{"id":23734,"date":"2020-02-10T08:00:00","date_gmt":"2020-02-10T16:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=23734"},"modified":"2020-02-11T09:06:30","modified_gmt":"2020-02-11T17:06:30","slug":"intel-parallel-studio-xe-2020-transform-enterprise-cloud-hpc-artificial-intelligence-with-faster-parallel-code","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/02\/10\/intel-parallel-studio-xe-2020-transform-enterprise-cloud-hpc-artificial-intelligence-with-faster-parallel-code\/","title":{"rendered":"Intel\u00ae Parallel Studio XE 2020: Transform Enterprise, Cloud, HPC &#038; Artificial Intelligence with Faster Parallel Code"},"content":{"rendered":"\n<div class=\"wp-block-image\"><figure class=\"alignright\"><img decoding=\"async\" loading=\"lazy\" width=\"200\" height=\"236\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/12\/Intel-Parallel-Studio-logo.png\" alt=\"\" class=\"wp-image-23735\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/12\/Intel-Parallel-Studio-logo.png 200w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/12\/Intel-Parallel-Studio-logo-127x150.png 127w\" sizes=\"(max-width: 200px) 100vw, 200px\" \/><\/figure><\/div>\n\n\n\n<p>In this article we\u2019ll drill down into the capabilities of <a href=\"https:\/\/software.intel.com\/en-us\/parallel-studio-xe\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">Intel\u00ae Parallel Studio XE<\/a> 2020, the latest release of a comprehensive, parallel programming tool suite that simplifies the creation and modernization of code. Using this newest release, software developers and architects can speed AI inferencing with support for Intel\u00ae Deep Learning Boost and Vector Neural Network Instructions (VNNI), designed to accelerate inner convolutional neural network (CNN) loops.<\/p>\n\n\n\n<p><strong>High Performance\nTools for AI Developers<\/strong><strong><\/strong><\/p>\n\n\n\n<p>Data-centric software applications that help solve critical problems across a range of industries, such as HPC, artificial intelligence (AI) and deep learning, as well as scientific research, demand ever-accelerating performance and faster parallel processing. Developers are challenged to deliver high-performance, scalable, and reliable parallel code that takes advantage of current and next-generation hardware. In response, more cores, more and wider SIMD registers, and competitive features are continuously integrated into the latest Intel\u00ae Xeon\u00ae Scalable processors\u2014and the new 2020 release of <a href=\"https:\/\/software.intel.com\/en-us\/parallel-studio-xe\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">Intel<\/a><sup><a href=\"https:\/\/software.intel.com\/en-us\/parallel-studio-xe\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">\u00ae<\/a><\/sup><a href=\"https:\/\/software.intel.com\/en-us\/parallel-studio-xe\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\"> Parallel Studio XE<\/a> makes it easier for developers to squeeze highest performance out of Intel\u00ae platforms\u2014today and for years to come. <\/p>\n\n\n\n<p>Now in its 12<sup>th<\/sup> year, this suite of 10+ best-in-class tools and performance libraries continues its proven ability to help developers optimize code for the latest multicore and many core Intel\u00ae architectures\u2014whether their focus is enterprise, cloud, HPC, or AI. (Check out the benchmarks <a href=\"https:\/\/software.intel.com\/en-us\/parallel-studio-xe\/features\/build\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">here<\/a>.) Using this newest release, developers can harness the latest techniques in vectorization, multi-threading, multi-node, and memory optimization, with these newest capabilities:<\/p>\n\n\n\n<ul><li>Speed AI inferencing with support for <a rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\" href=\"https:\/\/www.intel.ai\/intel-deep-learning-boost\/\" target=\"_blank\">Intel\u00ae Deep Learning (DL) Boost<\/a> with <a href=\"https:\/\/www.intel.ai\/vnni-enables-inference\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">Vector Neural Network Instructions (VNNI) <\/a>&nbsp;in 2<sup>nd<\/sup> generation Intel\u00ae Xeon\u00ae Scalable Processors in Intel\u00ae Compilers, Intel\u00ae Performance Libraries and analysis tools. Intel\u00ae Xeon\u00ae Scalable processors are built specifically for the flexibility to run complex AI workloads on the same hardware as your existing workloads, taking embedded AI performance to the next level with Intel\u00ae DL Boost. VNNI can be thought of as an AI inference accelerator integrated into every 2nd Gen Intel Xeon Scalable processor. <\/li><li>Develop for large memories of up to 512GB DIMMs with persistence. Identify, optimize and tune Intel\u00ae platforms for Intel\u00ae Optane\u2122 DC Persistent Memory using Intel\u00ae VTune\u2122 Profiler. <\/li><li>Stay updated with the latest standards support, including Fortran 2018 features, C++17 (with initial C++20 support), and OpenMP 4.5\/5.0.