Before we would be beginning, let’s gain the pedantic: at this point in time, artificial intelligence is considered to be a purely theoretical concept. True AI, a sentient computer capable of the initiative as well as human interaction, remains within the realm of science fiction. The AI research field is considered to be full of conflicting ideas, and it isn’t clear whether we could actually build a machine that could be replicating the inner workings of the human brain. For getting details regarding the AI, you should gain the study dumps which are being offered at the SPOTO Club, to acquire success.
In the data center
The impact of AI on data centers could be divided into two broad categories – the impact on hardware as well as architectures, as the users would be beginning adopting AI-inspired technologies, and the impact on the management as well as operation of the facilities themselves.
We would be beginning with the first category: turns out that machine learning as well as services such as speech and image recognition which would be requiring a new breed of servers, equipped with novel components like the GPUs (Graphics Processing Units), FPGAs (Field-Programmable Gate Arrays) and ASICs (Application-Specific Integrated Circuits). All of these would be requiring massive amounts of power, as well as producing massive amounts of heat.
Nvidia, the world’s largest supplier of graphics chips, would have just announced DGX-2, a 10U box for algorithm training that would be including 16 Volta V100 GPUs along with two Intel Xeon Platinum CPUs as well as 30TB of flash storage. DGX-2 delivers up to two Petaflops of compute, as well as consuming a whopping 10kW of power – more than an entire 42U rack of traditional servers.
And Nvidia isn’t considered to be alone in pushing the envelope on power density: DGX-2 would be actually a reference design, as well as server vendors have been given permission for iterating and creating their own variants, some of which might be even more power-hungry. Meanwhile, Intel would be just confirming the rumors that it’s working on its own data center GPUs which would be expected to hit the market in 2020.
As power densities go up, so does the amount of heat that would be required to be removed from the servers, and this would inevitably result in the growing adoption of liquid cooling.
For the data center
But machine learning is considered to be also useful in the management of the data center, where it could help you to optimize energy consumption as well as server use. For example, an algorithm could spot under-utilized servers, automatically moving the workloads as well as either switch off idle machines for conserving energy or rent them out as part of a cloud service, which would be creating an additional revenue stream.
American software vendor Nlyte would have just partnered with IBM for integrating Watson perhaps the most famous ‘cognitive computing’ product to date into its DCIM (Data Centre Infrastructure Management) products.
Beyond management, AI could be improving physical security by tracking individuals throughout the data center utilizing CCTV, as well as alerting its masters when something would be looking out of order.
I think it would be a safe bet for saying that every DCIM vendor would be eventually offering some kind of AI functionality. Or at least something they would be calling AI functionality.
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