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• Sorting Arrays by More the One Value, and Prioritizing the Sort based on Column data.

  - by Mark Tomlin

  I'm looking for a way to sort an array, based on the information in each row, based on the information in certain cells, that I'll call columns. Each row has columns that must be sorted based on the priority of: timetime, lapcount & timestamp. Each column cotains this information: split1, split2, split3, laptime, lapcount, timestamp. laptime if in hundredths of a second. (1:23.45 or 1 Minute, 23 Seconds & 45 Hundredths is 8345.) Lapcount is a simple unsigned tiny int, or unsigned char. timestamp is unix epoch. The lowest laptime should be at the get a better standing in this sort. Should two peoples laptimes equal, then timestamp will be used to give the better standing in this sort. Should two peoples timestamp equal, then the person with less of a lapcount get's the better standing in this sort. By better standing, I mean closer to the top of the array, closer to the index of zero where it a numerical array. I think the array sorting functions built into php can do this with a callback, I was wondering what the best approch was for a weighted sort like this would be.

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• How to recalculate all-pairs shorthest paths on-line if nodes are getting removed?

  - by Pavel Shved

  Latest news about underground bombing made me curious about the following problem. Assume we have a weighted undirected graph, nodes of which are sometimes removed. The problem is to re-calculate shortest paths between all pairs of nodes fast after such removals. With a simple modification of Floyd-Warshall algorithm we can calculate shortest paths between all pairs. These paths may be stored in a table, where shortest[i][j] contains the index of the next node on the shortest path between i and j (or NULL value if there's no path). The algorithm requires O(n³) time to build the table, and eacho query shortest(i,j) takes O(1). Unfortunately, we should re-run this algorithm after each removal. The other alternative is to run graph search on each query. This way each removal takes zero time to update an auxiliary structure (because there's none), but each query takes O(E) time. What algorithm can be used to "balance" the query and update time for all-pairs shortest-paths problem when nodes of the graph are being removed?

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• Algorithm for finding the best routes for food distribution in game

  - by Tautrimas

  Hello, I'm designing a city building game and got into a problem. Imagine Sierra's Caesar III game mechanics: you have many city districts with one market each. There are several granaries over the distance connected with a directed weighted graph. The difference: people (here cars) are units that form traffic jams (here goes the graph weights). Note: in Ceasar game series, people harvested food and stockpiled it in several big granaries, whereas many markets (small shops) took food from the granaries and delivered it to the citizens. The task: tell each district where they should be getting their food from while taking least time and minimizing congestions on the city's roads. Map example Sample diagram Suppose that yellow districts need 7, 7 and 4 apples accordingly. Bluish granaries have 7 and 11 apples accordingly. Suppose edges weights to be proportional to their length. Then, the solution should be something like the gray numbers indicated on the edges. Eg, first district gets 4 apples from the 1st and 3 apples from the 2nd granary, while the last district gets 4 apples from only the 2nd granary. Here, vertical roads are first occupied to the max, and then the remaining workers are sent to the diagonal paths. Question What practical and very fast algorithm should I use? I was looking at some papers (Congestion Games: Optimization in Competition etc.) describing congestion games, but could not get the big picture. Any help is very appreciated! P. S. I can post very little links and no images because of new user restriction.

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• Examples of monoids/semigroups in programming

  - by jkff

  It is well-known that monoids are stunningly ubiquitous in programing. They are so ubiquitous and so useful that I, as a 'hobby project', am working on a system that is completely based on their properties (distributed data aggregation). To make the system useful I need useful monoids :) I already know of these: Numeric or matrix sum Numeric or matrix product Minimum or maximum under a total order with a top or bottom element (more generally, join or meet in a bounded lattice, or even more generally, product or coproduct in a category) Set union Map union where conflicting values are joined using a monoid Intersection of subsets of a finite set (or just set intersection if we speak about semigroups) Intersection of maps with a bounded key domain (same here) Merge of sorted sequences, perhaps with joining key-equal values in a different monoid/semigroup Bounded merge of sorted lists (same as above, but we take the top N of the result) Cartesian product of two monoids or semigroups List concatenation Endomorphism composition. Now, let us define a quasi-property of an operation as a property that holds up to an equivalence relation. For example, list concatenation is quasi-commutative if we consider lists of equal length or with identical contents up to permutation to be equivalent. Here are some quasi-monoids and quasi-commutative monoids and semigroups: Any (a+b = a or b, if we consider all elements of the carrier set to be equivalent) Any satisfying predicate (a+b = the one of a and b that is non-null and satisfies some predicate P, if none does then null; if we consider all elements satisfying P equivalent) Bounded mixture of random samples (xs+ys = a random sample of size N from the concatenation of xs and ys; if we consider any two samples with the same distribution as the whole dataset to be equivalent) Bounded mixture of weighted random samples Which others do exist?

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• Algorithm To Select Most Popular Places from Database

  - by Russell C.

  We have a website that contains a database of places. For each place our users are able to take one of the follow actions which we record: VIEW - View it's profile RATING - Rate it on a scale of 1-5 stars REVIEW - Review it COMPLETED - Mark that they've been there WISH LIST - Mark that they want to go there FAVORITE - Mark that it's one of their favorites In our database table of places each place contains a count of the number of times each action above was taken as well as the average rating given by users. views ratings avg_rating completed wishlist favorite What we want to be able to do is generate lists of the top places using the above information. Ideally, we would want to be able to generate this list using a relatively simple SQL query without needing to do any legwork to calculate additional fields or stack rank places against one another. That being said, since we only have about 50,000 places we could run a nightly cron job to calculate some fields such as rankings on different categories if it would make a meaningful difference in the overall results of our top places. I'd appreciate if you could make some suggestions on how we should think about bubbling the best places to the top, which criteria we should weight more heavily, and given that information - suggest what the MySQL query would need to look like in order to select the top 10 places. One thing to note is that at this time we are less concerned with the recency of a place being popular - meaning that looking at the aggregate information is fine and that more recent data doesn't need to be weighted more heavily. Thanks in advance for your help & advice!

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• mysql: Average over multiple columns in one row, ignoring nulls

  - by Sai Emrys

  I have a large table (of sites) with several numeric columns - say a through f. (These are site rankings from different organizations, like alexa, google, quantcast, etc. Each has a different range and format; they're straight dumps from the outside DBs.) For many of the records, one or more of these columns is null, because the outside DB doesn't have data for it. They all cover different subsets of my DB. I want column t to be their weighted average (each of a..f have static weights which I assign), ignoring null values (which can occur in any of them), except being null if they're all null. I would prefer to do this with a simple SQL calculation, rather than doing it in app code or using some huge ugly nested if block to handle every permutation of nulls. (Given that I have an increasing number of columns to average over as I add in more outside DB sources, this would be exponentially more ugly and bug-prone.) I'd use AVG but that's only for group by, and this is w/in one record. The data is semantically nullable, and I don't want to average in some "average" value in place of the nulls; I want to only be counting the columns for which data is there. Is there a good way to do this? Ideally, what I want is something like UPDATE sites SET t = AVG(a*a_weight,b*b_weight,...) where any null values are just ignored and no grouping is happening.

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• A Digg-like rotating homepage of popular content, how to include date as a factor?

  - by Ferdy

  I am building an advanced image sharing web application. As you may expect, users can upload images and others can comments on it, vote on it, and favorite it. These events will determine the popularity of the image, which I capture in a "karma" field. Now I want to create a Digg-like homepage system, showing the most popular images. It's easy, since I already have the weighted Karma score. I just sort on that descendingly to show the 20 most valued images. The part that is missing is time. I do not want extremely popular images to always be on the homepage. I guess an easy solution is to restrict the result set to the last 24 hours. However, I'm also thinking that in order to keep the image rotation occur throughout the day, time can be some kind of variable where its offset has an influence on the image's sorting. Specific questions: Would you recommend the easy scenario (just sort for best images within 24 hours) or the more sophisticated one (use datetime offset as part of the sorting)? If you advise the latter, any help on the mathematical solution to this? Would it be best to run a scheduled service to mark images for the homepage, or would you advise a direct query (I'm using MySQL) As an extra note, the homepage should support paging and on a quiet day should include entries of days before in order to make sure it is always "filled" I'm not asking the community to build this algorithm, just looking for some advise :)

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• Shortest acyclic path on directed cyclic graph with negative weights/cycles

  - by Janathan

  I have a directed graph which has cycles. All edges are weighted, and the weights can be negative. There can be negative cycles. I want to find a path from s to t, which minimizes the total weight on the path. Sure, it can go to negative infinity when negative cycles exist. But what if I disallow cycles in the path (not in the original graph)? That is, once the path leaves a node, it can not enter the node again. This surely avoids the negative infinity problem, but surprisingly no known algorithm is found by a search on Google. The closest is Floyd–Warshall algorithm, but it does not allow negative cycles. Thanks a lot in advance. Edit: I may have generalized my original problem too much. Indeed, I am given a cyclic directed graph with nonnegative edge weights. But in addition, each node has a positive reward too. I want to find a simple path which minimizes (sum of edge weights on the path) - (sum of node rewards covered by the path). This can be surely converted to the question that I posted, but some structure is lost. And some hint from submodular analysis suggests this motivating problem is not NP-hard. Thanks a lot

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• Is there a free, smale-scale, not web-based issue/bug tracking system?