<\/li><li>Understand and optimize platform configuration for applications through extended, coarse-grained profiling using platform-level collection and analysis in Intel VTune Profiler.<\/li><li>Get HPC cloud support with low-latency, high-bandwidth communications for MPI applications using the AWS* Parallel Cluster* and AWS Elastic Fabric Adapter* in the Intel\u00ae MPI Library.<\/li><li>Take advantage of support for the latest Intel\u00ae processors including Intel\u00ae Xeon\u00ae Scalable Processors (codenamed Cascade Lake\/Cascade Lake AP\/Cooper Lake\/Ice Lake).<\/li><li>Harness support for Amazon Linux* 2, the AWS* next-gen Linux OS that offers a secure, stable, high-performance execution environment to develop and run cloud and enterprise applications.<sup>1<\/sup><\/li><li>Access priority support for a full year to connect directly with Intel engineers and get quick answers to technical questions.<\/li><\/ul>\n\n\n\n<p><strong>Deep\nLearning Parallelism<\/strong><\/p>\n\n\n\n<p>Deep learning has an incredible propensity to tackle data-centric problems across a wide spectrum of domains, such as object detection for autonomous vehicles, facial recognition, and natural language processing (NLP) for conversational AI, among many others. <\/p>\n\n\n\n<p>Parallel programming plays a key role in allowing deep learning to work its magic by enabling software programs to take advantage of multicore and many core systems to accelerate the training of deep neural networks. Parallelism ensures that compute-intensive workloads are fast, reliable, and scalable, particularly important when processing deep neural networks (DNNs) using optimization methods such as stochastic gradient descent (SGD), along with popular weight update rules: learning rate, adaptive learning rate, momentum, Nesterov momentum, AdaGrad, RMSProp, and Adam. These methods are all steeped in linear algebra and partial differential equations. Allowing these methods to run in parallel can yield a reduction in training times from days to seconds. An excellent survey paper that explores parallelism from a theoretical perspective is \u201c<a href=\"https:\/\/arxiv.org\/pdf\/1802.09941.pdf\">Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis<\/a>,\u201d by Ben-Nun and Hoefler. <\/p>\n\n\n\n<p>Using tools like the Intel\u00ae Parallel Studio XE 2020 tool suite allows developers to capitalize on this parallelism in deep learning, specifically parallel deep convolutional neural network (CNN) training. CNN training is a computationally intensive task whose parallelization has become critical in order to complete the training within an acceptable time period. Many of the tool suite\u2019s features enable the use of deep learning technology.  <\/p>\n\n\n\n<p><strong>Multiple Editions<\/strong><\/p>\n\n\n\n<p>Intel\u00ae Parallel Studio XE 2020 comes in <a href=\"https:\/\/software.intel.com\/en-us\/parallel-studio-xe\/choose-download\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">three editions<\/a>, each catering to specific levels of developer needs:<\/p>\n\n\n\n<ul><li>Composer Edition &#8211; Includes Intel\u00ae C++ and Fortran compilers, performance\nlibraries, and performance-optimized Python* libraries.<\/li><li>Professional Edition &#8211; Includes everything in the Composer Edition, plus\nperformance profiling, a memory and thread debugger, and design tools to\nsimplify adding threading and vectorization.<\/li><li>Cluster Edition \u2013 Includes everything in the Professional Edition, plus an\nMPI library, MPI profiling and error-checking tools, and an advanced cluster\ndiagnostic expert system tool.<\/li><\/ul>\n\n\n\n<p><strong>Conclusion<\/strong><\/p>\n\n\n\n<p>This latest\nrelease of a tried and proven tool suite simplifies the creation and\nmodernization of code and accelerates workloads using the latest techniques in\nvectorization, multi-threading, multi-node, and memory optimization. It\ncombines industry-leading, standards-based compilers, award-winning numerical\nlibraries, performance profilers, and code analyzers so developers\u2014C\/C++,\nFortran and Python\u2014can confidently optimize software delivering high\nperformance code that scales efficiently on today\u2019s and future Intel\u00ae\nplatforms. <\/p>\n\n\n\n<p><a href=\"https:\/\/software.intel.com\/en-us\/parallel-studio-xe\/choose-download\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">Try it Now &gt;<\/a><\/p>\n\n\n\n<p><sup>1<\/sup>Supported features of tools and libraries\nmay vary by instances and configurations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this article we\u2019ll drill down into the capabilities of Intel\u00ae Parallel Studio XE 2020, the latest release of a comprehensive, parallel programming tool suite that simplifies the creation and modernization of code. Using this newest release, software developers and architects can speed AI inferencing with support for Intel\u00ae Deep Learning Boost and Vector Neural Network Instructions (VNNI), designed to accelerate inner convolutional neural network (CNN) loops.