<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>Thema "ippsMean 2" in Intel® Integrated Performance Primitives</title>
    <link>https://community.intel.com/t5/Intel-Integrated-Performance/ippsMean-2/m-p/767992#M455</link>
    <description>Hi. &lt;BR /&gt;What I meant in the prev thread ippsMean 1 , ippsMeanColumn has the solution in how to make the averages for example in the FFT , let's suppose to have 1024 of FFT's beans magnetudes and we want to average them for 20 times , with ippsMeanColumn simple giving the 1024x20 array's pointer you have the result for each column in one 1024 1D array . no loop just one call , what Isee with ippsMean instead you have just a number for the average of one row, seem to be another job . Is this right ? or what else now can do the mentioned FFT job ? &lt;BR /&gt;Thanks&lt;BR /&gt;Cartu&lt;BR /&gt;</description>
    <pubDate>Thu, 09 Dec 2010 20:46:05 GMT</pubDate>
    <dc:creator>carlo-turri</dc:creator>
    <dc:date>2010-12-09T20:46:05Z</dc:date>
    <item>
      <title>ippsMean 2</title>
      <link>https://community.intel.com/t5/Intel-Integrated-Performance/ippsMean-2/m-p/767992#M455</link>
      <description>Hi. &lt;BR /&gt;What I meant in the prev thread ippsMean 1 , ippsMeanColumn has the solution in how to make the averages for example in the FFT , let's suppose to have 1024 of FFT's beans magnetudes and we want to average them for 20 times , with ippsMeanColumn simple giving the 1024x20 array's pointer you have the result for each column in one 1024 1D array . no loop just one call , what Isee with ippsMean instead you have just a number for the average of one row, seem to be another job . Is this right ? or what else now can do the mentioned FFT job ? &lt;BR /&gt;Thanks&lt;BR /&gt;Cartu&lt;BR /&gt;</description>
      <pubDate>Thu, 09 Dec 2010 20:46:05 GMT</pubDate>
      <guid>https://community.intel.com/t5/Intel-Integrated-Performance/ippsMean-2/m-p/767992#M455</guid>
      <dc:creator>carlo-turri</dc:creator>
      <dc:date>2010-12-09T20:46:05Z</dc:date>
    </item>
    <item>
      <title>ippsMean 2</title>
      <link>https://community.intel.com/t5/Intel-Integrated-Performance/ippsMean-2/m-p/767993#M456</link>
      <description>Cartu,&lt;BR /&gt;&lt;BR /&gt;could you please compare performance of loop of 20 iterations of calls ippsMean from 7.0 with performance of ippsMeanColumn from 6.1? I actually do not think there is big difference in performance.&lt;BR /&gt;&lt;BR /&gt;Regards,&lt;BR /&gt; Vladimir</description>
      <pubDate>Thu, 09 Dec 2010 21:44:07 GMT</pubDate>
      <guid>https://community.intel.com/t5/Intel-Integrated-Performance/ippsMean-2/m-p/767993#M456</guid>
      <dc:creator>Vladimir_Dudnik</dc:creator>
      <dc:date>2010-12-09T21:44:07Z</dc:date>
    </item>
    <item>
      <title>ippsMean 2</title>
      <link>https://community.intel.com/t5/Intel-Integrated-Performance/ippsMean-2/m-p/767994#M457</link>
      <description>I think the two are not analogous. When we want to average across a set of vectors, the data is not aligned such that ippsMean can be used. If it were, it might mean thousands of calls to ippsMean.&lt;DIV&gt;&lt;SPAN style="font-family: Verdana, Arial, Helvetica, sans-serif;"&gt;&lt;BR /&gt;&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN style="font-family: Verdana, Arial, Helvetica, sans-serif;"&gt;The preferred alternative is, I think, is to use ippsAdd_*_I repeatedly, followed by an ippsDivC_*_I.&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;SPAN style="white-space: pre;"&gt;		&lt;/SPAN&gt;&lt;/DIV&gt;&lt;/DIV&gt;</description>
      <pubDate>Tue, 26 Apr 2011 02:07:58 GMT</pubDate>
      <guid>https://community.intel.com/t5/Intel-Integrated-Performance/ippsMean-2/m-p/767994#M457</guid>
      <dc:creator>jeffc111</dc:creator>
      <dc:date>2011-04-26T02:07:58Z</dc:date>
    </item>
  </channel>
</rss>

