Dedicated AI architectures are a complex mix of many different, yet equally important parts working in tandem. Imagine a Formula 1 race car. You can have the most powerful engine on the market, but if you’re racing on tires built for a Prius, that engine won’t save you from having to stop at the pit for maintenance after each lap.
Optimizing these systems requires careful planning and deep technical expertise, which not all of us have. DDN’s AI data intelligence platform is dedicated to those of us who are humbly non-technical and appreciate the simplified version of things from time to time.
In today’s blog, we’ll explore why an unnecessary number of companies are losing millions of dollars on their GPU investments due to inefficient GPU usage.
The Big Three
While a whole lot more goes into building a high-performance AI system, if our engineers had to pick the three most important components, they would be GPUs, networking, and the data platform.
The value proposition of the GPU is straightforward: they process the data being passed through an AI model and they do it fast; the race car engine analogy is fitting here.
As for networking, high-speed, low-latency networks ensure that the data we want the GPUs to process arrives to those GPUs without a hitch; networking is like the performance tires on our racecar.
So, if fast GPUs and fast networking enable fast data processing, why do we care about where that data is stored? Shouldn’t we just optimize for cost efficiency and get more data platforms to train larger models?
In AI systems, the data platform isn’t just the fuel tank that holds our precious data. It’s responsible for deciding how data travels to the GPUs, in addition to storing it. So, when it comes to the data platform, it’s less about capacity and more about the efficiency of the path that we create for data to travel to the GPUs.
Your Engine is Stalling
The most popular data platform architectures are NAS and NFS, which most providers use, including most of our competitors in the AI data platform space. Both originated and gained popularity around the 1990s and haven’t changed much since.
Simplifying things, both NAS and NFS deliver data to GPUs sequentially, or in a straight line. This means that, for the GPU to process data, each piece of data must be delivered one after the other at the speed of the networking.
Because GPUs are so fast, this often means that there is a waiting period between when the GPU processes a piece of data, and when the NAS or NFS platform delivers the next piece. In other words, our race car’s engine is stalling.
So how do we fix this? By delivering more than one piece of data at the same time to eliminate the waiting period. The kind of architecture that works like this is called a Parallel File System, or PFS, which has been perfected by DDN over the last two decades.
By delivering data to the GPUs in parallel, rather than sequentially, they always have more data to process and won’t waste time sitting idle.
How does this relate to our race car analogy? DDN’s Parallel File System is like rigging your engine with a state-of-the-art fuel injector, in addition to the standard fuel tank. You can house large amounts of training data, and that data is distributed to your GPUs in perfect amounts to keep them running smoothly, and at their highest performance potential!
The Numbers
At DDN, we’ve helped a significant number of customers fix their AI data platform bottlenecks. Here’s a few statistics we’re proud of:
- DDN customers see up to a 99% reduction in processing latency when transitioning from traditional NAS and NFS platforms to DDN’s unique parallel file system
- DDN data intelligence platform delivers up to 700% performance gains for AI & Machine Learning workloads
- DDN technology accelerates over 60% of the global top 100 supercomputers
- 11,000+ customers benefiting from DDN technology that powers over 500,000 GPUs worldwide
Make Your GPUs Happy
Nobody likes waste. Whether that’s wasted time, effort, money, or in this case, all three. Be kind to your GPUs, your engineers, data scientists, executives and stakeholders; don’t cut corners when you’re planning out your investments for the AI data platform. Choose DDN.
