The DDN team explores the steps to achieving a data-enabled data center in todays big-data driven markets.
An AI-enabled data center must be able to concurrently and efficiently service the entire spectrum of activities involved in the AI and DL process. (Photo: Shutterstock/Gorodenkoff)
Despite their many promising benefits, advancements in artificial intelligence (AI) and deep learning (DL) are creating some of the most challenging workloads in modern computing history and put significant strain on the underlying I/O, storage, compute and network. Current enterprise and research data center IT infrastructures can’t keep up with these demanding needs of AI and DL. Designed to handle modest workloads, minimal scalability, limited performance needs, and small data volumes, these platforms are highly bottlenecked and lack the fundamental capabilities needed for AI-enabled deployments.
An AI-enabled data center must be able to concurrently and efficiently service the entire spectrum of activities involved in the AI and DL process, including data ingest, training, and inference. The IT infrastructure supporting an AI-enabled datacenter must adapt and scale rapidly, efficiently, and reliably, as data volumes grow and application workloads become more intense, complex, and diverse. It must seamlessly and continuously handle transitions between different phases of experimental training and production inference in order to provide more accurate answers, faster. In short, the IT infrastructure is key to realizing the full potential of AI, and DL in business and research.
Without the right data storage platform, a GPU-based computing platform is just as bottlenecked and ineffective as an antiquated non-AI-enabled data center.
Fortunately, breakthrough technologies in processors and storage are acting as catalysts of effective AI data center enablement – graphical processing units (GPUs) deliver acceleration from slower CPUs, while flash enabled parallel I/O storage provides a significant performance boost to traditional hard disk-based storage.
GPUs are significantly more scalable and faster than CPUs. Their large number of cores permits massively parallel execution of concurrent threads, which results in faster AI training, and quicker inference capabilities. GPUs enable DL applications to deliver better and more accurate answers, significantly faster.
However, in order for GPUs to fulfill their promise of acceleration, data must be processed and delivered to the underlying AI applications with great speed, scalability and consistently low latencies. This requires a parallel I/O storage platform for performance scalability and real time data delivery and flash media for speed.
Without the right data storage platform, a GPU-based computing platform is just as bottlenecked and ineffective as an antiquated non-AI-enabled data center. The proper selection of the data storage platform and its efficient integration in the data center infrastructure are key to eliminating AI bottlenecks and truly accelerating time to insight.
The right data storage system must deliver high throughput, high IOPS and high concurrency in order to prevent idling of precious GPU cycles. It must also be flexible and scalable in implementation and enable efficient handling of a wide breadth of data sizes and types, including highly concurrent random streaming, a typical DL dataset attribute.
If properly selected and implemented, such a data storage system will deliver the full potential of GPU computing platforms, accelerate time to insight at any scale, effortlessly handle every stage of the AI and DL process, and do so reliably, efficiently and cost effectively.
At DDN, our A³I solutions are fully-optimized to accelerate AI applications and streamline DL workflows for greatest productivity. Learn more about these solutions here.
This article originally ran on the DDN Blog.
