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Nvidia Announces PCIe Tesla V100
- polskibus 9y agoI wonder when will such briefs include information about cryptocurrency mining performance on a regular basis.
- fleetwood 9y agoProbably quite a while, as NVIDIA currently doesn't mention cryptocurrency mining publicly to keep investors from worrying about the sustainability of their recent revenue growth.
- polskibus 9y agoI wonder whether cryptomining sells more gpus than deep learning, outside public cloud providers.
- fleetwood 9y agoHard to say... most recent quarter had datacenter at $409mm in revenue. Mining falls under gaming, which has grown ~$600mm since 2015 to >$1b per quarter today. Try to buy a gaming GPU today and you won't be able to -- they're all sold out because of cryptomining.
- icahnvalyou 9y agoOnly mid-tier, where they're most power efficient. High end cards are generally still available, since the efficiency curve drops off.
- sremani 9y agoNvidia 1050 ti is almost untouched for mining purposes and there is 10% price increase in 1060s and 1070s. 1080s & 1080 ti you can get for retail. The GPUs the are in insane demand are AMD RX 580/570/480.
- dantillberg 9y agoI don't know about the deep learning side of things, but on the mining side of things, over the past month, my calculations are that ~50,000 GPUs are added to mine the major GPU-minable coins every day -- from roughly one million GPUs on April 1 to about 3.5 million GPUs now. At an average of $250 revenue per card, that'd work out to ~$13M revenue per day in GPU sales for cryptomining, split between AMD cards and NVidia cards. This, however, is up about ten-fold from March, and in all likelihood will return to those levels as mining returns inevitably decline. Source of this data is my own calculations using historical data on mining difficulty for the various coins, plus benchmarks for typical/optimal-ish GPUs used to mine each coin.
- RussianCow 9y agoAre you assuming that all mining is happening with GPUs? Because a large portion of it now is being done with dedicated mining hardware.
- npongratz 9y agoGP states they are referring to "... the major GPU-minable coins ... ".
- RussianCow 9y agoI interpreted that as "possible to mine on GPUs", not "profitable to mine", but I guess I misunderstood that.
- dantillberg 9y agoYes, I am assuming that all mining is done with GPUs for ETH/ETC/ZEC/XMR and some others. I'm only including coins which are currently profitable to mine on GPUs, which I take as an indication that those algorithms have seen little/no efficient ASIC implementation yet. I'm thus not including bitcoin, since GPUs are little more than space heaters mining bitcoin; and I'm not including e.g. lbry and litecoin and some others because I don't know enough about the ASICification of those. It's possible, though, as you suggest, that there are some folks running ASICs or improved hashing algorithms at a small scale, small enough not to overwhelm profitability of GPU mining but large enough to muddy calculations which assume that all mining is being done on GPUs.
- wmf 9y agoIt is rumored that NVIDIA will announce mining-only cards within weeks: http://wccftech.com/nvidia-pascal-gpu-cryptocurrency-mining-price-specs-performance-detailed/ http://wccftech.com/nvidia-pascal-gpu-cryptocurrency-mining-...
- cookingrobot 9y agoWhy don't they run them themselves?
- wmf 9y agoYou can usually sell mining equipment for more than the net present value of the coins it will mine.
- colordrops 9y agoI was under the impression that ASICs were far better for mining. Have GPUs caught up again?
- deleted 9y ago[deleted]
- dantillberg 9y agoGPUs are definitely dead for bitcoin. You'll get something like 10^9th more efficient SHA256 hashing from an ASIC than a GPU. But for many other coins, there are two big factors bringing GPUs back into the game for mining: - Lots of alt-coins competing for popularity; many might have ASIC mining implementations in the future, but there either hasn't been enough time or enough money to be made by doing it yet. - There's been various research done to reduce the potential gain in efficiency from an ASIC implementation over using commodity hardware. This was done in response to the concentrating effect of ASIC mining operations, which put a greater percentage of global hashpower in a smaller set of hands. The main way ethereum implements this "ASIC resistance" is by exercising memory bandwidth -- an area where GPUs are already quite optimized -- in the hashing algorithm. https://github.com/ethereum/wiki/wiki/Ethash https://github.com/ethereum/wiki/wiki/Ethash goes into detail.
- deleted 9y ago[deleted]
- petra 9y agoWhat else besides deep learning can the tensor core do well ?
- krona 9y agomultiplying and adding matrices are fairly fundamental operations. Here is a starting point: https://en.wikipedia.org/wiki/Multiply%E2%80%93accumulate_operation https://en.wikipedia.org/wiki/Multiply%E2%80%93accumulate_op...
- science404 9y agoYes but they are limited in size and in precision to FP16, which is not sufficient for most applications, especially scientific computing... What this person is asking I think is: Can you actually think of another concrete application?