  - by Doc Brown

  I know, there were posts before here on SO before concerning issue or bug tracking systems, like this one, but the given answers point either to commercial systems or web-based systems, which both seem to be oversized for our needs. What I am looking for is a non-commercial tool for a team of 3 to 4 developers, which can be used on an existing fileserver, without the need of installing additional server software like a C/S database or a web server. Some things I expect from such a system: allows to remember bugs (with a priority) and issues / ideas for new features (mostly without a priority) description of the issue, perhaps some additional remarks short info who entered the bug/issue entry one or more tags allowing us to group or filter the list Any suggestions? EDIT: I should have said that, but we are using MS Windows clients, Visual Studio development, Tortoise SVN (the latter works fine without a subversion server). And yes, I am strict on "no server software", since all server based solutions I have seen so far seem much to oversized/heavy weighted/too-much-effort-to-be-worth-it. In fact, if no one has a better idea, we are going to use a spreadsheet, but I can't believe there are no ready-made, light weight solutions.

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• In Python, how to make data members visible to subclasses if not known when initializing an object ?

  - by LB

  The title is a bit long, but it should be pretty straightforward for someone well-aware of python. I'm a python newbie. So, maybe i'm doing things in the wrong way. Suppose I have a class TreeNode class TreeNode(Node): def __init__(self, name, id): Node.__init__(self, name, id) self.children = [] and a subclass with a weight: class WeightedNode(TreeNode): def __init__(self,name, id): TreeNode.__init__(self, name, id) self.weight = 0 So far, i think I'm ok. Now, I want to add an object variable called father in TreeNode so that WeightedNode has also this member. The problem is that I don't know when initializing the object who is going to be the father. I set the father afterwards with this method in TreeNode : def set_father(self, father_node): self.father = father_node The problem is then when i'm trying to access self.father in Weighted: print 'Name %s Father %s '%(self.name, self.father.name) I obtain: AttributeError: WeightedNode instance has no attribute 'father' I thought that I could make father visible by doing something in TreeNode.__init__ but i wasn't able to find what. How can i do that ? Thanks.

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• R Random Data Sets within loops

  - by jugossery

  Here is what I want to do: I have a time series data frame with let us say 100 time-series of length 600 - each in one column of the data frame. I want to pick up 4 of the time-series randomly and then assign them random weights that sum up to one (ie 0.1, 0.5, 0.3, 0.1). Using those I want to compute the mean of the sum of the 4 weighted time series variables (e.g. convex combination). I want to do this let us say 100k times and store each result in the form ts1.name, ts2.name, ts3.name, ts4.name, weight1, weight2, weight3, weight4, mean so that I get a 9*100k df. I tried some things already but R is very bad with loops and I know vector oriented solutions are better because of R design. Thanks Here is what I did and I know it is horrible The df is in the form v1,v2,v2.....v100 1,5,6,.......9 2,4,6,.......10 3,5,8,.......6 2,2,8,.......2 etc e=NULL for (x in 1:100000) { s=sample(1:100,4)#pick 4 variables randomly a=sample(seq(0,1,0.01),1) b=sample(seq(0,1-a,0.01),1) c=sample(seq(0,(1-a-b),0.01),1) d=1-a-b-c e=c(a,b,c,d)#4 random weights average=mean(timeseries.df[,s]%*%t(e)) e=rbind(e,s,average)#in the end i get the 9*100k df } The procedure runs way to slow. EDIT: Thanks for the help i had,i am not used to think R and i am not very used to translate every problem into a matrix algebra equation which is what you need in R. Then the problem becomes a little bit complex if i want to calculate the standard deviation. i need the covariance matrix and i am not sure i can if/how i can pick random elements for each sample from the original timeseries.df covariance matrix then compute the sample variance (t(sampleweights)%*%sample_cov.mat%*%sampleweights) to get in the end the ts.weighted_standard_dev matrix Last question what is the best way to proceed if i want to bootstrap the original df x times and then apply the same computations to test the robustness of my datas thanks

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• “Being Agile” Means No Documentation, Right?

  - by jesschadwick

  Ask most software professionals what Agile is and they’ll probably start talking about flexibility and delivering what the customer wants.  Some may even mention the word “iterations”.  But inevitably, they’ll say at some point that it means less or even no documentation.  After all, doesn’t creating, updating, and circulating painstakingly comprehensive documentation that everyone and their mother have officially signed off on go against the very core of Agile?  Of course it does!  But really, they’re missing the point! Read The Agile Manifesto. (No, seriously - read it now. It’s short. I’ll wait.)  It’s essentially a list of values.  More specifically, it’s a right-side/left-side weighted list of values:  “Value this over that”. Many people seem to get the impression that this is really a “good vs. bad” list and that those values on the right side are evil and should essentially be tossed on the floor.  This leads to the conclusion that in order to be Agile we must throw away our fancy expensive tools, document as little as possible, and scoff at the idea of a project plan.  This conclusion is quite convenient because it essentially means “less work, more productivity!” (particularly in regards to the documentation and project planning).  I couldn’t disagree with this conclusion more. My interpretation of the Manifesto targets “over” as the operative word.  It’s not just a list of right vs. wrong or good vs. bad.  It’s a list of priorities.  In other words, none of the concepts on the list should be removed from your development lifecycle – they are all important… just not equally important.  This is not a unique interpretation, in fact it says so right at the end of the manifesto! So, the next time your team sits down to tackle that big new project, don’t make the first order of business to outlaw all meetings, documentation, and project plans.  Instead, collaborate with both your team and the business members involved (you do have business members sitting in the room, directly involved in the project planning, right?) and determine the bare minimum that will allow all of you to work and communicate in the best way possible.  This often means that you can pick and choose which parts of the Agile methodologies and process work for your particular project and end up with an amalgamation of Waterfall, Agile, XP, SCRUM and whatever other methodologies the members of your team have been exposed to (my favorite is “SCRUMerfall”). The biggest implication of this is that there is no one way to implement Agile.  There is no checklist with which you can tick off boxes and confidently conclude that, “Yep, we’re Agile™!”  In fact, depending on your business and the members of your team, moving to Agile full-bore may actually be ill-advised.  Such a drastic change just ends up taking everyone out of their comfort zone which they inevitably fall back into by the end of the project.  This often results in frustration to the point that Agile is abandoned altogether because “we just need to ship something!”  Needless to say, this is far more devastating to a project. Instead, I offer this approach: keep it simple and take it slow.  If your business members or customers are only involved at the beginning phases and nowhere to be seen until the project is delivered, invite them to your daily meetings; encourage them to keep up to speed on what’s going on on a daily basis and provide feedback.  If your current process is heavy on the documentation, try to reduce it as opposed to eliminating it outright.  If you need a “TPS Change Request” signed in triplicate with a 5-day “cooling off period” before a change is implemented, try a simple bug tracking system!  Tighten the feedback loop! Finally, at the end of every “iteration” (whatever that means to you, as long as it’s relatively frequent), take as much time as you can spare (even if it’s an hour or so) and perform some kind of retrospective.  Learn from your mistakes.  Figure out what’s working for you and what’s not, then fix it.  Before you know it you’ve got a handful of iterations and/or projects under your belt and you sit down with your team to realize that, “Hey, this is working - we’re pretty Agile!”  After all, Agile is a Zen journey.  It’s a destination that you aim for, not force, and even if you never reach true “enlightenment” that doesn’t mean your team can’t be exponentially better off from merely taking the journey.

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• World Record Batch Rate on Oracle JD Edwards Consolidated Workload with SPARC T4-2