<\/p>\n","protected":false},"author":10513,"featured_media":23735,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,87,180,210,56,311,1],"tags":[437,324,264,284,788,774,832,95],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Intel\u00ae Parallel Studio XE 2020: Transform Enterprise, Cloud, HPC &amp; Artificial Intelligence with Faster Parallel Code - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2020\/02\/10\/intel-parallel-studio-xe-2020-transform-enterprise-cloud-hpc-artificial-intelligence-with-faster-parallel-code\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Intel\u00ae Parallel Studio XE 2020: Transform Enterprise, Cloud, HPC &amp; Artificial Intelligence with Faster Parallel Code - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this article we\u2019ll drill down into the capabilities of Intel\u00ae Parallel Studio XE 2020, the latest release of a comprehensive, parallel programming tool suite that simplifies the creation and modernization of code. 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Intel shared the optimization results in a recently published solution\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/DarwinAI_solution_pic.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":19837,"url":"https:\/\/insidebigdata.com\/2018\/01\/23\/convergence-ai-data-hpc\/","url_meta":{"origin":23734,"position":3},"title":"Exploring the Convergence of AI, Data and HPC","date":"January 23, 2018","format":false,"excerpt":"The demand for performant and scalable AI solutions has stimulated a convergence of science, algorithm development, and affordable technologies to create a software ecosystem designed to support the data scientist. A special insideHPC report explores how HPC and the data driven AI communities are converging as they are arguably running\u2026","rel":"","context":"In &quot;Featured Resource - Chronological&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2018\/01\/IntelOMD_IBDCover2_2018-01-22.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":23141,"url":"https:\/\/insidebigdata.com\/2019\/08\/26\/develop-multiplatform-computer-vision-solutions-with-intel-distribution-of-openvino-toolkit\/","url_meta":{"origin":23734,"position":4},"title":"Develop Multiplatform Computer Vision Solutions with Intel\u00ae Distribution of OpenVINO\u2122 Toolkit","date":"August 26, 2019","format":false,"excerpt":"Realize your computer vision deployment needs on Intel\u00ae platforms\u2014from smart cameras and video surveillance to robotics, transportation, and much more. The Intel\u00ae Distribution of OpenVINO\u2122 Toolkit (includes the Intel\u00ae Deep Learning Deployment Toolkit) allows for the development of deep learning inference solutions for multiple platforms.","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/OpenVINO_pic1.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":23061,"url":"https:\/\/insidebigdata.com\/2019\/08\/14\/the-ai-opportunity\/","url_meta":{"origin":23734,"position":5},"title":"The AI Opportunity","date":"August 14, 2019","format":false,"excerpt":"The tremendous growth in compute power and explosion of data is leading every industry to seek AI-based solutions. In this Tech.Decoded video, \u201cThe AI Opportunity - Episode 1: The Compute Power Difference,\u201d Vice President of Intel Architecture and AI expert Wei Li shares his views on the opportunities and challenges\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_AI_opportunities_pic.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/23734"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/10513"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=23734"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/23734\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/23735"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=23734"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=23734"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=23734"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}