Comparison of cryptocurrencies - Bitcoin Wiki


Aeon (AEON) is a private, secure, untraceable currency. You are your bank, you control your funds, and nobody can trace your transfers.

"How a bug in Visual Studio 2015 exposed my source code on GitHub and cost me $6,500 in a few hours" ~ Bots continuously scan github source code looking for exposed amazon access keys which they use to spawn large numbers of EC2 instances to mine on someone else's dime...

submitted by dalovindj to Bitcoin [link] [comments]

Anyone bullish on XLNX?

There's a pretty interesting debate in the AI space right now on whether FPGAs or ASICs are the way to go for hardware-accelerated AI in production. To summarize, it's more about how to operationalize AI - how to use already trained models with millions of parameters to get real-time predictions, like in video analysis or complex time series models based on deep neural networks. Training those AI models still seems to favor GPUs for now.
Google seem to be betting big on ASICs with their TPU. On the other hand, Microsoft and Amazon seem to favor FPGAs. In fact Microsoft have recently partnered with Xilinx to add FPGA co-processors on half of their servers (they were previously only using Intel's Altera).
The FPGA is the more flexible piece of hardware but it is less efficient than an ASIC, and have been notoriously hard to program against (though things are improving). There's also a nice article out there summarizing the classical FPGA conundrum: they're great for designing and prototyping but as soon as your architecture stabilizes and you're looking to ramp up production, taking the time to do an ASIC will more often be the better investment.
So the question (for me) is where AI inference will be in that regard. I'm sure Google's projects are large scale enough that an ASIC makes sense, but not everyone is Google. And there is so much research being done in the AI space right now and everyone's putting out so many promising new ideas that being more flexible might carry an advantage. Google have already put out three versions of their TPUs in the space of two years
Which brings me back to Xilinx. They have a promising platform for AI acceleration both in the datacenter and embedded devices which was launched two months ago. If it catches on it's gonna give them a nice boost for the next couple of years. If it doesn't, they still have traditional Industrial, Aerospace & Defense workloads to fall back on...
Another wrinkle is their SoCs are being used in crypto mining ASICs like Antminer, so you never know how that demand is gonna go. As the value of BTC continues to sink there is constant demand for more efficient mining hardware, and I do think cryptocurrencies are here to stay. While NVDA has fallen off a cliff recently due to excess GPU inventory, XLNX has kept steady.

XLNX TTM P/E is 28.98
Semiconductors - Programmable Logic industry's TTM P/E is 26.48

submitted by neaorin to StockMarket [link] [comments]

How do I attach and use an Elastic GPU to my Windows EC2 Instance? AWS re:Invent 2017 - Amazon EC2 Elastic GPUs Deploy Toshi Bitcoin Node with Docker on AWS in 30 minutes. Beginner Friendly! Overview of Amazon EC2 G4 Instances Dogecoin mining using AWS EC2 GPU instances g2.2xlarge

P2 instances are equipped with NVIDIA K80 GPUs (2,496 CUDA cores, 12GiB of GPU memory), while P3 instances feature NVIDIA Tesla V100 GPUs (5,120 CUDA Cores, 640 Tensor Cores, 16GiB of GPU memory). Note that some types might not be available in some regions. EC2 imposes limits on how many instances of every type you are allowed to launch. Limits Elastic GPUs allow you to easily attach low-cost graphics acceleration to a wide range of EC2 instances over the network. Simply choose an instance with the right amount of compute, memory, and storage for your application, and then use Elastic GPUs to add the GPU […] However, due to enormous bitcoin mining amazon aws demand, EC2 spot posting ads work from home pricing is nowhere near what I paid in early 2017. Coinbase AWS c5.4xlarge (using 15 vCPU’s of 16):For example there are a lot of sites that compare mining contracts for Bitcoin, Litecoin and Ethereum - but they are rewarded by the scam artists for But that’s where AWS and EC2 comes into the picture. Because they take care of the virtualizing for you. So, we need to pinpoint a couple of strong GPU’s to use for our operations. A nifty thing AWS allows is the bidding on various GPU instances. On the left side of the screen, you will see, “Spot Request”. Click on this. Bitcoin can only be used as a payment after a credit card has been added to the users account Bitcoin can only be used once the user has added their credit card info into their Luna node account. This pretty much negates the point of using bitcoin, if you need to hand over your personal information anyways.

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How do I attach and use an Elastic GPU to my Windows EC2 Instance?

Deploy Toshi Bitcoin Node with Docker on AWS in 30 minutes. ... Deep Dive on Amazon EC2 Elastic ... Pyrit on amazon EC2 dual Tesla GPU instance - Duration: 6:14. RockTouching 12,387 views. 6:14. ... Find more details in the AWS Knowledge Center: Alessandro, an AWS Cloud Suppo... Amazon EC2 G4 instances deliver the industry’s most cost-effective and versatile GPU instance for deploying machine learning models in production and graphics-intensive applications. Amazon EC2 Elastic GPUs allow you to easily attach low-cost graphics acceleration to a wide range of EC2 instances over the network. ... Starting up AWS ec2 gpu instance for first time - Duration ... Elastic GPUs support OpenGL 3.3 and offer up to 8GB of GPU memory, making them ideally suited for any workload that needs a small amount of additional GPU such as virtual desktops, gaming ...

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