{"id":23087,"date":"2019-08-12T08:30:25","date_gmt":"2019-08-12T15:30:25","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=23087"},"modified":"2019-08-13T09:02:56","modified_gmt":"2019-08-13T16:02:56","slug":"fast-track-application-performance-and-development-with-intel-performance-libraries","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/","title":{"rendered":"Fast-track Application Performance and Development with Intel\u00ae Performance Libraries"},"content":{"rendered":"\n<p style=\"text-align:center\"><em>Sponsored Post<\/em><\/p>\n\n\n\n<p>Intel continues its strident efforts to refine libraries optimized to yield the utmost performance from Intel\u00ae processors. The <a href=\"https:\/\/software.seek.intel.com\/performance-libraries?utm_campaign=cmd_perflib&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_perflib_ibd_synd\">Intel\u00ae Performance Libraries<\/a> provide a large collection of prebuilt and tested, performance-optimized functions to developers. By utilizing these libraries, developers can reduce the costs and time associated with software development and maintenance, and focus efforts on their own application code. Intel has a mission to support innovation and impressive performance: <\/p>\n\n\n\n<ul><li>Intel\u00ae Data\nAnalytics Acceleration Library &#8211; boosts machine learning and big data analytics,\noptimizing across all data analysis stages<\/li><li>Intel\u00ae Integrated\nPerformance Primitives &#8211; highly optimized image, signal, data compression, and\ncryptography functions<\/li><li>Intel\u00ae Math\nKernel Library &#8211; features highly optimized, threaded, and vectorized functions\nto maximize performance on each processor family<\/li><li>Intel\u00ae MPI\nLibrary &#8211; focuses on enabling Message Passing Interface\n(MPI) applications to perform better for clusters based on Intel\u00ae architecture<\/li><li>Intel\u00ae Threading\nBuilding Blocks &#8211; scalable parallel model to implement task-based parallelism<\/li><\/ul>\n\n\n\n<p>The functions contained in the\nlibraries have been carefully optimized to capitalize on specific performance features\nbuilt into current Intel processors and will be optimized for future Intel\nprocessors. An important advantage of using the Intel Performance Libraries is that\nthey provide transparent portability of application programs across the full\nrange of Intel processors. <\/p>\n\n\n\n<p>The <a href=\"https:\/\/software.intel.com\/en-us\/intel-daal?utm_campaign=cmd_daal&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_daal_ibd_synd\">Intel\u00ae Data Analytics Acceleration Library<\/a> (Intel\u00ae DAAL) helps boost machine learning and big-data analytics and helps data engineers reduce the time it takes to develop high-performance applications. Intel DAAL enables applications to make better predictions faster and analyze larger data sets with available compute resources. Simply link to the newest version and your code is ready for the latest processors. This library addresses all stages of the data analytics pipeline: preprocessing, transformation, analysis, modeling, validation, and decision-making.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" loading=\"lazy\" width=\"835\" height=\"168\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_DAAL_pic.png\" alt=\"\" class=\"wp-image-23088\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_DAAL_pic.png 835w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_DAAL_pic-150x30.png 150w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_DAAL_pic-300x60.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_DAAL_pic-768x155.png 768w\" sizes=\"(max-width: 835px) 100vw, 835px\" \/><figcaption>Intel DAAL Fits in the Data Analytics Ecosystem<\/figcaption><\/figure>\n\n\n\n<p>The <a href=\"https:\/\/software.intel.com\/en-us\/ipp?utm_campaign=cmd_mult_tools&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_mult_tools_ibd_synd\">Intel\u00ae Integrated Performance Primitives<\/a> (Intel\u00ae IPP) is a valuable resource for programming tools and libraries that are highly optimized for a wide range of Intel\u00ae architecture (Intel Atom\u00ae, Intel\u00ae Core\u2122, and Intel\u00ae Xeon\u00ae processors). These ready-to-use, APIs are used by software developers, integrators, and solution providers to tune their applications and get the best performance. <\/p>\n\n\n\n<p>Intel\nIPP software building blocks are highly optimized using Intel\u00ae Streaming SIMD\nExtensions (Intel\u00ae SSE), Intel\u00ae Advanced Vector Extensions 2 (Intel\u00ae AVX2), and\nIntel\u00ae Advanced Vector Extensions 512 (Intel\u00ae AVX-512) instruction sets. Plug\nin these primitives to have your applications perform faster than what an\noptimizing compiler can produce alone.<\/p>\n\n\n\n<p>Intel\nIPP offers thousands of optimized functions for commonly used algorithms,\nincluding those for creating digital media, enterprise data, embedded\ncommunications, and scientific, technical, and security applications. The\nlibrary includes more than 2,500 image processing, 1,300 signal processing, 500\ncomputer vision, and 300 cryptography primitives.