites are not a reliable source to be trusted.1 TESLA P100 PCIe GPU ACCELERATOR PB _v01 October 2016 Product BriefĢ DOCUMENT CHANGE HISTORY PB _v01 Version Date Authors Description of Change 01 OctoGG, SM Initial Release GPU Accelerator PB _v01 iiģ TABLE OF CONTENTS Overview. Now on to looking for any Nvidia V100 whitepapers for some figures on that GPU micro-arch, as I’ll only trust what I can find from Nvidia’s whitepapers first and if I can not get a more complete pitcure there I’ll search the academic research paper sources(Peer Reviewed academic Journals and such). So what I’m getting at is I do not trust your figures or Amazom’s figures and I’ll trust the Nvidia whitepaper’s figures for the boost clock figures on P100, and hopefully there will be some other Nvidia whitepapers that have any base clock figures for P100. I’m no longer trusting any enthusiasts websites or any enthusiasts website’s GPU databases because there is information missing and when articles are published on enthusiasts websites there is no direct refrences to where the reporters got their figures(FP/other) in the first place(Anazon’s figures have less weight than Nvidia’s whitepaper figures). And if I can not get it there I’ll look for other sorces for base and boost clock figures but not the figures from any enthusiasts websites. So from now on I’ll have to look for any other Nvidia whitepapers that list Base and Boost clock figures on the Base P100 die design, or V100 Base die design, to try and build up a refrence of as near to accurate figures with Nvidia whitepaper results as refrences.
#Tesla p100 fp64 64 Bit#
So that 1/2 rate is true, but using different FP numbers.Īnd the figures from TechPowerUp’s article are just estimates in that article and even TechPowerUp’s GPU database is not listing GP100’s 16 bit HP FP or 64 bit DP FP numbers, but lists the SP FP(10,329 GFLOPS) number only and maybe that’s a base clock number. 21.2 TFLOPS of half-precision (FP16) performance”
![tesla p100 fp64 tesla p100 fp64](https://cdn.videocardz.com/1/2016/04/NVIDIA_Tesla_P100_GPU_topangleleft4-768x603.jpg)
10.6 TFLOPS of single precision (FP32) performance
![tesla p100 fp64 tesla p100 fp64](https://www.prowesscomputing.com/layout/products/900-2H400-0010-000/10588-0046.png)
5.3 TFLOPS of double precision floating point (FP64) performance Your figures: “The largest Tesla P100 (with NVLink) does 9.5 TFLOPs of FP32 and 4.75 TFLOPs” is that based on the base clock? And if so the Nvidia white paper figures for GP100(Boost Clock) are: It would be less confusing if the reporters would state base or boost clock figures when they report on any GPUs FP/Flops numbers so readers could at least know that often ommitted fact when a GPUs FP metrics where touted. And even there the base clock FP numbers are not listed but at least there is the Boost clock numbers in that table and a lot of other nice info that really should be included somewhere online in an easily searchable form. So yes that 1/2 DP FP rate of yours is correct is what I’m saying it’s correct but your numbers are different than Nvidia’s boost numbers in the white paper.īut look at all of the other figures that do not match what Nvidia states in their white paper on GP100. So the Nvidia whitepaper is more of a definitive source because I no longer trust any web based sources that are not directly from the GPUs maker(published Whitepapers/Professional Journals).
![tesla p100 fp64 tesla p100 fp64](https://cdn.wccftech.com/wp-content/uploads/2016/04/Nvidia-GP100-powered-P100.jpg)
I’m saying is there appears to be different numbers all across the web for GPU FP capability and not one definitive source. This keeps money rolling in that will fund new chip designs for all the other segments. While Google has again been quickly leapfrogged by Amazon, it’s good to see NVIDIA getting wins in multiple cloud providers. It also has access to NVIDIA’s tensor cores, which are specialized for 16-bit, 4×4 multiply-add matrix operations that are apparently common in neural networks, both training and inferencing.Īmazon allows up to eight of them at once (with their P3.16xlarge instances).
#Tesla p100 fp64 full#
Same as Pascal, they also support full 1:2:4 FP64:FP32:FP16 performance scaling. The Tesla V100, with its ridiculous die size, pushes that up over 14 TFLOPs. To compare the two parts, the Tesla P1 CUDA cores, yielding just under 10 TFLOPs of single-precision performance. Remember last month? Remember when I said that Google’s introduction of Tesla P100s would be good leverage over Amazon, as the latter is still back in the Kepler days (because Maxwell was 32-bit focused)?Īmazon has leapfrogged them by introducing Volta-based V100 GPUs.