  - by Brian

  Oracle produced a World Record batch throughput for single system results on Oracle's JD Edwards EnterpriseOne Day-in-the-Life benchmark using Oracle's SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2. The workload includes both online and batch workload. The SPARC T4-2 server delivered a result of 8,000 online users while concurrently executing a mix of JD Edwards EnterpriseOne Long and Short batch processes at 95.5 UBEs/min (Universal Batch Engines per minute). In order to obtain this record benchmark result, the JD Edwards EnterpriseOne, Oracle WebLogic and Oracle Database 11g Release 2 servers were executed each in separate Oracle Solaris Containers which enabled optimal system resources distribution and performance together with scalable and manageable virtualization. One SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2 utilized only 55% of the available CPU power. The Oracle DB server in a Shared Server configuration allows for optimized CPU resource utilization and significant memory savings on the SPARC T4-2 server without sacrificing performance. This configuration with SPARC T4-2 server has achieved 33% more Users/core, 47% more UBEs/min and 78% more Users/rack unit than the IBM Power 770 server. The SPARC T4-2 server with 2 processors ran the JD Edwards "Day-in-the-Life" benchmark and supported 8,000 concurrent online users while concurrently executing mixed batch workloads at 95.5 UBEs per minute. The IBM Power 770 server with twice as many processors supported only 12,000 concurrent online users while concurrently executing mixed batch workloads at only 65 UBEs per minute. This benchmark demonstrates more than 2x cost savings by consolidating the complete solution in a single SPARC T4-2 server compared to earlier published results of 10,000 users and 67 UBEs per minute on two SPARC T4-2 and SPARC T4-1. The Oracle DB server used mirrored (RAID 1) volumes for the database providing high availability for the data without impacting performance. Performance Landscape JD Edwards EnterpriseOne Day in the Life (DIL) Benchmark Consolidated Online with Batch Workload System Rack Units BatchRate(UBEs/m) Online Users Users /Units Users /Core Version SPARC T4-2 (2 x SPARC T4, 2.85 GHz) 3 95.5 8,000 2,667 500 9.0.2 IBM Power 770 (4 x POWER7, 3.3 GHz, 32 cores) 8 65 12,000 1,500 375 9.0.2 Batch Rate (UBEs/m) — Batch transaction rate in UBEs per minute Configuration Summary Hardware Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 4 x 300 GB 10K RPM SAS internal disk 2 x 300 GB internal SSD 2 x Sun Storage F5100 Flash Arrays Software Configuration: Oracle Solaris 10 Oracle Solaris Containers JD Edwards EnterpriseOne 9.0.2 JD Edwards EnterpriseOne Tools (8.98.4.2) Oracle WebLogic Server 11g (10.3.4) Oracle HTTP Server 11g Oracle Database 11g Release 2 (11.2.0.1) Benchmark Description JD Edwards EnterpriseOne is an integrated applications suite of Enterprise Resource Planning (ERP) software. Oracle offers 70 JD Edwards EnterpriseOne application modules to support a diverse set of business operations. Oracle's Day in the Life (DIL) kit is a suite of scripts that exercises most common transactions of JD Edwards EnterpriseOne applications, including business processes such as payroll, sales order, purchase order, work order, and manufacturing processes, such as ship confirmation. These are labeled by industry acronyms such as SCM, CRM, HCM, SRM and FMS. The kit's scripts execute transactions typical of a mid-sized manufacturing company. The workload consists of online transactions and the UBE – Universal Business Engine workload of 61 short and 4 long UBEs. LoadRunner runs the DIL workload, collects the user’s transactions response times and reports the key metric of Combined Weighted Average Transaction Response time. The UBE processes workload runs from the JD Enterprise Application server. Oracle's UBE processes come as three flavors: Short UBEs < 1 minute engage in Business Report and Summary Analysis, Mid UBEs > 1 minute create a large report of Account, Balance, and Full Address, Long UBEs > 2 minutes simulate Payroll, Sales Order, night only jobs. The UBE workload generates large numbers of PDF files reports and log files. The UBE Queues are categorized as the QBATCHD, a single threaded queue for large and medium UBEs, and the QPROCESS queue for short UBEs run concurrently. Oracle's UBE process performance metric is Number of Maximum Concurrent UBE processes at transaction rate, UBEs/minute. Key Points and Best Practices Two JD Edwards EnterpriseOne Application Servers, two Oracle WebLogic Servers 11g Release 1 coupled with two Oracle Web Tier HTTP server instances and one Oracle Database 11g Release 2 database on a single SPARC T4-2 server were hosted in separate Oracle Solaris Containers bound to four processor sets to demonstrate consolidation of multiple applications, web servers and the database with best resource utilizations. Interrupt fencing was configured on all Oracle Solaris Containers to channel the interrupts to processors other than the processor sets used for the JD Edwards Application server, Oracle WebLogic servers and the database server. A Oracle WebLogic vertical cluster was configured on each WebServer Container with twelve managed instances each to load balance users' requests and to provide the infrastructure that enables scaling to high number of users with ease of deployment and high availability. The database log writer was run in the real time RT class and bound to a processor set. The database redo logs were configured on the raw disk partitions. The Oracle Solaris Container running the Enterprise Application server completed 61 Short UBEs, 4 Long UBEs concurrently as the mixed size batch workload. The mixed size UBEs ran concurrently from the Enterprise Application server with the 8,000 online users driven by the LoadRunner. See Also SPARC T4-2 Server oracle.com OTN JD Edwards EnterpriseOne oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Oracle Fusion Middleware oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 09/30/2012.

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• The Unspoken - The Why of GC Ergonomics

  - by jonthecollector

  Do you use GC ergonomics, -XX:+UseAdaptiveSizePolicy, with the UseParallelGC collector? The jist of GC ergonomics for that collector is that it tries to grow or shrink the heap to meet a specified goal. The goals that you can choose are maximum pause time and/or throughput. Don't get too excited there. I'm speaking about UseParallelGC (the throughput collector) so there are definite limits to what pause goals can be achieved. When you say out loud "I don't care about pause times, give me the best throughput I can get" and then say to yourself "Well, maybe 10 seconds really is too long", then think about a pause time goal. By default there is no pause time goal and the throughput goal is high (98% of the time doing application work and 2% of the time doing GC work). You can get more details on this in my very first blog. GC ergonomics The UseG1GC has its own version of GC ergonomics, but I'll be talking only about the UseParallelGC version. If you use this option and wanted to know what it (GC ergonomics) was thinking, try -XX:AdaptiveSizePolicyOutputInterval=1 This will print out information every i-th GC (above i is 1) about what the GC ergonomics to trying to do. For example, UseAdaptiveSizePolicy actions to meet *** throughput goal *** GC overhead (%) Young generation: 16.10 (attempted to grow) Tenured generation: 4.67 (attempted to grow) Tenuring threshold: (attempted to decrease to balance GC costs) = 1 GC ergonomics tries to meet (in order) Pause time goal Throughput goal Minimum footprint The first line says that it's trying to meet the throughput goal. UseAdaptiveSizePolicy actions to meet *** throughput goal *** This run has the default pause time goal (i.e., no pause time goal) so it is trying to reach a 98% throughput. The lines Young generation: 16.10 (attempted to grow) Tenured generation: 4.67 (attempted to grow) say that we're currently spending about 16% of the time doing young GC's and about 5% of the time doing full GC's. These percentages are a decaying, weighted average (earlier contributions to the average are given less weight). The source code is available as part of the OpenJDK so you can take a look at it if you want the exact definition. GC ergonomics is trying to increase the throughput by growing the heap (so says the "attempted to grow"). The last line Tenuring threshold: (attempted to decrease to balance GC costs) = 1 says that the ergonomics is trying to balance the GC times between young GC's and full GC's by decreasing the tenuring threshold. During a young collection the younger objects are copied to the survivor spaces while the older objects are copied to the tenured generation. Younger and older are defined by the tenuring threshold. If the tenuring threshold hold is 4, an object that has survived fewer than 4 young collections (and has remained in the young generation by being copied to the part of the young generation called a survivor space) it is younger and copied again to a survivor space. If it has survived 4 or more young collections, it is older and gets copied to the tenured generation. A lower tenuring threshold moves objects more eagerly to the tenured generation and, conversely a higher tenuring threshold keeps copying objects between survivor spaces longer. The tenuring threshold varies dynamically with the UseParallelGC collector. That is different than our other collectors which have a static tenuring threshold. GC ergonomics tries to balance the amount of work done by the young GC's and the full GC's by varying the tenuring threshold. Want more work done in the young GC's? Keep objects longer in the survivor spaces by increasing the tenuring threshold. This is an example of the output when GC ergonomics is trying to achieve a pause time goal UseAdaptiveSizePolicy actions to meet *** pause time goal *** GC overhead (%) Young generation: 20.74 (no change) Tenured generation: 31.70 (attempted to shrink) The pause goal was set at 50 millisecs and the last GC was 0.415: [Full GC (Ergonomics) [PSYoungGen: 2048K-0K(26624K)] [ParOldGen: 26095K-9711K(28992K)] 28143K-9711K(55616K), [Metaspace: 1719K-1719K(2473K/6528K)], 0.0758940 secs] [Times: user=0.28 sys=0.00, real=0.08 secs] The full collection took about 76 millisecs so GC ergonomics wants to shrink the tenured generation to reduce that pause time. The previous young GC was 0.346: [GC (Allocation Failure) [PSYoungGen: 26624K-2048K(26624K)] 40547K-22223K(56768K), 0.0136501 secs] [Times: user=0.06 sys=0.00, real=0.02 secs] so the pause time there was about 14 millisecs so no changes are needed. If trying to meet a pause time goal, the generations are typically shrunk. With a pause time goal in play, watch the GC overhead numbers and you will usually see the cost of setting a pause time goal (i.e., throughput goes down). If the pause goal is too low, you won't achieve your pause time goal and you will spend all your time doing GC. GC ergonomics is meant to be simple because it is meant to be used by anyone. It was not meant to be mysterious and so this output was added. If you don't like what GC ergonomics is doing, you can turn it off with -XX:-UseAdaptiveSizePolicy, but be pre-warned that you have to manage the size of the generations explicitly. If UseAdaptiveSizePolicy is turned off, the heap does not grow. The size of the heap (and the generations) at the start of execution is always the size of the heap. I don't like that and tried to fix it once (with some help from an OpenJDK contributor) but it unfortunately never made it out the door. I still have hope though. Just a side note. With the default throughput goal of 98% the heap often grows to it's maximum value and stays there. Definitely reduce the throughput goal if footprint is important. Start with -XX:GCTimeRatio=4 for a more modest throughput goal (%20 of the time spent in GC). A higher value means a smaller amount of time in GC (as the throughput goal).

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• SPARC T4-4 Delivers World Record First Result on PeopleSoft Combined Benchmark

  - by Brian

  Oracle's SPARC T4-4 servers running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved World Record 18,000 concurrent users while executing a PeopleSoft Payroll batch job of 500,000 employees in 43.32 minutes and maintaining online users response time at < 2 seconds. This world record is the first to run online and batch workloads concurrently. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 35% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. This is the first three tier mixed workload (online and batch) PeopleSoft benchmark also processing PeopleSoft payroll batch workload. Performance Landscape PeopleSoft HR Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-2 (db) 18,000 0.944 0.503 43.32 64 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory 5 x 300 GB SAS internal disks 1 x 100 GB and 2 x 300 GB internal SSDs 2 x 10 Gbe HBA Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory 3 x 300 GB SAS internal disks Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 2 x 300 GB SAS internal disks 1 x 100 GB internal SSD Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two Oracle PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Management oracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Oracle's PeopleSoft HR and Payroll combined benchmark, www.oracle.com/us/solutions/benchmark/apps-benchmark/peoplesoft-167486.html, results 09/30/2012.