<\/p>\n\n\n\n<p>The <a href=\"https:\/\/software.intel.com\/en-us\/mkl?utm_campaign=cmd_mkl&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_mkl_ibd_synd\">Intel\u00ae Math Kernel Library<\/a> (Intel\u00ae MKL) optimizes code and offers a choice of compilers, languages, operating systems, and linking and threading models. The library features highly optimized, threaded, and vectorized math functions that maximize performance on each processor family. The library uses industry-standard C and Fortran APIs for compatibility with popular Basic Linear Algebra Subprograms (BLAS), Linear Algebra Package (LAPACK), and Fast Fourier Transform (FFT) functions\u2014no code changes required. Intel MKL dispatches optimized code for each processor automatically without the need to branch code.<\/p>\n\n\n\n<p>Intel and Cloudera have collaborated to\nspeed up Spark\u2019s machine learning (ML) algorithms via integration with the Intel\u00ae\nMKL. Spark\u2019s ML libraries (known as\nMLlib) is a leading solution for machine learning on large distributed data\nsets.<\/p>\n\n\n\n<p>The <a href=\"https:\/\/software.intel.com\/en-us\/mpi-library?utm_campaign=cmd_mpi&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_mpi_ibd_synd\">Intel\u00ae MPI Library<\/a> is a multi-fabric message-passing library that implements the open-source MPICH specification. The library is used to create, maintain, and test advanced, complex applications that perform well on HPC clusters based on Intel\u00ae processors. You can develop applications that can run on multiple cluster interconnects chosen by the user at run time, and quickly deliver maximum end-user performance without having to change the software or operating environment. The Intel<sup>\u00ae<\/sup> MPI library helps you achieve the best latency, bandwidth, and scalability through automatic tuning for the latest Intel\u00ae platforms. In addition, you can reduce the time to market by linking to one library and deploying on the latest optimized fabrics.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" loading=\"lazy\" width=\"500\" height=\"480\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_MPI_library.png\" alt=\"\" class=\"wp-image-23089\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_MPI_library.png 500w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_MPI_library-150x144.png 150w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_MPI_library-300x288.png 300w\" sizes=\"(max-width: 500px) 100vw, 500px\" \/><\/figure><\/div>\n\n\n\n<p>The <a href=\"https:\/\/software.intel.com\/en-us\/tbb?utm_campaign=cmd_tbb&amp;utm_source=ibd&amp;utm_medium=synd&amp;utm_content=prod-info&amp;utm_term=hpc_ww&amp;cid=cmd_tbb_ibd_synd\">Intel\u00ae Threading Building Blocks Library<\/a> (Intel\u00ae TBB) allows for advanced threading for fast, scalable parallel applications. It also provides the ability to parallelize computationally intensive work, delivering higher-level and simpler solutions using standard C++. Intel\u00ae TBB is a feature-rich and comprehensive solution for parallel application development and highly portable, composable, affordable, and approachable and also provides future-proof scalability. Intel\u00ae TBB is a C++ library for shared-memory parallel programming and intra-node distributed memory programming. The library provides a wide range of features for parallel programming, including generic parallel algorithms, concurrent containers, a scalable memory allocator, work-stealing task scheduler, and low-level synchronization primitives.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Intel continues its strident efforts to refine libraries optimized to yield the utmost performance from Intel\u00ae processors. The Intel\u00ae Performance Libraries provide a large collection of prebuilt and tested, performance-optimized functions to developers. By utilizing these libraries, developers can reduce the costs and time associated with software development and maintenance, and focus efforts on their own application code.<\/p>\n","protected":false},"author":37,"featured_media":23091,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[182,87,180,210,773,56,1],"tags":[133,568,95],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Fast-track Application Performance and Development with Intel\u00ae Performance Libraries - 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\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fast-track Application Performance and Development with Intel\u00ae Performance Libraries - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Intel continues its strident efforts to refine libraries optimized to yield the utmost performance from Intel\u00ae processors. The Intel\u00ae Performance Libraries provide a large collection of prebuilt and tested, performance-optimized functions to developers. By utilizing these libraries, developers can reduce the costs and time associated with software development and maintenance, and focus efforts on their own application code.