- thearn4 9y agoCFD and other high-fidelity multiphysics simulations can be efficient when GPU-bound. It all comes down to being able to write a matrix and stream a bunch of vectors to multiply it by. When considering Krylov subspace methods, this extends to covering a large part of the problem space of linear algebra. I think the Tesla cards in particular are double precision, which is not necessarily the best for deep learning applications.
- tanderson92 9y agoHigh-fidelity multiphysics seems incompatible with FP16, unless you're talking some kind of mixed precision method.
- lvspiff 9y agoI remember a time back before the turn of the century me and my friends joking how cool it would be to run doom on a CRAY - who knew that less than 20 years later we could be...
- astrodust 9y agoDOOM wouldn't run very well on a CRAY machine for multiple reasons, but one of which is it doesn't have the right video hardware. A Raspberry Pi has way more compute power than the first generation Cray machine and a 386-vintage machine has better single-core, non-vectorized performance.
- modeless 9y agoDon't get too excited about V100. P100 was announced over 1 year ago and is still today not available from AWS, GCE, or Azure (despite various announcements). Although you can now, finally, order one at retail for the low, low price of $12k+ [1] [2]. The upcoming Volta-based consumer GPUs are going to be your best value for machine learning, not V100. [1] https://www.amazon.com/NVIDIA-Tesla-P100-computing-processor/dp/B06WV7HFWV/ https://www.amazon.com/NVIDIA-Tesla-P100-computing-processor... [2] http://accessories.us.dell.com/sna/productdetail.aspx?c=us&l=en&s=bsd&cs=04&sku=489-BBCF&cid=298721&st=&VEN1=saalXTMnP%2C101952148149%2C901q5c14135%2Cc%2C%2C489-BBCF&VEN2=%2C&lid=5704670&dgc=ST&DGSeg=SO&acd=12309152537501410&VEN3=105703622544116288 http://accessories.us.dell.com/sna/productdetail.aspx?c=us&l...
- jamesfmilne 9y agoYou can get the Quadro GP100, which is pretty much identical (I've got 2). http://images.nvidia.com/content/pdf/quadro/data-sheets/302049-NV-DS-Quadro-Pascal-GP100-US-NV-27Feb17-HR.pdf http://images.nvidia.com/content/pdf/quadro/data-sheets/3020... Agreed re consumer GPUs for machine learning though.
- modeless 9y agoInteresting, I hadn't heard of GP100, only released in February. Still, at $6000, as expensive as eight 1080 Tis.
- tanderson92 9y agoYeah but the double precision on the 1080 TIs is crippled, so the eight 1080 TIs would (in theory) be 50% as powerful in FP64 performance of a single Quadro GP100. There are good reasons to use the higher end chips, in some cases (maybe not for ML).
- endorphone 9y agoMany of the early P100s went to supercomputers. The V100 is interesting because they implemented "tensor cores", and claim that it will offer 12 times the deep learning performance of the already class leading P100. I'm not sure how consumer GPUs that don't exist can be better, though. That seems highly presumptive, especially given that most machine learning setups are heat, cooling and space limited.
- sgt101 9y agoTensorflow 1.2 has integrated new instructions from Intel, does anyone have any figures on 1.2 showing CPU performance vs GPU's?
- visionscaper 9y agoThe "NVIDIA Tesla Family Specification Comparison" table indicates 112 TFLOPS "Tensor Performance (Deep Learning)" for Tesla V100 (PCIe). Is that double precision? The Nvidia 1080 Ti has a double precision performance of 332 GFLOPS [1]. If the above number is for double precision computing, the Tesla V100 (PCIe) would be about 337 times as fast (!!) Does anyone have more insight into these numbers? [1] https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_processing_units https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_proces... UPDATE : I should have read the article more carefully, it seems to be a mix of FP16 (half precision) and FP32 (single precision). That would likely mean a factor ~10 in computation performance (specifically for deep learning)
- wmf 9y ago"...matrix operations with FP16 inputs and FP32 accumulation..." http://images.nvidia.com/content/volta-architecture/pdf/Volta-Architecture-Whitepaper-v1.0.pdf http://images.nvidia.com/content/volta-architecture/pdf/Volt...
- visionscaper 9y agoThanks, I also read the article better now. I updated my comment.
- davesque 9y agoOn this topic, hasn't there been some research that has shown that lesser precision values can be used in neural network algorithms without much loss in performance?
- visionscaper 9y agoThere had been a lot of research on this topic (just Google it), with good results.
- redtuesday 9y agoWith the incredible die size of Volta, and the cost associated with it, I wonder if the rumors are true that AMDs next gen GPU codenamed Navi is using the same strategy as their Zen CPUs (Ryzen, Threadripper, Epyc) - multiple smaller dies connected for better yields and lower cost. Would something like that be even possible with GPUs? I know the Exascale paper from AMD mentions GPU chiplets which sound like this.