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Deep learning (DL) techniques have enabled great successes in many fields, such as computer vision, natural language processing (NLP), gaming, and autonomous driving. These techniques enable a model to learn from existing data and then to make corresponding predictions. The success is due to a combination of improved algorithms, access to larger datasets, and increased computational power. To be effective at enterprise scale, the computational intensity of DL requires highly efficient parallel architectures. The choice and design of the system components, carefully selected and tuned for DL use-cases, can have a big impact on the speed, accuracy, and business value of implementing artificial intelligence (AI) techniques.
In such a demanding environment, it is critical that organizations be able to rely on vendors that they trust. Over the last few years, Dell Technologies and AMD have established a strong partnership to help organizations fast-track their AI initiatives. Our partnership is built on the philosophy of offering flexibility and informed choice across a broad portfolio that combines outstanding GPU accelerated compute, scale-out storage, and networking.
This paper focuses on how the Dell PowerScale F900 scale-out NAS platform accelerates AI innovation by delivering the performance, scalability, and I/O concurrency for high-performance AI workloads, using Dell PowerEdge servers and AMD Instinct™ MI100 GPUs.