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• Streaming desktop with avconv - severe sound issues

  - by Tommy Brunn

  I'm trying to do some live streaming in Ubuntu 12.10, but I'm having some problems with audio. More specifically, the quality is complete garbage and it's at least 10 seconds out of sync with the video. I'm using an excellent guide found here to set up my loopback devices so that I can combine the desktop audio with the microphone input. It seems to work, as I'm able to stream both audio and video to Twitch.tv. But, as I said, the audio quality is terrible. The microphone audio is very, very low, but if I increase it, I get a horrible garbled sound that is absolutely unbearable. Nothing like that is present during VoIP calls or when recording sound alone with the sound recorder, so it's not an issue with the microphone itself. The entire audio stream is also delayed about 10-15 seconds compared to the video stream. I put together an imgur album of my settings. Here is some example output from when I'm streaming: avconv version 0.8.4-6:0.8.4-0ubuntu0.12.10.1, Copyright (c) 2000-2012 the Libav developers built on Nov 6 2012 16:51:11 with gcc 4.7.2 [x11grab @ 0x162fd80] device: :0.0+570,262 -> display: :0.0 x: 570 y: 262 width: 1280 height: 720 [x11grab @ 0x162fd80] shared memory extension found [x11grab @ 0x162fd80] Estimating duration from bitrate, this may be inaccurate Input #0, x11grab, from ':0.0+570,262': Duration: N/A, start: 1353181686.735113, bitrate: 884736 kb/s Stream #0.0: Video: rawvideo, bgra, 1280x720, 884736 kb/s, 30 tbr, 1000k tbn, 30 tbc [alsa @ 0x163fce0] capture with some ALSA plugins, especially dsnoop, may hang. [alsa @ 0x163fce0] Estimating duration from bitrate, this may be inaccurate Input #1, alsa, from 'pulse': Duration: N/A, start: 1353181686.773841, bitrate: N/A Stream #1.0: Audio: pcm_s16le, 48000 Hz, 2 channels, s16, 1536 kb/s Incompatible pixel format 'bgra' for codec 'libx264', auto-selecting format 'yuv420p' [buffer @ 0x1641ec0] w:1280 h:720 pixfmt:bgra [scale @ 0x1642480] w:1280 h:720 fmt:bgra -> w:852 h:480 fmt:yuv420p flags:0x4 [libx264 @ 0x165ae80] VBV maxrate unspecified, assuming CBR [libx264 @ 0x165ae80] using cpu capabilities: MMX2 SSE2Fast SSSE3 FastShuffle SSE4.2 [libx264 @ 0x165ae80] profile Main, level 3.1 [libx264 @ 0x165ae80] 264 - core 123 r2189 35cf912 - H.264/MPEG-4 AVC codec - Copyleft 2003-2012 - http://www.videolan.org/x264.html - options: cabac=1 ref=2 deblock=1:0:0 analyse=0x1:0x111 me=hex subme=6 psy=1 psy_rd=1.00:0.00 mixed_ref=0 me_range=16 chroma_me=1 trellis=1 8x8dct=0 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=4 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=0 b_adapt=1 b_bias=0 direct=1 weightb=0 open_gop=1 weightp=1 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=30 rc=cbr mbtree=1 bitrate=712 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 vbv_maxrate=712 vbv_bufsize=512 nal_hrd=none ip_ratio=1.25 aq=1:1.00 Output #0, flv, to 'rtmp://live.justin.tv/app/live_23011330_Pt1plSRM0z5WVNJ0QmCHvTPmpUnfC4': Metadata: encoder : Lavf53.21.0 Stream #0.0: Video: libx264, yuv420p, 852x480, q=-1--1, 712 kb/s, 1k tbn, 30 tbc Stream #0.1: Audio: libmp3lame, 44100 Hz, 2 channels, s16, 712 kb/s Stream mapping: Stream #0:0 -> #0:0 (rawvideo -> libx264) Stream #1:0 -> #0:1 (pcm_s16le -> libmp3lame) Press ctrl-c to stop encoding frame= 17 fps= 0 q=0.0 size= 0kB time=10000000000.00 bitrate= 0.0kbitframe= 32 fps= 31 q=0.0 size= 0kB time=10000000000.00 bitrate= 0.0kbitframe= 40 fps= 23 q=29.0 size= 44kB time=0.03 bitrate=13786.2kbits/s dup=frame= 47 fps= 21 q=31.0 size= 93kB time=2.73 bitrate= 277.7kbits/s dup=0frame= 62 fps= 23 q=29.0 size= 160kB time=3.23 bitrate= 406.2kbits/s dup=0frame= 77 fps= 24 q=23.0 size= 209kB time=3.71 bitrate= 462.5kbits/s dup=0frame= 92 fps= 25 q=20.0 size= 267kB time=4.91 bitrate= 445.2kbits/s dup=0frame= 107 fps= 25 q=20.0 size= 318kB time=5.41 bitrate= 482.1kbits/s dup=0frame= 123 fps= 26 q=18.0 size= 368kB time=5.96 bitrate= 505.7kbits/s dup=0frame= 139 fps= 26 q=16.0 size= 419kB time=6.48 bitrate= 529.7kbits/s dup=0frame= 155 fps= 27 q=15.0 size= 473kB time=7.00 bitrate= 553.6kbits/s dup=0frame= 170 fps= 27 q=14.0 size= 525kB time=7.52 bitrate= 571.7kbits/s dup=0 frame= 180 fps= 25 q=-1.0 Lsize= 652kB time=7.97 bitrate= 670.0kbits/s dup=0 drop=32 //Here I stop the streaming video:531kB audio:112kB global headers:0kB muxing overhead 1.345945% [libx264 @ 0x165ae80] frame I:1 Avg QP:30.43 size: 39748 [libx264 @ 0x165ae80] frame P:45 Avg QP:11.37 size: 11110 [libx264 @ 0x165ae80] frame B:134 Avg QP:15.93 size: 27 [libx264 @ 0x165ae80] consecutive B-frames: 0.6% 0.0% 1.7% 97.8% [libx264 @ 0x165ae80] mb I I16..4: 7.3% 0.0% 92.7% [libx264 @ 0x165ae80] mb P I16..4: 0.1% 0.0% 0.1% P16..4: 49.1% 1.2% 2.1% 0.0% 0.0% skip:47.4% [libx264 @ 0x165ae80] mb B I16..4: 0.0% 0.0% 0.0% B16..8: 0.1% 0.0% 0.0% direct: 0.0% skip:99.9% L0:42.5% L1:56.9% BI: 0.6% [libx264 @ 0x165ae80] coded y,uvDC,uvAC intra: 82.3% 87.4% 71.9% inter: 7.1% 8.4% 7.0% [libx264 @ 0x165ae80] i16 v,h,dc,p: 27% 29% 16% 28% [libx264 @ 0x165ae80] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 21% 14% 8% 8% 8% 7% 5% 7% [libx264 @ 0x165ae80] i8c dc,h,v,p: 47% 22% 20% 11% [libx264 @ 0x165ae80] Weighted P-Frames: Y:0.0% UV:0.0% [libx264 @ 0x165ae80] ref P L0: 96.4% 3.6% [libx264 @ 0x165ae80] kb/s:474.19 Received signal 2: terminating. Any ideas on how I can resolve this? The video delay is perfectly acceptable, so I wouldn't think that it's a network issue that's causing the delay in the audio. Any help would be appreciated.

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• ffmpeg - How to determine if -movflags faststart is enabled? PHP

