Benchmarking AI Workloads on Intel Xeon with AMX: Best Practices and Performance Insights

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Introduction Artificial Intelligence (AI) workloads increasingly depend on robust computational resources, and Intel Xeon processors present an attractive solution for both training and inference. The introduction of Advanced Matrix Extensions (AMX) in Intel Xeon has significantly enhanced AI acceleration, especially for deep learning, natural language processing, and high-performance computing applications. Accurate benchmarking of these workloads […]

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The Decline of TensorFlow and the Rise of PyTorch in AI Workloads: A Documented Shift

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TensorFlow, formerly the preeminent deep learning framework created by Google, has been eclipsed in recent years by PyTorch, developed by Facebook AI Research. Previously the unequivocal choice for researchers and corporations, TensorFlow has experienced a slow decrease as PyTorch has emerged as the favoured framework for AI workloads. This transition is seen not only in […]

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Using and Optimizing Containers for AI, ML, and DL Workloads in Virtualisation

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The combination of containers with AI, ML, and DL has been nothing short of revolutionary in the dynamic landscape of modern software development. These cutting-edge computational technologies promise more effective, versatile, and rapid outcomes when combined with the portability, isolation, and scalability provided by containers. However, there are unique difficulties associated with virtualising AI/ML/DL workloads. […]

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