/i/2001041343.jpeg "tesla p100 fp64")
Are they just going for no-expense-spared bragging rights against the Xeon Phi for die size and performance? Although it must be noted Intel are being very secretive around Phi's die size, which some have estimated to be in the area of 700mm2 IIRC. And for the sort of application this seems to be targeted at, even Nvidia are showing it must scale well across nodes given they're sticking eight of these in a box. I wonder what the real motivation is behind something like this? It seems like it's more marketing and halo effect than anything - they could have gone marginally smaller with a relatively insubstantial difference in performance but massive improvements to costs and yield. IIRC they can get around that limit for things like interposers by using multiple exposures but that wouldn't work for a logic die (I could imagine a die somehow divided down the middle working, but then it would make no sense to leave it as a singe die vs MCM). I don't even recall Intel making something that large! I assume they're talking about the logic die rather than the interposer, but I thought TSMC's single-exposure reticle size was somewhere around the 600mm2 mark, hence both AMD and Nvidia hitting that wall on 28nm. (* Peak TFLOP/s rates are based on GPU Boost clock.) Tesla V100 Compared to Prior Generation Tesla Accelerators. A DGX-1 system powered by the new GPUs will set you back $149,000 for example. Those interested in deploying Volta based solutions should know that the first server and deep learning products based upon Tesla V100 will become available starting from Q3 2017. You can head on over there to read more about the technicalities and architectural nuances of the new architecture: Inside Volta. Nvidia has published quite a lengthy blog post about the arrival of Volta with the Tesla V100 accelerator.
#Tesla p100 fp64 software
Supporting the Volta chip in the Tesla V100, Nvidia has architected an SMX card with second gen NV-Link high speed interconnect technology for up to 300GB/s links, 16GB of HBM2 memory from Samsung providing 1.5x delivered memory bandwidth versus Pascal GP100, Maximum Performance and Maximum Efficiency Modes are present, and that all-important optimised software support with GPU accelerated libraries is available. In the Tesla V100 80 SMs are enabled, so there's 5120 CUDA cores at work, probably reduced from the maximum possible for better yields and to provide room for next generation Titan headlines.
#Tesla p100 fp64 full
Among the computing components inside a full GV100 GPU are 84 SMs, a total of 5376 FP32 cores, 5376 INT32 cores, 2688 FP64 cores, 672 Tensor Cores, and 336 texture units. Chip size is considerably higher than the last gen, with the GPU measuring 815mm 2 compared to the P100's 610 mm². First of all, from the article subheading, you will already be aware that this GPU packs in 21.1 billion transistors and is fabricated using TSMCs 12nm FFN process. The Volta GV100 GPU powering Nvidia's latest accelerator product has some mighty specs. I think that means the GV100 leapfrogs Google's TPU ASIC which is capable of 90 TOPS. "Tesla V100’s Tensor Cores deliver up to 120 Tensor TFLOPS for training and inference applications," notes Nvidia. They provide a significant performance uplift in training neural networks. There are 8 Tensor Core per SM unit in the Volta GV100, that's 640 in total. It's interesting to see that Nvidia's Volta GV100 architecture offers dedicated Tensor Cores to compete with accelerators from the likes of Google.
Furthermore, it hasn't been afraid to invest, spending $3 billion in R&D in developing Volta. The Nvidia Tesla V100 is referred to in headline terms as an "AI computing and HPC powerhouse".įollowing hot on the heels of its Q1 financials, showing stellar performance in its datacentre business, Nvidia looks to be keeping the pressure up on this sector. As those familiar with Nvidia's nomenclature will be aware, the first product based upon Volta is an accelerator targeting complex problem solving. This launch marks several milestones for Nvidia, not least the introduction of its first Volta architecture GPU based product. A few hours ago at the GTC 2017 Nvidia CEO Jensen Huang took the wraps off the Tesla V100 accelerator.
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