  - by IIIOXIII

  While I am able to encode an mp4 file which I can plan on my local windows machine, I am having trouble encoding files to mp4 which are readable when streaming by safari, etc. After a bit of reading, I believe my issue is that I must move the metadata from the end of the file to the beginning in order for the converted mp4 files to be streamable. To that end, I am trying to find out if the build of ffmpeg that I am currently using is able to use the -movflags faststart option through php - as my current outputted mp4 files are not working when streamed online. This is the way I am now echoing the -help, -formats, -codecs, but I am not seeing anything about -movflags faststart in any of the lists: exec($ffmpegPath." -help", $codecArr); for($ii=0;$ii<count($codecArr);$ii++){ echo $codecArr[$ii].'</br>'; } Is there a similar method of determining if -movflags fastart is available to my ffmpeg build? Any other way? Should it be listed with any of the previously suggested commands? -help/-formats? Can someone that knows it is enabled in their version of ffmpeg check to see if it is listed under -help or -formats, etc.? TIA. EDIT: COMPLETE CONSOLE OUTPUT FOR BOTH THE CONVERSION COMMAND AND -MOVFLAGS COMMAND BELOW: COMMAND: ffmpeg_new -i C:\vidtests\Wildlife.wmv -s 640x480 C:\vidtests\Wildlife.mp4 OUTPUT: ffmpeg version N-54207-ge59fb3f Copyright (c) 2000-2013 the FFmpeg developers built on Jun 25 2013 21:55:00 with gcc 4.7.3 (GCC) configuration: --enable-gpl --enable-version3 --disable-w32threads --enable-av isynth --enable-bzlib --enable-fontconfig --enable-frei0r --enable-gnutls --enab le-iconv --enable-libass --enable-libbluray --enable-libcaca --enable-libfreetyp e --enable-libgsm --enable-libilbc --enable-libmodplug --enable-libmp3lame --ena ble-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-l ibopus --enable-librtmp --enable-libschroedinger --enable-libsoxr --enable-libsp eex --enable-libtheora --enable-libtwolame --enable-libvo-aacenc --enable-libvo- amrwbenc --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libxavs -- enable-libxvid --enable-zlib libavutil 52. 37.101 / 52. 37.101 libavcodec 55. 17.100 / 55. 17.100 libavformat 55. 10.100 / 55. 10.100 libavdevice 55. 2.100 / 55. 2.100 libavfilter 3. 77.101 / 3. 77.101 libswscale 2. 3.100 / 2. 3.100 libswresample 0. 17.102 / 0. 17.102 libpostproc 52. 3.100 / 52. 3.100 [asf @ 00000000002ed760] Stream #0: not enough frames to estimate rate; consider increasing probesize Guessed Channel Layout for Input Stream #0.0 : stereo Input #0, asf, from 'C:\vidtests\Wildlife.wmv' : Metadata: SfOriginalFPS : 299700 WMFSDKVersion : 11.0.6001.7000 WMFSDKNeeded : 0.0.0.0000 comment : Footage: Small World Productions, Inc; Tourism New Zealand | Producer: Gary F. Spradling | Music: Steve Ball title : Wildlife in HD copyright : -¬ 2008 Microsoft Corporation IsVBR : 0 DeviceConformanceTemplate: AP@L3 Duration: 00:00:30.09, start: 0.000000, bitrate: 6977 kb/s Stream #0:0(eng): Audio: wmav2 (a[1][0][0] / 0x0161), 44100 Hz, stereo, fltp , 192 kb/s Stream #0:1(eng): Video: vc1 (Advanced) (WVC1 / 0x31435657), yuv420p, 1280x7 20, 5942 kb/s, 29.97 tbr, 1k tbn, 1k tbc [libx264 @ 00000000002e6980] using cpu capabilities: MMX2 SSE2Fast SSSE3 Cache64 [libx264 @ 00000000002e6980] profile High, level 3.0 [libx264 @ 00000000002e6980] 264 - core 133 r2334 a3ac64b - H.264/MPEG-4 AVC cod ec - Copyleft 2003-2013 - http://www.videolan.org/x264.html - options: cabac=1 r ef=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed _ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pski p=1 chroma_qp_offset=-2 threads=3 lookahead_threads=1 sliced_threads=0 nr=0 deci mate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_ adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=2 5 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.6 0 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00 Output #0, mp4, to 'C:\vidtests\Wildlife.mp4': Metadata: SfOriginalFPS : 299700 WMFSDKVersion : 11.0.6001.7000 WMFSDKNeeded : 0.0.0.0000 comment : Footage: Small World Productions, Inc; Tourism New Zealand | Producer: Gary F. Spradling | Music: Steve Ball title : Wildlife in HD copyright : -¬ 2008 Microsoft Corporation IsVBR : 0 DeviceConformanceTemplate: AP@L3 encoder : Lavf55.10.100 Stream #0:0(eng): Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 6 40x480, q=-1--1, 30k tbn, 29.97 tbc Stream #0:1(eng): Audio: aac (libvo_aacenc) ([64][0][0][0] / 0x0040), 44100 Hz, stereo, s16, 128 kb/s Stream mapping: Stream #0:1 -> #0:0 (vc1 -> libx264) Stream #0:0 -> #0:1 (wmav2 -> libvo_aacenc) Press [q] to stop, [?] for help frame= 53 fps= 49 q=29.0 size= 0kB time=00:00:00.13 bitrate= 2.9kbits/ frame= 63 fps= 40 q=29.0 size= 0kB time=00:00:00.46 bitrate= 0.8kbits/ frame= 74 fps= 35 q=29.0 size= 0kB time=00:00:00.83 bitrate= 0.5kbits/ frame= 85 fps= 32 q=29.0 size= 0kB time=00:00:01.20 bitrate= 0.3kbits/ frame= 95 fps= 30 q=29.0 size= 0kB time=00:00:01.53 bitrate= 0.3kbits/ frame= 107 fps= 28 q=29.0 size= 0kB time=00:00:01.93 bitrate= 0.2kbits/ Queue input is backward in time [mp4 @ 00000000003ef800] Non-monotonous DTS in output stream 0:1; previous: 7616 , current: 7063; changing to 7617. This may result in incorrect timestamps in th e output file. frame= 118 fps= 28 q=29.0 size= 113kB time=00:00:02.30 bitrate= 402.6kbits/ frame= 129 fps= 26 q=29.0 size= 219kB time=00:00:02.66 bitrate= 670.7kbits/ frame= 141 fps= 26 q=29.0 size= 264kB time=00:00:03.06 bitrate= 704.2kbits/ frame= 152 fps= 25 q=29.0 size= 328kB time=00:00:03.43 bitrate= 782.2kbits/ frame= 163 fps= 25 q=29.0 size= 431kB time=00:00:03.80 bitrate= 928.1kbits/ frame= 174 fps= 24 q=29.0 size= 568kB time=00:00:04.17 bitrate=1116.3kbits/ frame= 190 fps= 25 q=29.0 size= 781kB time=00:00:04.70 bitrate=1359.9kbits/ frame= 204 fps= 25 q=29.0 size= 1006kB time=00:00:05.17 bitrate=1593.1kbits/ frame= 218 fps= 25 q=29.0 size= 1058kB time=00:00:05.63 bitrate=1536.8kbits/ frame= 229 fps= 25 q=29.0 size= 1093kB time=00:00:06.00 bitrate=1490.9kbits/ frame= 239 fps= 24 q=29.0 size= 1118kB time=00:00:06.33 bitrate=1444.4kbits/ frame= 251 fps= 24 q=29.0 size= 1150kB time=00:00:06.74 bitrate=1397.9kbits/ frame= 265 fps= 24 q=29.0 size= 1234kB time=00:00:07.20 bitrate=1402.3kbits/ frame= 278 fps= 25 q=29.0 size= 1332kB time=00:00:07.64 bitrate=1428.3kbits/ frame= 294 fps= 25 q=29.0 size= 1403kB time=00:00:08.17 bitrate=1405.7kbits/ frame= 308 fps= 25 q=29.0 size= 1547kB time=00:00:08.64 bitrate=1466.4kbits/ frame= 323 fps= 25 q=29.0 size= 1595kB time=00:00:09.14 bitrate=1429.5kbits/ frame= 337 fps= 25 q=29.0 size= 1702kB time=00:00:09.60 bitrate=1450.7kbits/ frame= 351 fps= 25 q=29.0 size= 1755kB time=00:00:10.07 bitrate=1427.1kbits/ frame= 365 fps= 25 q=29.0 size= 1820kB time=00:00:10.54 bitrate=1414.1kbits/ frame= 381 fps= 25 q=29.0 size= 1852kB time=00:00:11.07 bitrate=1369.6kbits/ frame= 396 fps= 26 q=29.0 size= 1893kB time=00:00:11.57 bitrate=1339.5kbits/ frame= 409 fps= 26 q=29.0 size= 1923kB time=00:00:12.01 bitrate=1311.8kbits/ frame= 421 fps= 25 q=29.0 size= 1967kB time=00:00:12.41 bitrate=1298.3kbits/ frame= 434 fps= 25 q=29.0 size= 1998kB time=00:00:12.84 bitrate=1274.0kbits/ frame= 445 fps= 25 q=29.0 size= 2018kB time=00:00:13.21 bitrate=1251.3kbits/ frame= 458 fps= 25 q=29.0 size= 2048kB time=00:00:13.64 bitrate=1229.5kbits/ frame= 471 fps= 25 q=29.0 size= 2067kB time=00:00:14.08 bitrate=1202.3kbits/ frame= 484 fps= 25 q=29.0 size= 2189kB time=00:00:14.51 bitrate=1235.5kbits/ frame= 497 fps= 25 q=29.0 size= 2260kB time=00:00:14.94 bitrate=1238.3kbits/ frame= 509 fps= 25 q=29.0 size= 2311kB time=00:00:15.34 bitrate=1233.3kbits/ frame= 523 fps= 25 q=29.0 size= 2429kB time=00:00:15.81 bitrate=1258.1kbits/ frame= 535 fps= 25 q=29.0 size= 2541kB time=00:00:16.21 bitrate=1283.5kbits/ frame= 548 fps= 25 q=29.0 size= 2718kB time=00:00:16.64 bitrate=1337.5kbits/ frame= 560 fps= 25 q=29.0 size= 2845kB time=00:00:17.05 bitrate=1367.1kbits/ frame= 571 fps= 25 q=29.0 size= 2965kB time=00:00:17.41 bitrate=1394.6kbits/ frame= 580 fps= 25 q=29.0 size= 3025kB time=00:00:17.71 bitrate=1398.7kbits/ frame= 588 fps= 25 q=29.0 size= 3098kB time=00:00:17.98 bitrate=1411.1kbits/ frame= 597 fps= 25 q=29.0 size= 3183kB time=00:00:18.28 bitrate=1426.1kbits/ frame= 606 fps= 24 q=29.0 size= 3279kB time=00:00:18.58 bitrate=1445.2kbits/ frame= 616 fps= 24 q=29.0 size= 3441kB time=00:00:18.91 bitrate=1489.9kbits/ frame= 626 fps= 24 q=29.0 size= 3650kB time=00:00:19.25 bitrate=1553.0kbits/ frame= 638 fps= 24 q=29.0 size= 3826kB time=00:00:19.65 bitrate=1594.7kbits/ frame= 649 fps= 24 q=29.0 size= 3950kB time=00:00:20.02 bitrate=1616.3kbits/ frame= 660 fps= 24 q=29.0 size= 4067kB time=00:00:20.38 bitrate=1634.1kbits/ frame= 669 fps= 24 q=29.0 size= 4121kB time=00:00:20.68 bitrate=1631.8kbits/ frame= 682 fps= 24 q=29.0 size= 4274kB time=00:00:21.12 bitrate=1657.9kbits/ frame= 696 fps= 24 q=29.0 size= 4446kB time=00:00:21.58 bitrate=1687.1kbits/ frame= 709 fps= 24 q=29.0 size= 4590kB time=00:00:22.02 bitrate=1707.3kbits/ frame= 719 fps= 24 q=29.0 size= 4772kB time=00:00:22.35 bitrate=1748.5kbits/ frame= 732 fps= 24 q=29.0 size= 4852kB time=00:00:22.78 bitrate=1744.3kbits/ frame= 744 fps= 24 q=29.0 size= 4973kB time=00:00:23.18 bitrate=1756.9kbits/ frame= 756 fps= 24 q=29.0 size= 5099kB time=00:00:23.59 bitrate=1770.8kbits/ frame= 768 fps= 24 q=29.0 size= 5149kB time=00:00:23.99 bitrate=1758.4kbits/ frame= 780 fps= 24 q=29.0 size= 5227kB time=00:00:24.39 bitrate=1755.7kbits/ frame= 797 fps= 24 q=29.0 size= 5377kB time=00:00:24.95 bitrate=1765.0kbits/ frame= 813 fps= 24 q=29.0 size= 5507kB time=00:00:25.49 bitrate=1769.5kbits/ frame= 828 fps= 24 q=29.0 size= 5634kB time=00:00:25.99 bitrate=1775.5kbits/ frame= 843 fps= 24 q=29.0 size= 5701kB time=00:00:26.49 bitrate=1762.9kbits/ frame= 859 fps= 24 q=29.0 size= 5830kB time=00:00:27.02 bitrate=1767.0kbits/ frame= 872 fps= 24 q=29.0 size= 5926kB time=00:00:27.46 bitrate=1767.7kbits/ frame= 888 fps= 24 q=29.0 size= 6014kB time=00:00:27.99 bitrate=1759.7kbits/ frame= 900 fps= 24 q=29.0 size= 6332kB time=00:00:28.39 bitrate=1826.9kbits/ frame= 901 fps= 24 q=-1.0 Lsize= 6717kB time=00:00:30.10 bitrate=1828.0kbits /s video:6211kB audio:472kB subtitle:0 global headers:0kB muxing overhead 0.513217% [libx264 @ 00000000002e6980] frame I:8 Avg QP:21.77 size: 39744 [libx264 @ 00000000002e6980] frame P:433 Avg QP:25.69 size: 11490 [libx264 @ 00000000002e6980] frame B:460 Avg QP:29.25 size: 2319 [libx264 @ 00000000002e6980] consecutive B-frames: 5.4% 78.6% 2.7% 13.3% [libx264 @ 00000000002e6980] mb I I16..4: 21.8% 48.8% 29.5% [libx264 @ 00000000002e6980] mb P I16..4: 0.7% 4.0% 1.3% P16..4: 37.1% 22.2 % 15.5% 0.0% 0.0% skip:19.2% [libx264 @ 00000000002e6980] mb B I16..4: 0.1% 0.5% 0.2% B16..8: 43.5% 7.0 % 2.1% direct: 2.2% skip:44.5% L0:36.4% L1:52.7% BI:10.9% [libx264 @ 00000000002e6980] 8x8 transform intra:62.8% inter:56.2% [libx264 @ 00000000002e6980] coded y,uvDC,uvAC intra: 74.2% 78.8% 44.0% inter: 2 3.6% 14.5% 1.0% [libx264 @ 00000000002e6980] i16 v,h,dc,p: 48% 24% 9% 20% [libx264 @ 00000000002e6980] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 16% 17% 15% 7% 8% 11% 8% 10% 8% [libx264 @ 00000000002e6980] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19% 17% 15% 7% 10% 11% 8% 7% 7% [libx264 @ 00000000002e6980] i8c dc,h,v,p: 53% 21% 18% 7% [libx264 @ 00000000002e6980] Weighted P-Frames: Y:0.7% UV:0.0% [libx264 @ 00000000002e6980] ref P L0: 62.4% 19.0% 12.0% 6.6% 0.0% [libx264 @ 00000000002e6980] ref B L0: 90.5% 8.9% 0.7% [libx264 @ 00000000002e6980] ref B L1: 97.9% 2.1% [libx264 @ 00000000002e6980] kb/s:1692.37 AND THE –MOVFLAGS COMMAND: C:\XSITE\SITE>ffmpeg_new -i C:\vidtests\Wildlife.mp4 -movflags faststart C:\vidtests\Wildlife_fs.mp4 AND THE –MOVFLAGS OUTPUT ffmpeg version N-54207-ge59fb3f Copyright (c) 2000-2013 the FFmpeg developers built on Jun 25 2013 21:55:00 with gcc 4.7.3 (GCC) configuration: --enable-gpl --enable-version3 --disable-w32threads --enable-av isynth --enable-bzlib --enable-fontconfig --enable-frei0r --enable-gnutls --enab le-iconv --enable-libass --enable-libbluray --enable-libcaca --enable-libfreetyp e --enable-libgsm --enable-libilbc --enable-libmodplug --enable-libmp3lame --ena ble-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-l ibopus --enable-librtmp --enable-libschroedinger --enable-libsoxr --enable-libsp eex --enable-libtheora --enable-libtwolame --enable-libvo-aacenc --enable-libvo- amrwbenc --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libxavs -- enable-libxvid --enable-zlib libavutil 52. 37.101 / 52. 37.101 libavcodec 55. 17.100 / 55. 17.100 libavformat 55. 10.100 / 55. 10.100 libavdevice 55. 2.100 / 55. 2.100 libavfilter 3. 77.101 / 3. 77.101 libswscale 2. 3.100 / 2. 3.100 libswresample 0. 17.102 / 0. 17.102 libpostproc 52. 3.100 / 52. 3.100 Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\vidtests\Wildlife.mp4': Metadata: major_brand : isom minor_version : 512 compatible_brands: isomiso2avc1mp41 title : Wildlife in HD encoder : Lavf55.10.100 comment : Footage: Small World Productions, Inc; Tourism New Zealand | Producer: Gary F. Spradling | Music: Steve Ball copyright : -¬ 2008 Microsoft Corporation Duration: 00:00:30.13, start: 0.036281, bitrate: 1826 kb/s Stream #0:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 640x480, 1692 kb/s, 29.97 fps, 29.97 tbr, 30k tbn, 59.94 tbc Metadata: handler_name : VideoHandler Stream #0:1(eng): Audio: aac (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 12 8 kb/s Metadata: handler_name : SoundHandler [libx264 @ 0000000004360620] using cpu capabilities: MMX2 SSE2Fast SSSE3 Cache64 [libx264 @ 0000000004360620] profile High, level 3.0 [libx264 @ 0000000004360620] 264 - core 133 r2334 a3ac64b - H.264/MPEG-4 AVC cod ec - Copyleft 2003-2013 - http://www.videolan.org/x264.html - options: cabac=1 r ef=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed _ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pski p=1 chroma_qp_offset=-2 threads=3 lookahead_threads=1 sliced_threads=0 nr=0 