Enmotus Blog

A.I. For Storage

Posted by Jim O'Reilly on Dec 18, 2017 2:12:46 PM

As we saw in the previous part of this two-part series, “Storage for A.I.”, the performance demands of A.I. will combine with technical advances in non-volatile memory to dramatically increase performance and scale within the storage pool and also move addressing of data to a much finer granularity, the byte level rather than 4KB block. This all creates a manageability challenge that must be resolved if we are to attain the potential of A.I. systems (and next-gen computing in general).

Simply put, storage is getting complex and will become ever more so as we expand the size and use of Big Data. Rapid and agile monetization of data will be the mantra of the next decade. Consequentially, the IT industry is starting to look for ways to migrate from today’s essentially manual storage management paradigms to emulate and exceed the automation of control demonstrated in 

public clouds.

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Topics: NVMe, Data Center, NVMe over Fibre, enmotus, data analytics, NVDIMM, artificial intelligence

Storage for Artificial Intelligence

Posted by Jim O'Reilly on Dec 4, 2017 1:17:42 PM

It’s not often I can write about two dissimilar views of the same technology, but recent moves in the industry on the A.I. front mean that not only does storage need to better align with A.I. needs than any traditional storage approach, but the rise of software-defined storage concepts makes A.I. an inevitable choice for solving advanced problems. The result, this article on “Storage for A.I.” and the second part of the story on “A.I for Storage”.

The issue is delivery. A.I. is very data hungry. The more data A.I. sees, the better its results. Traditional storage, the world of RAID and SAN, iSCSI and arrays of drives, is a world of bottlenecks, queues and latencies. There’s the much-layered file stack in the requesting server, protocol latency, and then the ultimate choke point, the array controller.

That controller can talk to 64 drives or more, via SATA or SAS, but typically only has output equivalent to maybe 8 SATA ports. This didn’t matter much with HDDs, but SSDs can deliver data much faster than spinning rust and so we have a massive choke point just in reducing streams to the array output ports’ capability.

There’s more! That controller is in the data path and data is queued up in its memory, adding latency. Then we need to look at the file stack latency. That stack is a much-patched solution with layer upon layer of added functionality and virtualization. In fact, the “address” of a block of data is transformed no less than 7 times before it reaches the actual bits and bytes on the drive. This was very necessary for the array world, but solid state drives are fundamentally different and simplicity is a possibility.

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Topics: NVMe, SSD, NVDIMM, artificial intelligence, machine learning

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