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The industry initiative is growing with increased use of the oneAPI programming model in Artificial Intelligence, Visual Computing, HPC and other segments. Learn more here.

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Fujitsu and RIKEN Optimized oneDNN for Improved Performance on ARM

Claiming the title of world’s fastest performance for the number of deep learning models trained per time unit for CosmoFlow,…

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TensorFlow and oneDNN in Partnership 

There has been rapid growth in AI and machine learning innovations over the years, and high-compute workloads necessitate constant developments…

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Standard Driven Heterogeneous Programming with oneAPI

Getting the maximum achievable performance out of today’s hardware requires developers to delicately balance the unlocking of their hardware's full…

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Accelerate PyTorch with IPEX and oneDNN

Enjoy Ease of Use and More Efficient Hardware Performance Figure 1. AVX512_BF16 instruction set introduced in 3rd gen Intel Xeon…

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Speed Training 8X Using PyTorch with oneCCL Backend

Train Complex Models in Hours, Not Days, with Distributed Training Fig.1 Software stacks for PyTorch DistributedDataParallel. CCL is one of…

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Developer Story: How We Ported oneDNN to Fugaku with Arm

Fujitsu optimized and ported the oneDNN DL process library software for the Armv8-A instruction set so that it can be…

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Huawei Extends DPC++ with Support for its Ascend AI Chipset

Applying the oneAPI Industry Initiative to Real-World AI Use Cases Wilson Feng, Rasool Maghareh and Amy Wang collaborate on compiler…

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Newsflash: oneAPI 1.1 Provisional Spec Available Now!

I’m thrilled to see how far oneAPI has progressed since we started on this journey in 2019. Various elements have…

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Lawrence Berkeley National Laboratory drives heterogenous computing with oneAPI’s Math Kernel Library(oneMKL)

oneMKL Random Number Generators Domain now supports Nvidia GPUs Dr. Vincent R. Pascuzzi The increasing number of high-performance computing centers…

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