deci mate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_ adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=2 5 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.6 0 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00 Output #0, mp4, to 'C:\vidtests\Wildlife_fs.mp4': Metadata: major_brand : isom minor_version : 512 compatible_brands: isomiso2avc1mp41 title : Wildlife in HD copyright : -¬ 2008 Microsoft Corporation comment : Footage: Small World Productions, Inc; Tourism New Zealand | Producer: Gary F. Spradling | Music: Steve Ball encoder : Lavf55.10.100 Stream #0:0(eng): Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 6 40x480, q=-1--1, 30k tbn, 29.97 tbc Metadata: handler_name : VideoHandler Stream #0:1(eng): Audio: aac (libvo_aacenc) ([64][0][0][0] / 0x0040), 44100 Hz, stereo, s16, 128 kb/s Metadata: handler_name : SoundHandler Stream mapping: Stream #0:0 -> #0:0 (h264 -> libx264) Stream #0:1 -> #0:1 (aac -> libvo_aacenc) Press [q] to stop, [?] for help frame= 52 fps=0.0 q=29.0 size= 29kB time=00:00:01.76 bitrate= 133.9kbits/ frame= 63 fps= 60 q=29.0 size= 104kB time=00:00:02.14 bitrate= 397.2kbits/ frame= 74 fps= 47 q=29.0 size= 176kB time=00:00:02.51 bitrate= 573.2kbits/ frame= 87 fps= 41 q=29.0 size= 265kB time=00:00:02.93 bitrate= 741.2kbits/ frame= 101 fps= 37 q=29.0 size= 358kB time=00:00:03.39 bitrate= 862.8kbits/ frame= 113 fps= 34 q=29.0 size= 437kB time=00:00:03.79 bitrate= 943.7kbits/ frame= 125 fps= 33 q=29.0 size= 520kB time=00:00:04.20 bitrate=1012.2kbits/ frame= 138 fps= 32 q=29.0 size= 606kB time=00:00:04.64 bitrate=1069.8kbits/ frame= 151 fps= 31 q=29.0 size= 696kB time=00:00:05.06 bitrate=1124.3kbits/ frame= 163 fps= 30 q=29.0 size= 780kB time=00:00:05.47 bitrate=1166.4kbits/ frame= 176 fps= 30 q=29.0 size= 919kB time=00:00:05.90 bitrate=1273.9kbits/ frame= 196 fps= 31 q=29.0 size= 994kB time=00:00:06.57 bitrate=1237.4kbits/ frame= 213 fps= 31 q=29.0 size= 1097kB time=00:00:07.13 bitrate=1258.8kbits/ frame= 225 fps= 30 q=29.0 size= 1204kB time=00:00:07.53 bitrate=1309.8kbits/ frame= 236 fps= 30 q=29.0 size= 1323kB time=00:00:07.91 bitrate=1369.4kbits/ frame= 249 fps= 29 q=29.0 size= 1451kB time=00:00:08.34 bitrate=1424.6kbits/ frame= 263 fps= 29 q=29.0 size= 1574kB time=00:00:08.82 bitrate=1461.3kbits/ frame= 278 fps= 29 q=29.0 size= 1610kB time=00:00:09.30 bitrate=1416.9kbits/ frame= 296 fps= 30 q=29.0 size= 1655kB time=00:00:09.91 bitrate=1368.0kbits/ frame= 313 fps= 30 q=29.0 size= 1697kB time=00:00:10.48 bitrate=1326.4kbits/ frame= 330 fps= 30 q=29.0 size= 1737kB time=00:00:11.05 bitrate=1286.5kbits/ frame= 345 fps= 30 q=29.0 size= 1776kB time=00:00:11.54 bitrate=1260.4kbits/ frame= 361 fps= 30 q=29.0 size= 1813kB time=00:00:12.07 bitrate=1230.3kbits/ frame= 377 fps= 30 q=29.0 size= 1847kB time=00:00:12.59 bitrate=1201.4kbits/ frame= 395 fps= 30 q=29.0 size= 1880kB time=00:00:13.22 bitrate=1165.0kbits/ frame= 410 fps= 30 q=29.0 size= 1993kB time=00:00:13.72 bitrate=1190.2kbits/ frame= 424 fps= 30 q=29.0 size= 2080kB time=00:00:14.18 bitrate=1201.4kbits/ frame= 439 fps= 30 q=29.0 size= 2166kB time=00:00:14.67 bitrate=1209.4kbits/ frame= 455 fps= 30 q=29.0 size= 2262kB time=00:00:15.21 bitrate=1217.5kbits/ frame= 469 fps= 30 q=29.0 size= 2341kB time=00:00:15.68 bitrate=1223.0kbits/ frame= 484 fps= 30 q=29.0 size= 2430kB time=00:00:16.19 bitrate=1229.1kbits/ frame= 500 fps= 30 q=29.0 size= 2523kB time=00:00:16.71 bitrate=1236.3kbits/ frame= 515 fps= 30 q=29.0 size= 2607kB time=00:00:17.21 bitrate=1240.4kbits/ frame= 531 fps= 30 q=29.0 size= 2681kB time=00:00:17.73 bitrate=1238.2kbits/ frame= 546 fps= 30 q=29.0 size= 2758kB time=00:00:18.24 bitrate=1238.2kbits/ frame= 561 fps= 30 q=29.0 size= 2824kB time=00:00:18.75 bitrate=1233.4kbits/ frame= 576 fps= 30 q=29.0 size= 2955kB time=00:00:19.25 bitrate=1256.8kbits/ frame= 586 fps= 29 q=29.0 size= 3061kB time=00:00:19.59 bitrate=1279.6kbits/ frame= 598 fps= 29 q=29.0 size= 3217kB time=00:00:19.99 bitrate=1318.4kbits/ frame= 610 fps= 29 q=29.0 size= 3354kB time=00:00:20.39 bitrate=1347.2kbits/ frame= 622 fps= 29 q=29.0 size= 3483kB time=00:00:20.78 bitrate=1372.6kbits/ frame= 634 fps= 29 q=29.0 size= 3593kB time=00:00:21.19 bitrate=1388.6kbits/ frame= 648 fps= 29 q=29.0 size= 3708kB time=00:00:21.66 bitrate=1402.3kbits/ frame= 661 fps= 29 q=29.0 size= 3811kB time=00:00:22.08 bitrate=1413.5kbits/ frame= 674 fps= 29 q=29.0 size= 3978kB time=00:00:22.53 bitrate=1446.3kbits/ frame= 690 fps= 29 q=29.0 size= 4133kB time=00:00:23.05 bitrate=1468.4kbits/ frame= 706 fps= 29 q=29.0 size= 4263kB time=00:00:23.58 bitrate=1480.4kbits/ frame= 721 fps= 29 q=29.0 size= 4391kB time=00:00:24.08 bitrate=1493.8kbits/ frame= 735 fps= 29 q=29.0 size= 4524kB time=00:00:24.55 bitrate=1509.4kbits/ frame= 748 fps= 29 q=29.0 size= 4661kB time=00:00:24.98 bitrate=1528.2kbits/ frame= 763 fps= 29 q=29.0 size= 4835kB time=00:00:25.50 bitrate=1553.1kbits/ frame= 778 fps= 29 q=29.0 size= 4993kB time=00:00:25.99 bitrate=1573.6kbits/ frame= 795 fps= 29 q=29.0 size= 5149kB time=00:00:26.56 bitrate=1588.1kbits/ frame= 814 fps= 29 q=29.0 size= 5258kB time=00:00:27.18 bitrate=1584.4kbits/ frame= 833 fps= 29 q=29.0 size= 5368kB time=00:00:27.82 bitrate=1580.2kbits/ frame= 851 fps= 29 q=29.0 size= 5469kB time=00:00:28.43 bitrate=1575.9kbits/ frame= 870 fps= 29 q=29.0 size= 5567kB time=00:00:29.05 bitrate=1569.5kbits/ frame= 889 fps= 29 q=29.0 size= 5688kB time=00:00:29.70 bitrate=1568.4kbits/ Starting second pass: moving header on top of the file frame= 902 fps= 28 q=-1.0 Lsize= 6109kB time=00:00:30.14 bitrate=1659.8kbits /s dup=1 drop=0 video:5602kB audio:472kB subtitle:0 global headers:0kB muxing overhead 0.566600% [libx264 @ 0000000004360620] frame I:8 Avg QP:20.52 size: 39667 [libx264 @ 0000000004360620] frame P:419 Avg QP:25.06 size: 10524 [libx264 @ 0000000004360620] frame B:475 Avg QP:29.03 size: 2123 [libx264 @ 0000000004360620] consecutive B-frames: 3.2% 79.6% 0.3% 16.9% [libx264 @ 0000000004360620] mb I I16..4: 20.7% 52.3% 26.9% [libx264 @ 0000000004360620] mb P I16..4: 0.7% 4.2% 1.1% P16..4: 39.4% 21.4 % 13.8% 0.0% 0.0% skip:19.3% [libx264 @ 0000000004360620] mb B I16..4: 0.1% 0.9% 0.3% B16..8: 41.8% 6.4 % 1.7% direct: 1.7% skip:47.1% L0:36.4% L1:53.3% BI:10.3% [libx264 @ 0000000004360620] 8x8 transform intra:65.7% inter:58.8% [libx264 @ 0000000004360620] coded y,uvDC,uvAC intra: 71.2% 76.6% 35.7% inter: 2 0.7% 13.0% 0.5% [libx264 @ 0000000004360620] i16 v,h,dc,p: 48% 24% 8% 20% [libx264 @ 0000000004360620] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 17% 18% 15% 6% 8% 11% 8% 10% 8% [libx264 @ 0000000004360620] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19% 16% 15% 7% 10% 11% 8% 8% 7% [libx264 @ 0000000004360620] i8c dc,h,v,p: 51% 22% 19% 9% [libx264 @ 0000000004360620] Weighted P-Frames: Y:0.7% UV:0.0% [libx264 @ 0000000004360620] ref P L0: 63.4% 19.7% 11.0% 5.9% 0.0% [libx264 @ 0000000004360620] ref B L0: 90.7% 8.7% 0.7% [libx264 @ 0000000004360620] ref B L1: 98.4% 1.6% [libx264 @ 0000000004360620] kb/s:1524.54

