By now everyone knows what bitcoin is.Almost as many people know you can mine bitcoin with your computer.Like any good mining rush, some early adopters made it big and everyone else has been chasing the ghosts of a fortune.If you could mine a crypto currency in the cloud for less than the cost of renting those servers, you could scale up nearly instantly and make a similarly sized pile of money.Using the cloud for crypto-currency mining has long been the dream of miners, but it never really works.Forget that, let’s throw caution to the wind and see for ourselves.A couple of factors led me to experiment if it would work again with the following considerations: Once the CUDA machine was up and running with CudaMiner it was time to get it benchmarked: Then it was time to let it go: It wasn’t always super reliable: When discussing the price of crypto-currency, we nearly always determine effectiveness against the current price for them or running price.However their fluctuation means that if you believed there would be a change in price in the future of these prices you might be willing to invest more mining them.

I believe that is the reason mining crypto currencies has continued to be popular.Looking at how slow these miners were though, you would have to expect orders of magnitude increases in prices for it to be worth it.If you’re so inclined, here’s a snap of the history that it took to get running: If we’re getting 260khash/s and AWS costs $.65 per hour for a GPU instance, plugging those numbers into current value calculator, we get a value of $.001 per hour.fundacion bitcoin chileI also tried using the free tier as I had originally thought this could be a good avenue.bitcoin vietnam lua daoIt was more than an order of magnitude slower, meaning that you would be pulling in less than $.0001 per hour (and there is no cloud space even close to that cheap).bitcoin asrock motherboard

If you look at a graph of the difficulty of litecoin: You’ll immediately notice insane growth in difficulty.Basically, starting in May, Litecoin has turned into the arms-race that directly mimics what happened with Bitcoin.TL;DR: It might have made sense in early spring this year to attempt to mine Litecoins with either a CPU or desktop GPU, this is no longer true.bitcoin anlageBarring huge decreases in difficulty and increases in prices, this is unlikely to reverse.ripple to bitcoin walletIt’s impossible to buy anything but the most entry level Radeon graphics cards right now.That’s expected at the high-end, as AMD’s enthusiast-focused Radeon RX Vega graphics cards won’t launch until the very end of July.tor bitcoin donationBut even “sweet spot” mainstream graphics cards like the superb Radeon RX 570 and RX 580 can’t be found right now, with all models either out of stock or selling for wildly inflated prices online.bitcoin bitfinex

You’ll find a couple of PowerColor RX 580s ostensibly selling at standard cost on Amazon, but if you look closely you’ll see the models are out of stock.Amazon’s selling first dibs on inevitable restocks.You probably shouldn’t expect to see the cards in your hands any time soon.So what’s going on?Computer Base asked hardware vendors about the shortage at Computex 2017 and the answer can be summed up in a single word: Miners.Cryptocurrency users can use graphics cards to “mine” new coins and generate a profit, and AMD’s graphics cards happen to be particularly well-suited for the task.bitcoin tax swedenThis isn’t new: Bitcoin and Litecoin miners gobbled up every Radeon graphics card they could get at the end of 2013, creating a global shortage and inflated pricing.vendere bitcoin italiaAs cryptocurrency matured, however, ASIC hardware dedicated specifically to mining surpassed the efficiency of consumer graphics cards, easing the pressure.Then came Ethereum, a cryptocurrency that can be mined like Bitcoin.

Brad Chacos/IDG Sapphire's Radeon RX 580 Pulse.The Ethereum network was built to be resistant to ASIC hardware, making mining Ether with graphics cards viable.Ethereum’s enjoying a Bitcoin-esque bubble of mammoth proportions right now, with the price of Ethereum skyrocketing from under $19 at the beginning of March to roughly $220 today.That’s the perfect recipe for making Radeon cards disappear.Budget graphics cards aren’t as good for mining, so the Radeon RX 550 ($80 on Amazon) and RX 560 ($110 on Amazon) are still available at standard prices.That modest hardware simply can’t deliver top-notch 1080p gaming experiences like the RX 570 and RX 580 can, however.While the RX 570 and RX 580 juuuust edge out Nvidia’s similarly priced offerings in PCWorld’s guide to the best graphics cards for PC gaming, they're damned close.The 3GB GeForce GTX 1060 ($190 on Amazon) and 6GB GTX 1060 ($240 on Amazon) match up very competitively with AMD’s offerings.Nvidia’s graphics cards aren’t selling for crazy sums, either—at least for now.If you’re in the market for a new $200-ish graphics card, the RX 570 and 580 still earn our top recommendation, on the off chance you can find one at an affordable cost.

But if you can’t, and you can’t wait for Radeon prices to plunge back to earth (because who knows when that will be?), Nvidia’s GeForce GTX 1060 cards won’t let you down.They’re damned fine hardware too.Fingers crossed these dark times end soon, though.To comment on this article and other PCWorld content, visit our Facebook page or our Twitter feed.Didn't receive the product, clueless where it could have gone to.I was badly needing it but it didn't come...For years I’ve been saying that, as more and more workloads migrate to the cloud, the mass concentration of similar workloads make hardware acceleration a requirement rather than an interesting option.When twenty servers are working on a given task, it makes absolutely no sense to do specialized hardware acceleration.When one thousand servers are working on the task, it certainly makes sense to do custom boards and/or Field Programmable Gate Arrays (FPGAs).But one thousand is actually a fairly small number in the cloud.When there might several hundred thousand servers all running the same workload, hardware specialization goes from an interesting idea to almost a responsibility.

