![]() P100 has 2x performance to AMD FirePro S9170 at FP64 (this is Hawaii and will improve on the 14nm but worth noting the 290x was 1.4 tflops to the S9170 2.6 tflops so there will still be a restricting ratio relationship between built for consumer and the pro version, the earlier S9100 was I think 2.1 fltops) P100 as mentioned has over 5x the FP64 performance of AMD Duo Pro (however similar situation to NVIDIA they sell their older architecture for FP64, the now superseded Tesla Kepler against S9100 range Hawaii) Using a mix of both puts 32/16 the P100 in a very strong position, and separately shows how much FP64 is on this die. P100 has fp16 that is 2x its performance of its P100 fp32 - currently I think the GCN architecture is limited in FP16 implementation (for AMD fingers crossed their 14nm pro cards has better support although still needs to be shown by NVIDIA-real world applications how much this can help with Deep Learning) P100 is roughly 30% slower to AMD Duo Pro at FP32 but has over 5x more FP64 performance. ![]() Usually the AMD figure is a theoretical peak tflops, as I mentioned before NVIDIAs are not perfect but probably get the edge with real-world scientific applications due to their software experience-CUDA-relationship with said software companies. That is where the power is with this card IMO, and AMD will definitely need to get FP16 working well as this could be a big part in the future of deep learning. This is a mixed precision GPU and I get the feeling NVIDIA with their software expertise will be pushing for clients to mix the use of FP64 and FP32, and mix the use of the FP32 and FP16. I really feel many are missing what has been achieved with this chip. I have been reading recently several additional forum sites beyond those I am a member and responses to the Tesla P100 and where people are being critical in it only has so much double precision only bit better than older cards/it only has so much single precision worst than AMD Duo Pro.
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