ºÝºÝߣshows by User: wackytrixxie1 / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: wackytrixxie1 / Wed, 28 Jun 2017 17:34:19 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: wackytrixxie1 Intel the-latest-on-ofi /slideshow/intel-thelatestonofi/77345536 intel-the-latest-on-ofi-170628173419
With the diversity of platforms, it is impossible for MPI libraries to automatically provide the best performance for all existing applications. In this session, we demonstrate that Intel® MPI Library is not a black box and contains several features allowing users to enhance MPI applications. From basic (process mapping, collective tuning) to advanced features (unreliable datagram, kernel-assisted approaches), this session covers a large spectrum of possibilities offered by the Intel MPI Library to improve the performance of parallel applications on high-performance computing (HPC) systems. This session introduces tuning flags and explains features available on Intel MPI Library by highlighting results obtained on a Stampede* cluster. It is designed to help beginner and intermediate Intel MPI Library users to better understand all of the library's capabilities. Resources]]>
With the diversity of platforms, it is impossible for MPI libraries to automatically provide the best performance for all existing applications. In this session, we demonstrate that Intel® MPI Library is not a black box and contains several features allowing users to enhance MPI applications. From basic (process mapping, collective tuning) to advanced features (unreliable datagram, kernel-assisted approaches), this session covers a large spectrum of possibilities offered by the Intel MPI Library to improve the performance of parallel applications on high-performance computing (HPC) systems. This session introduces tuning flags and explains features available on Intel MPI Library by highlighting results obtained on a Stampede* cluster. It is designed to help beginner and intermediate Intel MPI Library users to better understand all of the library's capabilities. Resources]]> Wed, 28 Jun 2017 17:34:19 GMT /slideshow/intel-thelatestonofi/77345536 wackytrixxie1@slideshare.net(wackytrixxie1) Intel the-latest-on-ofi wackytrixxie1 With the diversity of platforms, it is impossible for MPI libraries to automatically provide the best performance for all existing applications. In this session, we demonstrate that Intel® MPI Library is not a black box and contains several features allowing users to enhance MPI applications. From basic (process mapping, collective tuning) to advanced features (unreliable datagram, kernel-assisted approaches), this session covers a large spectrum of possibilities offered by the Intel MPI Library to improve the performance of parallel applications on high-performance computing (HPC) systems. This session introduces tuning flags and explains features available on Intel MPI Library by highlighting results obtained on a Stampede* cluster. It is designed to help beginner and intermediate Intel MPI Library users to better understand all of the library's capabilities. Resources <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/intel-the-latest-on-ofi-170628173419-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> With the diversity of platforms, it is impossible for MPI libraries to automatically provide the best performance for all existing applications. In this session, we demonstrate that Intel® MPI Library is not a black box and contains several features allowing users to enhance MPI applications. From basic (process mapping, collective tuning) to advanced features (unreliable datagram, kernel-assisted approaches), this session covers a large spectrum of possibilities offered by the Intel MPI Library to improve the performance of parallel applications on high-performance computing (HPC) systems. This session introduces tuning flags and explains features available on Intel MPI Library by highlighting results obtained on a Stampede* cluster. It is designed to help beginner and intermediate Intel MPI Library users to better understand all of the library&#39;s capabilities. Resources
Intel the-latest-on-ofi from Tracy Johnson
]]> 96 3 https://cdn.slidesharecdn.com/ss_thumbnails/intel-the-latest-on-ofi-170628173419-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Intel colfax optimizing-machine-learning-workloads /slideshow/intel-colfax-optimizingmachinelearningworkloads/77345380 intelcolfaxoptimizing-machine-learning-workloads-170628172920
In this lecture with live code modification components, we showcase distributed deep learning on an Intel® Xeon Phi™ processor cluster with Intel® Omni-Path Architecture. It targets developers of all skill levels, and is designed to give a brief but hands-on introduction to the machine learning frameworks with Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) enhancements. Start with a brief introduction to machine learning frameworks that are optimized with the new Intel® MKL-DNN. Develop a simple deep learning image recognition application using the framework. Observe how the computational performance of this application scales while adding compute nodes.]]>
In this lecture with live code modification components, we showcase distributed deep learning on an Intel® Xeon Phi™ processor cluster with Intel® Omni-Path Architecture. It targets developers of all skill levels, and is designed to give a brief but hands-on introduction to the machine learning frameworks with Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) enhancements. Start with a brief introduction to machine learning frameworks that are optimized with the new Intel® MKL-DNN. Develop a simple deep learning image recognition application using the framework. Observe how the computational performance of this application scales while adding compute nodes.]]> Wed, 28 Jun 2017 17:29:19 GMT /slideshow/intel-colfax-optimizingmachinelearningworkloads/77345380 wackytrixxie1@slideshare.net(wackytrixxie1) Intel colfax optimizing-machine-learning-workloads wackytrixxie1 In this lecture with live code modification components, we showcase distributed deep learning on an Intel® Xeon Phi™ processor cluster with Intel® Omni-Path Architecture. It targets developers of all skill levels, and is designed to give a brief but hands-on introduction to the machine learning frameworks with Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) enhancements. Start with a brief introduction to machine learning frameworks that are optimized with the new Intel® MKL-DNN. Develop a simple deep learning image recognition application using the framework. Observe how the computational performance of this application scales while adding compute nodes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/intelcolfaxoptimizing-machine-learning-workloads-170628172920-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this lecture with live code modification components, we showcase distributed deep learning on an Intel® Xeon Phi™ processor cluster with Intel® Omni-Path Architecture. It targets developers of all skill levels, and is designed to give a brief but hands-on introduction to the machine learning frameworks with Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) enhancements. Start with a brief introduction to machine learning frameworks that are optimized with the new Intel® MKL-DNN. Develop a simple deep learning image recognition application using the framework. Observe how the computational performance of this application scales while adding compute nodes.
Intel colfax optimizing-machine-learning-workloads from Tracy Johnson
]]> 86 2 https://cdn.slidesharecdn.com/ss_thumbnails/intelcolfaxoptimizing-machine-learning-workloads-170628172920-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-wackytrixxie1-48x48.jpg?cb=1524817035 For those who new me before getting married.. You would have known me as Tracy Mezzatesta. http://digitalbackpacker.tumblr.com/ https://cdn.slidesharecdn.com/ss_thumbnails/intel-the-latest-on-ofi-170628173419-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/intel-thelatestonofi/77345536 Intel the-latest-on-ofi https://cdn.slidesharecdn.com/ss_thumbnails/intelcolfaxoptimizing-machine-learning-workloads-170628172920-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/intel-colfax-optimizingmachinelearningworkloads/77345380 Intel colfax optimizin...