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SD4Health Resources and Flavours Definitions: Difference between revisions
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SD4Health Resources and Flavours Definitions (view source)
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This flavour is designed for GPU-intensive workloads, such as machine learning, deep learning, and scientific simulations. It provides a high-performance computing environment with dedicated GPU resources, high memory, and fast storage. The virtual machines in this tier are optimized for GPU-intensive workloads and can handle large-scale parallel processing. | This flavour is designed for GPU-intensive workloads, such as machine learning, deep learning, and scientific simulations. It provides a high-performance computing environment with dedicated GPU resources, high memory, and fast storage. The virtual machines in this tier are optimized for GPU-intensive workloads and can handle large-scale parallel processing. | ||
==Definitions<ref>[https://docs.alliancecan.ca/wiki/Technical_glossary_for_the_resource_allocation_competitions#Computational_resources]DRAC</ref>== | =='''Definitions'''<ref>[https://docs.alliancecan.ca/wiki/Technical_glossary_for_the_resource_allocation_competitions#Computational_resources]DRAC</ref>== | ||
==== | ====GPU==== | ||
GPU computing is the use of a graphics processing unit (GPU) to accelerate deep learning, analytics, and engineering applications, for example. GPU accelerators now power energy-efficient data centres in government labs, universities, enterprises, and small-and-medium businesses around the world. | GPU computing is the use of a graphics processing unit (GPU) to accelerate deep learning, analytics, and engineering applications, for example. GPU accelerators now power energy-efficient data centres in government labs, universities, enterprises, and small-and-medium businesses around the world. | ||