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Home»Tech News»Storage the key bottleneck for AI processing
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Storage the key bottleneck for AI processing

September 4, 2022No Comments5 Mins Read
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Storage the key bottleneck for AI processing
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We discuss to Jeff Whitaker, vice-president for product advertising at scale-out NAS maker Panasas, about why storage is the important thing bottleneck in synthetic intelligence (AI) processing.

On this podcast, we take a look at the important thing necessities of AI processing and the way being attentive to the storage it requires can deliver advantages.

Whitaker talks about the necessity to get a lot of information into AI compute sources shortly and the way some are tempted to throw compute on the drawback. As an alternative, he argues that focus must be paid to storage that has the correct throughput and latency efficiency profiles to cope with a lot of small recordsdata, as present in AI

Antony Adshead: What are the challenges organisations face on the subject of storage for high-performance functions?

Jeff Whitaker: On the subject of high-performance functions…the appliance is attempting to get to outcomes quick. It’s attempting to get to a choice, attempting to get data again for the surroundings that’s utilizing the appliance.

There’s typically a heavy reliance on the compute aspect of this, and generally an over-reliance. A whole lot of occasions that may be discovered, that may be resolved by [asking], what [does] a typical software surroundings appear like? It’s compute, it’s community and it’s storage.

And I say storage third as a result of typically storage is the very last thing that’s considered on attempting to get efficiency out of an software surroundings.

One of many issues we like to take a look at is, on the subject of an software, what are the info wants? What sort of throughput is required, what sort of latencies are required, what’s it going to take for that software to run as effectively as potential?

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And infrequently, prospects and companions have checked out fixing the problem by throwing extra compute at making the functions sooner, however actually the bottleneck comes round storage.

It’s necessary for folks to know on the subject of their surroundings they need to take a look at the info wants earlier than they go and attempt to remedy the issue with simply compute.

So, it’s actually a matter of attempting to construct an environment friendly surroundings to get the outcomes they want. They want to take a look at what kind of a storage surroundings can remedy the challenges of their software.

 

Adshead: What are the important thing developments you might be seeing, significantly across the convergence of high-performance computing (HPC) with high-end enterprise storage, synthetic intelligence and machine studying?

Whitaker: HPC has historically been an software surroundings that wants a number of information. And a number of occasions, the storage surroundings must be one thing particular that may scale and tackle the throughput in order that the compute doesn’t simply sit there idle. It wants a number of information coming in there.

What we’ve began to see with the AI world and getting past simply the event and arising with concepts, they’re basically functions. An AI surroundings is attempting to course of a number of information and get to a outcome, particularly throughout the coaching course of there’s tonnes of knowledge being pumped into compute. So, on this case it’s typically GPUs [graphics processing units] which are used and people are costly and nobody needs to sit down there and have these idle.

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So, how briskly you may pump the info into an AI surroundings is important to how briskly the appliance can run or the AI coaching can run. For those who take a look at it, it’s nearly on a par with what an HPC surroundings usually appears to be like like the place you’re ingesting a tonne of knowledge attempting to get a outcome, so you really want to take a look at what these information wants are for that coaching course of or for the several types of HPC workloads and attempt to remedy the problem from there.

The one distinction that we see right here is usually in an HPC world, we see very giant recordsdata being pumped into the compute. Whereas within the AI aspect, we see tonnes of smaller recordsdata being pumped into the compute.

And actually the bottleneck turns into how briskly are you able to get that information into the compute so you may get to a outcome.

And actually going on the market and saying can a conventional enterprise storage surroundings remedy that want for you?

It’s latency, it’s throughput. Conventional environments have the flexibility to have small latency, however attempting to get very scalable throughput may be very difficult and that’s after we begin to take a look at totally different kind of structure like parallel options that may scale persistently, relying on how a lot efficiency you want, actually fixing that problem of ingesting tonnes of knowledge into these compute environments.

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