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• ffmpeg encoder streaming issues

  8 août 2017, par bobsingh1

  I am trying to build ffmpeg encoder on linux. I started with a custom built server Dual 1366 2.6 Ghz Xeon CPUs (6 cores) with 16 GB RAM with Ubuntu 16.04 minimal install. Built ffmpeg with h264 and aac. I am taking live source OTA channels and encoding/streaming them with following parameters

  -vcodec libx264 -preset superfast -crf 25 -x264opts keyint=60:min-keyint=60:scenecut=-1 -bufsize 7000k -b:v 6000k -maxrate 6300k -muxrate 6000k -s 1920x1080 -format yuv420p -g 60 -sn -c:a aac -b:a 384k -ar 44100

  And I am able to successfully udp out using mpegts. My problem starts with 5th stream. The server can handle four streams and as soon as I introduce 5th stream I start seeing hiccups in output. Looking at my cpu usage using top I still see only 65% to 75% usage with occasional 80% hit. Memory usage is well within acceptable parameters. So I am wondering either top is not giving me accurate cpu usage or something is not right with ffmpeg. The server is isolated for udp in/out on a 1 Gbps network.

  I decided to up the cpu power and installed two 3.5 Ghz CPUs (6 cores) thinking it was perhaps the cpu clock. To my surprise the results were no different. So now I am wondering is there some built in limit I am hitting when I process at 1080p. If I change the resolution to 720p it is able to process 8 streams but 720 is not acceptable.
  My target is 10 1080p streams per server.
  So my questions are
  1. If I use a quad motherboard and up the cpu count to 4 (6 or 8 cores) will I get 10 1080p streams ? Is there any theoretical max I can go with ffmpeg per machine ?
  2. Do cores matter more or does clock matter more ?
  3. Any suggestions in improvement with my options. I have tried ultrafast preset but the output quality is unacceptable.

  Thanks in advance

• lavc/flacdsp : optimise RVV vector type for lpc16

  14 mai 2024, par Rémi Denis-Courmont

  lavc/flacdsp : optimise RVV vector type for lpc16
  This calculates the optimal vector type value at run-time based on the
  
  hardware vector length and the FLAC LPC prediction order. In this
  
  particular case, the additional computation is easily amortised over
  
  the loop iterations :
  T-Head C908 :
  
      C     V before  V after
  
   1   48.0    214.7     95.2
  
   2   64.7    214.2     94.7
  
   3   79.7    213.5     94.5
  
   4   96.2    196.5     94.2 #
  
   5  111.0    195.7    118.5
  
   6  127.0    211.2    102.0
  
   7  143.7    194.2    101.5
  
   8  175.7    193.2    101.2 #
  
   9  176.2    224.2    126.0
  
  10  191.5    192.0    125.5
  
  11  224.5    191.2    124.7
  
  12  223.0    190.2    124.2
  
  13  239.2    189.5    123.7
  
  14  253.7    188.7    139.5
  
  15  286.2    188.0    122.7
  
  16  284.0    187.0    122.5 #
  
  17  300.2    186.5    186.5
  
  18  314.0    185.5    185.7
  
  19  329.7    184.7    185.0
  
  20  343.0    184.2    184.2
  
  21  358.7    199.2    183.7
  
  22  371.7    182.7    182.7
  
  23  387.5    181.7    182.0
  
  24  400.7    181.0    181.2
  
  25  431.5    180.2    196.5
  
  26  443.7    195.5    196.0
  
  27  459.0    178.7    196.2
  
  28  470.7    177.7    194.2
  
  29  470.0    177.0    193.5
  
  30  481.2    176.2    176.5
  
  31  496.2    175.5    175.7
  
  32  507.2    174.7    191.0 #
   # Power of two boundary.
  With 128-bit vectors, improvements are expected for the first two
  
  test cases only. For the other two, there is overhead but below noise.
  
  Improvements should be better observable with prediction order of 8
  
  and less, or on hardware with larger vector sizes.
  

  • [DH] libavcodec/riscv/flacdsp_init.c
  • [DH] libavcodec/riscv/flacdsp_rvv.S

• Make better marketing decisions with attribution modeling

  19 décembre 2017, par InnoCraft

  Do you suspect some traffic sources are not getting the rewards they deserve ? Do you want to know how much credit each of your marketing channel actually gets ?