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2019-08-12T15:30:25+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2019-08-13T16:02:56+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_Performance_libraries.png\" \/>\n\t<meta property=\"og:image:width\" content=\"300\" \/>\n\t<meta property=\"og:image:height\" content=\"166\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Daniel Gutierrez\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@AMULETAnalytics\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel Gutierrez\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/\",\"url\":\"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/\",\"name\":\"Fast-track Application Performance and Development with Intel\u00ae Performance Libraries - insideBIGDATA\",\"isPartOf\":{\"@id\":\"https:\/\/insidebigdata.com\/#website\"},\"datePublished\":\"2019-08-12T15:30:25+00:00\",\"dateModified\":\"2019-08-13T16:02:56+00:00\",\"author\":{\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\"},\"breadcrumb\":{\"@id\":\"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/insidebigdata.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Fast-track Application Performance and Development with Intel\u00ae Performance Libraries\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/insidebigdata.com\/#website\",\"url\":\"https:\/\/insidebigdata.com\/\",\"name\":\"insideBIGDATA\",\"description\":\"Your Source for AI, Data Science, Deep Learning &amp; Machine Learning Strategies\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/insidebigdata.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\",\"name\":\"Daniel Gutierrez\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"caption\":\"Daniel Gutierrez\"},\"description\":\"Daniel D. Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.\",\"sameAs\":[\"http:\/\/www.insidebigdata.com\",\"https:\/\/twitter.com\/@AMULETAnalytics\"],\"url\":\"https:\/\/insidebigdata.com\/author\/dangutierrez\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Fast-track Application Performance and Development with Intel\u00ae Performance Libraries - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/","og_locale":"en_US","og_type":"article","og_title":"Fast-track Application Performance and Development with Intel\u00ae Performance Libraries - insideBIGDATA","og_description":"Intel continues its strident efforts to refine libraries optimized to yield the utmost performance from Intel\u00ae processors. The Intel\u00ae Performance Libraries provide a large collection of prebuilt and tested, performance-optimized functions to developers. By utilizing these libraries, developers can reduce the costs and time associated with software development and maintenance, and focus efforts on their own application code.","og_url":"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/","og_site_name":"insideBIGDATA","article_publisher":"http:\/\/www.facebook.com\/insidebigdata","article_published_time":"2019-08-12T15:30:25+00:00","article_modified_time":"2019-08-13T16:02:56+00:00","og_image":[{"width":300,"height":166,"url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_Performance_libraries.png","type":"image\/png"}],"author":"Daniel Gutierrez","twitter_card":"summary_large_image","twitter_creator":"@AMULETAnalytics","twitter_site":"@insideBigData","twitter_misc":{"Written by":"Daniel Gutierrez","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/","url":"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/","name":"Fast-track Application Performance and Development with Intel\u00ae Performance Libraries - insideBIGDATA","isPartOf":{"@id":"https:\/\/insidebigdata.com\/#website"},"datePublished":"2019-08-12T15:30:25+00:00","dateModified":"2019-08-13T16:02:56+00:00","author":{"@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed"},"breadcrumb":{"@id":"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/insidebigdata.com\/2019\/08\/12\/fast-track-application-performance-and-development-with-intel-performance-libraries\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/insidebigdata.com\/"},{"@type":"ListItem","position":2,"name":"Fast-track Application Performance and Development with Intel\u00ae Performance Libraries"}]},{"@type":"WebSite","@id":"https:\/\/insidebigdata.com\/#website","url":"https:\/\/insidebigdata.com\/","name":"insideBIGDATA","description":"Your Source for AI, Data Science, Deep Learning &amp; Machine Learning Strategies","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/insidebigdata.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed","name":"Daniel Gutierrez","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g","caption":"Daniel Gutierrez"},"description":"Daniel D. Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_Performance_libraries.png","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-60n","jetpack-related-posts":[{"id":23011,"url":"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/","url_meta":{"origin":23087,"position":0},"title":"Supercharge Data Science Applications with the Intel\u00ae Distribution for Python","date":"July 29, 2019","format":false,"excerpt":"Intel\u00ae Distribution for Python is a distribution of commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science. With Intel\u00ae Distribution for Python you can supercharge Python applications and speed up core computational packages with this performance-oriented distribution. Professionals who\u2026","rel":"","context":"In &quot;Data Science&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_dist.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":18847,"url":"https:\/\/insidebigdata.com\/2017\/09\/13\/intel-parallel-studio-xe-helps-developers-take-hpc-enterprise-cloud-applications-max\/","url_meta":{"origin":23087,"position":1},"title":"Intel\u00ae Parallel Studio XE Helps Developers Take their HPC, Enterprise, and Cloud Applications to the Max","date":"September 13, 2017","format":false,"excerpt":"Intel\u00ae Parallel Studio XE is a comprehensive suite of development tools that make it fast and easy to build modern code that gets every last ounce of performance out of the newest Intel\u00ae processors. This tool-packed suite simplifies creating code with the latest techniques in vectorization, multi- threading, multi-node, and\u2026","rel":"","context":"In &quot;Featured&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2017\/09\/Intel-Parallel-Studio.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":23824,"url":"https:\/\/insidebigdata.com\/2020\/01\/13\/oneapi-a-unified-cross-architecture-high-performance-programming-model-designed-to-help-shape-the-future-of-application-development\/","url_meta":{"origin":23087,"position":2},"title":"oneAPI: &#8211; A Unified Cross-Architecture, High Performance Programming Model Designed to Help Shape the Future of Application Development","date":"January 13, 2020","format":false,"excerpt":"In this article, we\u2019ll dive into the newly announced oneAPI, a single, unified programming model that aims to simplify development across multiple architectures, such as CPUs, GPUs, FPGAs and other accelerators. The long-term journey is represented by two important first-steps \u2013 the industry initiative and the Intel beta product.","rel":"","context":"In &quot;Featured&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2020\/01\/oneAPI_toolkit.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":19053,"url":"https:\/\/insidebigdata.com\/2017\/10\/18\/building-fast-data-compression-code-cloud-edge-applications\/","url_meta":{"origin":23087,"position":3},"title":"Building Fast Data Compression Code for Cloud and Edge Applications","date":"October 18, 2017","format":false,"excerpt":"Finding efficient ways to compress and decompress data is more important than ever. Compressed data takes up less space and requires less time and network bandwidth to transfer. In this article, we\u2019ll discuss the data compression functions and the latest improvements in the Intel\u00ae Integrated Performance Primitives (Intel\u00ae IPP) library.","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":19007,"url":"https:\/\/insidebigdata.com\/2017\/10\/05\/solutions-autonomous-driving-car-cloud\/","url_meta":{"origin":23087,"position":4},"title":"Solutions for Autonomous Driving &#8211; From Car to Cloud","date":"October 5, 2017","format":false,"excerpt":"From car to cloud\u2015and the connectivity in between\u2015there is a need for automated driving solutions that include high-performance platforms, software development tools, and robust technologies for the data center. With Intel GO automotive driving solutions, Intel brings its deep expertise in computing, connectivity, and the cloud to the automotive industry.","rel":"","context":"In &quot;Featured&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2017\/10\/Intel_System_Studio_pic.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":23734,"url":"https:\/\/insidebigdata.com\/2020\/02\/10\/intel-parallel-studio-xe-2020-transform-enterprise-cloud-hpc-artificial-intelligence-with-faster-parallel-code\/","url_meta":{"origin":23087,"position":5},"title":"Intel\u00ae Parallel Studio XE 2020: Transform Enterprise, Cloud, HPC &#038; Artificial Intelligence with Faster Parallel Code","date":"February 10, 2020","format":false,"excerpt":"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\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/12\/Intel-Parallel-Studio-logo.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/23087"}],"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\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=23087"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/23087\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/23091"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=23087"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=23087"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=23087"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}