Hardware acceleration can reduce latency by a factor of ten, decrease costs by a factor of ten and, it’s better for the environment, with power/performance improved by at least a factor of ten.In the 80s I argued that specialized hardware was crazy and the future of our industry was innovative software on low-cost, commodity, general-purpose processors.For many decades that certainly appeared to be true and I’ve managed to make a respectable living on that basic approach: improving performance, availability, and lowering costs by using large numbers of commodity processors.If I ever was correct on this point, it’s certainly not true any longer.We are entering the era of hardware acceleration.In actuality hardware acceleration has been around in large numbers for a considerable length of time.Commercial routers have massive Application Specific Integrated Circuits (ASIC) at the core.Many specialized network appliances have workload specialize hardware doing the heavy lifting.Most network Interface cards have ASICs at the core.

The most effective BitCoin mining engines are using hardware workload acceleration in custom ASICs.It really wasn’t that long ago that that X86 processors didn’t include floating point on die and it was either done in software on in a separate floating point co-processor.In the somewhat more distant past, I’ve worked on processors that even lacked a fixed point multiply instruction.In the early days of high-performance computing, matrix work was done in software.Cray moved it into hardware with the Cray 1 vector units and, as the cost of a transistor continues to plummet, even hardware vector units are now standard fare in a modern X86 processor.Hardware acceleration isn’t new, but the operations being accelerated are moving up from the incredibly primitive to replacing ever larger hot kernels of higher-level applications.In the early days, adding the MultiplyAdd (also called Fused-Multiply Add or fmadd) instruction to IBM POWER was a reasonably big deal in allowing the two operations to be done as a single instruction rather than as two.

Over time, more hardware accelerations like cryptography have been creeping into general purpose processors and I expect this process will not just continue but pick up pace.General purpose processors have the die real estate to spare and, even if the accelerators are used rarely, with 10x gains across many dimensions, hardware acceleration makes excellent economic sense.In fact, as Moore’s law slows, higher-level hardware acceleration will become one of the most important ways that the next processor generation shows material advantage over the previous.While general purpose processors will continue to get more higher–level accelerations, another trend emerging much more broadly over the last ten years is the offloading of important workloads from the general-purpose processor entirely.Where a workload has both high value and massive parallelism, it’s a candidate for migration off of general purpose processors and onto graphics processors.It turns out the massive parallelism required for advanced computer graphics also supports some non-graphical workloads incredibly well.

Some of the first examples I came across was the offloading of hot financial calculations and the acceleration of seismic studies used in oil exploration.These were important, but the golden workload that has absolutely exploded the general purpose graphics processor market is machine learning.Training neural networks is a highly parallel task that runs incredibly well on graphics processors.Five years ago there was lots of talk about this and some were doing it.Today, it’s hard to justify not running these workloads on General Purpose Graphics Processor Units (GPGPUs) once the workload is being run at scale.As an example, the Nvidia Tesla K80 GPGPU board has 2 sockets with 2496 CUDA cores each and the overall system is capable of 8.74 TFLOB.The individual cores aren’t that powerful nor amazingly fast but, if the workload is highly parallel, there are a lot of cores available to host it.This part is a bit power intensive at 300W TDP (thermal design power) but that’s not really a problem.

What’s both a massive problem and proof of how well this systems supports some workloads is the price.The retail price of the K80 when announced was $5,000.This board is just about as close as our industry gets to pure profit margin with a complete disregard to cost so large discounts are available.But, regardless of discount, this board will never be confused for a low-cost or commodity part.The good news is our industry is self-correcting and there are many new solutions under development.In fact, partly because the price of this part is so crazy high and partly because cloud volumes are now large enough to justify customer processors, we are going to see more and more workloads hosted on custom ASICs.15% of the 2016 ISCA papers were on machine learning hardware accelerators and Google, Microsoft, and Amazon all have digital design skills.Not much has been written about the semiconductor work done by the mega-providers but, as an example, at AWS we deploy many hundreds of thousands of custom ASICS each year.

I just read about another excellent example higher-level application acceleration.In fact this best example I’ve seen publicly disclosed so far.The paper “In-Datacenter Performance Analysis of a Tensor Processor Unit” will be presented at the upcoming 44th International Symposium on Computer Architecture (ISCA) to be held in Toronto Canada June 26, 2017.In my opinion, this is excellent work, a well-written paper, and a balanced analysis of what they produced and started to deploy back in 2015.It’s the normal practice in our industry to only show that which has already been replaced or is about to be replaced but that’s just the reality of commercial innovation and I do the same thing myself.What I found most striking is the elegant simplicity of what has been done.It wins over general purpose Intel processors and Nvidia GPGPUs of the same generation by the greater than 10x we would expect and yet, they have kept the part simple and shown good taste in what to include and what not to.

The paper uses power/performance as a proxy for the price/performance they know they should be using but since this is commercial innovation, pricing needs to remains confidential.Because the part and board have been in production since 2015, they will have likely done more than 10^6 volume by now.Since the volume is good by semiconductor standards and the part is not that complex, I would speculate that the part is less than $50 and the full PCIe board will cost under $100.For machine learning inference, this part is more than an order of magnitude faster than an Nvidia GPGPU while being more than an order of magnitude less expensive.This is the power of workload hardware specialization and we are going to see a lot more of this over the next decade.Some key speeds and feeds from the paper: Some interesting observations and lessons learned from the paper in bold with my short form notes in italics: This is really fine work by Norman Jouppi, Cliff Young, Nishant Patil, the always excellent David Patterson, and others.