Tag Archives: GPU

  • New Twists in the Intertwining of HPC and AI

    The alliance between high performance computing and artificial intelligence is proving to be a fruitful one for researchers looking to accelerate scientific discovery in everything from climate prediction and genomics to particle physics and drug discovery. The impetus behind this is to use AI, and machine learning in particular, to augment the predictive capacity of…

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  • What’s New in HPC Research: Brain Mapping, Earthquakes, Energy Efficiency & More

    In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing (HPC) community and related domains. From parallel programming to exascale to quantum computing, the details are here. Overcoming limitations of GPGPU computing in scientific applications While GPU performance has improved steadily, the PCIe interconnect that connects the system host memory and the…

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  • Scalable and Distributed DNN Training on Modern HPC Systems

    In this video from the Swiss HPC Conference, DK Panda from Ohio State University presents: Scalable and Distributed DNN Training on Modern HPC Systems. The current wave of advances in Deep Learning (DL) has led to many exciting challenges and opportunities for Computer Science and Artificial Intelligence researchers alike. Modern DL frameworks like Caffe2, TensorFlow, Cognitive…

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  • Advancing U.S. Weather Predictions Capabilities with Exascale HPC

    In this video from GTC 2019, Mark Govett from NOAA presents: Advancing U.S. Weather Prediction Capabilities with Exascale HPC. We’ll discuss the revolution in computing, modeling, data handling and software development that’s needed to advance U.S. weather-prediction capabilities in the exascale computing era. Creating prediction models to cloud-resolving 1 KM-resolution scales will require an estimated 1,000-10,000 times more…

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  • Univa 2019 Predictions: More HPC Cloud Migration and Machine Learning

    Here is a short selection of Gary Tyreman’s predictions. 1)  Hybrid and dedicated clouds will drive massive growth in machine learning (ML) projects Machine learning is poised for explosive growth over the next two years with an increasing number of projects moving into production by 2020, based on a recent survey of more than 344 technology and…

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  • Migration Tools Needed to Shift ML to Production

    The confluence of accelerators like cloud GPUs along with the ability to handle data-rich HPC workloads will help push more machine learning projects into production, concludes a new study that also stresses the importance of cloud migration and accompanying tools. The survey released this week by workload management specialist Univa Corp. confirms the rush to machine…

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