{"id":176028,"date":"2017-02-07T22:25:15","date_gmt":"2017-02-08T03:25:15","guid":{"rendered":"http:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/the-evolution-and-maturation-of-hpc-in-the-enterprise-cio\/"},"modified":"2017-02-07T22:25:15","modified_gmt":"2017-02-08T03:25:15","slug":"the-evolution-and-maturation-of-hpc-in-the-enterprise-cio","status":"publish","type":"post","link":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/evolution\/the-evolution-and-maturation-of-hpc-in-the-enterprise-cio\/","title":{"rendered":"The Evolution and Maturation of HPC in the Enterprise &#8211; CIO"},"content":{"rendered":"<p><p>    By Adnan Khaleel  <\/p>\n<p>    The convergence of high performance computing (HPC) and big    data has been under way for years. As I noted in an     earlier blog, HPC and big data grew up in different worlds    and are now coming togetherdue to necessity. People using HPC    applications often work with big data, and people working with    big data often need the processing power of HPC systems. This    convergence is giving rise to the era of high performance data    analytics (HPDA) in the enterprise.  <\/p>\n<p>    Lets take a step back. For enterprises, data is coming at much    faster rates than anyone had expected. Whether its from the    Internet of Things, webpages, commercial transactions or other    sources, the amount of data pouring into enterprise data    centers exceeds current storage capacity. This flood of data    creates a new class of data consolidation, data handling and    data management challenges. Organizations cant just let the    data pile up. They now need to make deliberate decisions about    what data to store, what data to analyze and what data to    discard.  <\/p>\n<p>    Above all, enterprises need to find ways to turn the flood of    data into meaningful insights. This process increasingly    requires HPC capabilities that make applications run as fast as    possible. In many cases, enterprises need to generate insights    in real timewhether they need to optimize the performance of    remote equipment, respond faster to a customers needs or put    the brakes on a potentially fraudulent transaction.  <\/p>\n<p>    Lets take the example of the many enterprises that are getting    hit with an ever-growing wave of data from our world of    connected devices, the Internet of Things (IoT). To capitalize    on this data, whether in real time or over a period of time,    enterprises need to apply sophisticated machine learning and    deep learning techniques, and these techniques require HPC    systems paired with big data platforms and data analytics    tools.  <\/p>\n<p>    With HPDA, enterprises use HPC technologies to analyze big data    for rapid insights, real-time results and predictive analytics.    One study found that 67 percent of HPC users are already doing    HPDA, in addition to or instead of traditional HPC.[1]  <\/p>\n<p>    While HPDA is needed in traditional research-driven    applications of HPC, it is becoming a must-have in enterprise    environments. Depending on the industry, an enterprise might    need to leverage data-centric HPC platforms for more    traditional HPC applications like genomics, financial modeling    and signal processing, as well as new and emerging HPDA    applications like personalized medicine, fraud detection and    machine learning.  <\/p>\n<p>    The rise of new tools and technologies  <\/p>\n<p>    For organizations that need HPDA, there is good news on the    technology front: The tools and technologies for merging HPC    with data analytics are maturing rapidly. Better still, HPC and    big data platforms are converging in a manner that reduces the    need to move data back and forth between HPC and storage    environments. This convergence helps organizations avoid a    great deal of overhead and latency that comes with disparate    systems.  <\/p>\n<p>    Today, organizations can choose from a rapidly growing range of    tools and technologies like streaming analytics, graph    analytics, and exploratory data analysis in HPC environments.    Lets take a brief look at these tools.  <\/p>\n<p>    HPDA in action: case studies  <\/p>\n<p>    Lets consider a couple of real-life examples of HPDA in    action. These examples show how companies are capitalizing on    the convergence of technologies for HPC and big data.  <\/p>\n<p>    To help fight cancer and other diseases, TGen needed extremely    scalable, reliable and available HPC nodes to develop    personalized medical treatments. To meet this need, TGen    optimized its infrastructure, scaling its existing Dell EMC HPC    cluster with Dell EMC PowerEdge blades. The system    incorporates powerful big data and analytics tools, leveraging    a Dell EMC Hadoop platform and Statistica software. The    increased performance helps TGen accelerate results, enabling    researchers to expand treatments to a larger number of    patients.     Watch the video.  <\/p>\n<p>    Another Dell EMC customer, Sensus, needed to increase its data    set size to be able to more easily visualize meter sensor    performance problems. To meet this need, the company    implemented a data cluster and a data lakebased on a Hadoop    platform and technologies from Dell EMC and Intelthat    consolidates manufacturing, testing and other data streams.    With this consolidated platform, Sensus can quickly analyze    data from 17 million gas, electric and water meter sensors, and    proactively identify device problems, helping to predict and    prevent future device failures.     Read the case study.  <\/p>\n<p>    Enabling proactive maintenance with HPDA  <\/p>\n<p>    On the IoT front, HPDA technologies are enabling predictive    maintenance of assets to help prevent equipment failures,    extend machine life and help organizations gain a better return    on their assets. These technologies go beyond condition    monitoring to enable condition understanding. On its own,    condition monitoring provides time to act, but when data is    dynamically provided to a device-specific predictive model you    can achieve condition understanding. That means your users will    have time to act on maintenance events and have a clear    understanding of the actions they need to take.  <\/p>\n<p>    For organizations new to IoT, the challenges are numerous,    spanning both hardware and software. For example, they need to:  <\/p>\n<p>    And this is where expertise comes in extremely handy. With that    thought in mind, Dell EMC has joined forces with Software AG    and Kepware to produce an end-to-end solution for proactive    maintenance. It offers the complete hardware-software stack    that easily allows for the management of IoT sensors, the data    produced, and the analysis of that data in real timeultimately    easing the deployment of a comprehensive IoT based solution for    infrastructure maintenance.  <\/p>\n<p>    Thats just one of countless advances made possible by the rise    of technologies and solutions for high performance data    analytics. For a look at more of these technologies and    solutions, visit Dell.com\/HPC.  <\/p>\n<p>    Adnan Khaleel is a Global Sales Strategist for Dell    EMC.  <\/p>\n<p>    [1] HPCwire,    IDC 2015. The Changing Face of HPC.  <\/p>\n<p><!-- Auto Generated --><\/p>\n<p>Originally posted here:<\/p>\n<p><a target=\"_blank\" rel=\"nofollow\" href=\"http:\/\/www.cio.com\/article\/3167207\/analytics\/the-evolution-and-maturation-of-hpc-in-the-enterprise.html\" title=\"The Evolution and Maturation of HPC in the Enterprise - CIO\">The Evolution and Maturation of HPC in the Enterprise - CIO<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> By Adnan Khaleel The convergence of high performance computing (HPC) and big data has been under way for years. As I noted in an earlier blog, HPC and big data grew up in different worlds and are now coming togetherdue to necessity <a href=\"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/evolution\/the-evolution-and-maturation-of-hpc-in-the-enterprise-cio\/\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[187748],"tags":[],"class_list":["post-176028","post","type-post","status-publish","format-standard","hentry","category-evolution"],"_links":{"self":[{"href":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/wp-json\/wp\/v2\/posts\/176028"}],"collection":[{"href":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/wp-json\/wp\/v2\/comments?post=176028"}],"version-history":[{"count":0,"href":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/wp-json\/wp\/v2\/posts\/176028\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/wp-json\/wp\/v2\/media?parent=176028"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/wp-json\/wp\/v2\/categories?post=176028"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.euvolution.com\/prometheism-transhumanism-posthumanism\/wp-json\/wp\/v2\/tags?post=176028"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}