  When you look at which referrers contribute the most to your goal conversions or purchases, Matomo (Piwik) shows you only the referrer of the last visit. However, in reality, a visitor often visits a website multiple times from different referrers before they convert a goal. Giving all credit to the referrer of the last visit ignores all other referrers that contributed to a conversion as well.

  You can now push your marketing analysis to the next level with attribution modeling and finally discover the true value of all your marketing channels. As a result, you will be able to shift your marketing efforts and spending accordingly to maximize your success and stop wasting resources. In marketing, studying this data is called attribution modeling.

  Get the true value of your referrers

  Attribution is a premium feature that you can easily purchase from the Matomo (Piwik) marketplace.

  Once installed, you will be able to :

  • identify valuable referrers that you did not see before
  • invest in potential new partners
  • attribute a new level of conversion
  • make this work very easily by filling just a couple of form information

  Identify valuable referrers that you did not see before

  You probably have hundreds or even thousands of different sources listed within the referrer reports. We also guess that you have the feeling that it is always the same referrers which are credited of conversions.
  Guess what, those data are probably biased or at least are not telling you the whole story.
  Why ? Because by default, Matomo (Piwik) only attributes all credit to the last referrer.

  

  It is likely that many non credited sources played a role in the conversion process as well as people often visit your website several times before converting and they may come from different referrers.

  This is exactly where attribution modeling comes into play. With attribution modeling, you can decide which touchpoint you want to study. For example, you can choose to give credit to all the referrers a single visitor came from each time the user visits your website, and not only look at the last one. Without this feature, chances are, that you have spent too much money and / or efforts on the wrong referrer channels in the past because many referrers that contributed to conversions were ignored. Based on the insights you get by applying different attribution models, you can make better decisions on where to shift your marketing spending and efforts.

  Invest in potential new partners

  Once you apply different attribution models, you will find out that you need to consider a new list of referrers which you before either over- or under-estimated in terms of how much they contributed to your conversions. You probably did not identify those sources before because Matomo (Piwik) shows only the last referrer before a conversion. But you can now also look at what these newly discovered referrers are saying about your company, looking for any advertising programs they may offer, getting in contact with the owner of the website, and more.

  Apply up to 6 different attribution models

  By default, Matomo (Piwik) is attributing the conversion to the last referrer only. With attribution modeling you can analyze 6 different models :

  • Last Interaction : the conversion is attributed to the last referrer, even if it is a direct access.
  • Last Non-Direct : the conversion is attributed to the last referrer, but not in the case of a direct access.
  • First Interaction : the conversion is attributed to the first referrer which brought you the visit.
  • Linear : whatever the number of referrers which brought you the conversion, they will all get the same value.
  • Position Based : first and last referrer will be attributed 40% each the conversion value, the remaining 60% is divided between the rest of the referrers.
  • Time Decay : this attribution model means that the closer to the date of the conversion is, the more your last referrers will get credit.

  Those attribution models will enable you to analyze all your referrers deeply and increase your conversions.

  Let’s look at an example where we are comparing two models : “last interaction” and “first interaction”. Our goal is to identify whether some referrers that we are currently considering as less important, are finally playing a serious role in the total amount of conversions :

  

  Comparing Last Interaction model to First Interaction model

  Here it is interesting to observe that the website www.hongkiat.com is bringing almost 90% conversion more with the first interaction model rather than the last one.

  As a result we can look at this website and take the following actions :

  • have a look at the message on this website
  • look at opportunities to change the message
  • look at opportunities to display extra marketing messages
  • get in contact with the owner to identify any other communication opportunities

  The Multi Channel Attribution report

  Attribution modeling in Matomo (Piwik) does not require you to add any tracking code. The only thing you need is to install the plugin and let the magic happen.
  Simple as pie is the word you should keep in mind for this feature. Once installed, you will find the report within the goal section, just above the goals you created :

  

  The Multi Attribution menu

  There you can select the attribution model you would like to apply or compare.

  Attribution modeling is not just about playing with a new report. It is above all an opportunity to increase the number of conversions by identifying referrers that you may have not recognized as valuable in the past. To grow your business, it is crucial to identify the most (and least) successful channels correctly so you can spend your time and money wisely.

  Learn more about Multi Channel Conversion Attribution

  The post Make better marketing decisions with attribution modeling appeared first on Analytics Platform - Matomo.