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• Averaging initial values for rolling series

  - by Dave Jarvis

  Question Given a maximum sliding window size of 40 (i.e., the set of numbers in the list cannot exceed 40), what is the calculation to ensure a smooth averaging transition as the set size grows from 1 to 40? Problem Description Creating a trend line for a set of data has skewed initial values. The complete set of values is unknown at runtime: they are provided one at a time. It seems like a reverse-weighted average is required so that the initial values are averaged differently. In the image below the leftmost data for the trend line are incorrectly averaged. Current Solution Created a new type of ArrayList subclass that calculates the appropriate values and ensures its size never goes beyond the bounds of the sliding window: /** * A list of Double values that has a maximum capacity enforced by a sliding * window. Can calculate the average of its values. */ public class AveragingList extends ArrayList<Double> { private float slidingWindowSize = 0.0f; /** * The initial capacity is used for the sliding window size. * @param slidingWindowSize */ public AveragingList( int slidingWindowSize ) { super( slidingWindowSize ); setSlidingWindowSize( ( float )slidingWindowSize ); } public boolean add( Double d ) { boolean result = super.add( d ); // Prevent the list from exceeding the maximum sliding window size. // if( size() > getSlidingWindowSize() ) { remove( 0 ); } return result; } /** * Calculate the average. * * @return The average of the values stored in this list. */ public double average() { double result = 0.0; int size = size(); for( Double d: this ) { result += d.doubleValue(); } return (double)result / (double)size; } /** * Changes the maximum number of numbers stored in this list. * * @param slidingWindowSize New maximum number of values to remember. */ public void setSlidingWindowSize( float slidingWindowSize ) { this.slidingWindowSize = slidingWindowSize; } /** * Returns the number used to determine the maximum values this list can * store before it removes the first entry upon adding another value. * @return The maximum number of numbers stored in this list. */ public float getSlidingWindowSize() { return slidingWindowSize; } } Resulting Image Example Input The data comes into the function one value at a time. For example, data points (Data) and calculated averages (Avg) typically look as follows: Data: 17.0 Avg : 17.0 Data: 17.0 Avg : 17.0 Data: 5.0 Avg : 13.0 Data: 5.0 Avg : 11.0  Related Sites The following pages describe moving averages, but typically when all (or sufficient) data is known: http://www.cs.princeton.edu/introcs/15inout/MovingAverage.java.html http://stackoverflow.com/questions/2161815/r-zoo-series-sliding-window-calculation http://taragana.blogspot.com/ http://www.dreamincode.net/forums/index.php?showtopic=92508 http://blogs.sun.com/nickstephen/entry/dtrace_and_moving_rolling_averages

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• Information Gain and Entropy

  - by dhorn

  I recently read this question regarding information gain and entropy. I think I have a semi-decent grasp on the main idea, but I'm curious as what to do with situations such as follows: If we have a bag of 7 coins, 1 of which is heavier than the others, and 1 of which is lighter than the others, and we know the heavier coin + the lighter coin is the same as 2 normal coins, what is the information gain associated with picking two random coins and weighing them against each other? Our goal here is to identify the two odd coins. I've been thinking this problem over for a while, and can't frame it correctly in a decision tree, or any other way for that matter. Any help? EDIT: I understand the formula for entropy and the formula for information gain. What I don't understand is how to frame this problem in a decision tree format. EDIT 2: Here is where I'm at so far: Assuming we pick two coins and they both end up weighing the same, we can assume our new chances of picking H+L come out to 1/5 * 1/4 = 1/20 , easy enough. Assuming we pick two coins and the left side is heavier. There are three different cases where this can occur: HM: Which gives us 1/2 chance of picking H and a 1/4 chance of picking L: 1/8 HL: 1/2 chance of picking high, 1/1 chance of picking low: 1/1 ML: 1/2 chance of picking low, 1/4 chance of picking high: 1/8 However, the odds of us picking HM are 1/7 * 5/6 which is 5/42 The odds of us picking HL are 1/7 * 1/6 which is 1/42 And the odds of us picking ML are 1/7 * 5/6 which is 5/42 If we weight the overall probabilities with these odds, we are given: (1/8) * (5/42) + (1/1) * (1/42) + (1/8) * (5/42) = 3/56. The same holds true for option B. option A = 3/56 option B = 3/56 option C = 1/20 However, option C should be weighted heavier because there is a 5/7 * 4/6 chance to pick two mediums. So I'm assuming from here I weight THOSE odds. I am pretty sure I've messed up somewhere along the way, but I think I'm on the right path! EDIT 3: More stuff. Assuming the scale is unbalanced, the odds are (10/11) that only one of the coins is the H or L coin, and (1/11) that both coins are H/L Therefore we can conclude: (10 / 11) * (1/2 * 1/5) and (1 / 11) * (1/2) EDIT 4: Going to go ahead and say that it is a total 4/42 increase.

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• FFMPEG dropping frames while encoding JPEG sequence at color change

  - by Matt

  I'm trying to put together a slide show using imagemagick and FFMPEG. I use imagemagick to expand a single photo into 30fps video (imagemagick also handles things like putting some text captions on the frames along the way). When I go to let ffmpeg digest it into a video it clips along nicely on the color parts of the video, but when it gets to a black and white section it reports "frame= 2030 fps=102 q=32766.0 Lsize= 5203kB time=00:01:07.60 bitrate= 630.5kbits/s dup=0 drop=703" and drops every frame of video until it hits something with color. As you can imagine this results in entire photos being removed from the slideshow. Here is my latest dump... ffmpeg -y -r 30 -i "teststream/%06d.jpg" -c:v libx264 -r 30 newffmpeg.mp4 ffmpeg version git-2012-12-10-c3bb333 Copyright (c) 2000-2012 the FFmpeg developers built on Dec 10 2012 22:02:04 with gcc 4.6.1 (Ubuntu/Linaro 4.6.1-9ubuntu3) configuration: --enable-gpl --enable-libfaac --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-librtmp --enable-libtheora --enable-libvorbis --enable-libx264 --enable-nonfree --enable-version3 libavutil 52. 12.100 / 52. 12.100 libavcodec 54. 79.101 / 54. 79.101 libavformat 54. 49.100 / 54. 49.100 libavdevice 54. 3.102 / 54. 3.102 libavfilter 3. 26.101 / 3. 26.101 libswscale 2. 1.103 / 2. 1.103 libswresample 0. 17.102 / 0. 17.102 libpostproc 52. 2.100 / 52. 2.100 Input #0, image2, from 'teststream/%06d.jpg': Duration: 00:12:02.80, start: 0.000000, bitrate: N/A Stream #0:0: Video: mjpeg, yuvj444p, 720x480 [SAR 72:72 DAR 3:2], 25 fps, 25 tbr, 25 tbn, 25 tbc [libx264 @ 0x3450140] using SAR=1/1 [libx264 @ 0x3450140] using cpu capabilities: MMX2 SSE2Fast SSSE3 FastShuffle SSE4.2 [libx264 @ 0x3450140] profile High, level 3.0 [libx264 @ 0x3450140] 264 - core 129 r2 1cffe9f - H.264/MPEG-4 AVC codec - Copyleft 2003-2012 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00 Output #0, mp4, to 'newffmpeg.mp4': Metadata: encoder : Lavf54.49.100 Stream #0:0: Video: h264 ([33][0][0][0] / 0x0021), yuvj420p, 720x480 [SAR 1:1 DAR 3:2], q=-1--1, 15360 tbn, 30 tbc Stream mapping: Stream #0:0 - #0:0 (mjpeg - libx264) Press [q] to stop, [?] for help Input stream #0:0 frame changed from size:720x480 fmt:yuvj444p to size:720x480 fmt:yuvj422p Input stream #0:0 frame changed from size:720x480 fmt:yuvj422p to size:720x480 fmt:yuvj444pp=584 frame= 2030 fps=102 q=32766.0 Lsize= 5203kB time=00:01:07.60 bitrate= 630.5kbits/s dup=0 drop=703 video:5179kB audio:0kB subtitle:0 global headers:0kB muxing overhead 0.472425% [libx264 @ 0x3450140] frame I:9 Avg QP:20.10 size: 33933 [libx264 @ 0x3450140] frame P:636 Avg QP:24.12 size: 6737 [libx264 @ 0x3450140] frame B:1385 Avg QP:27.04 size: 514 [libx264 @ 0x3450140] consecutive B-frames: 2.5% 15.2% 13.2% 69.2% [libx264 @ 0x3450140] mb I I16..4: 8.3% 80.3% 11.5% [libx264 @ 0x3450140] mb P I16..4: 1.5% 2.5% 0.2% P16..4: 41.7% 18.0% 10.3% 0.0% 0.0% skip:25.9% [libx264 @ 0x3450140] mb B I16..4: 0.0% 0.0% 0.0% B16..8: 26.6% 0.6% 0.1% direct: 0.2% skip:72.3% L0:35.0% L1:60.3% BI: 4.7% [libx264 @ 0x3450140] 8x8 transform intra:64.1% inter:75.1% [libx264 @ 0x3450140] coded y,uvDC,uvAC intra: 51.6% 78.0% 43.7% inter: 10.6% 14.9% 2.1% [libx264 @ 0x3450140] i16 v,h,dc,p: 29% 19% 6% 46% [libx264 @ 0x3450140] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 15% 17% 5% 9% 10% 7% 8% 6% [libx264 @ 0x3450140] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 31% 18% 11% 5% 9% 10% 6% 6% 4% [libx264 @ 0x3450140] i8c dc,h,v,p: 46% 18% 24% 12% [libx264 @ 0x3450140] Weighted P-Frames: Y:20.1% UV:18.7% [libx264 @ 0x3450140] ref P L0: 59.2% 23.2% 13.1% 4.3% 0.2% [libx264 @ 0x3450140] ref B L0: 88.7% 8.3% 3.0% [libx264 @ 0x3450140] ref B L1: 95.0% 5.0% [libx264 @ 0x3450140] kb/s:626.88 Received signal 2: terminating. One last note: If I remove the -r 30 from the input and output it works flawlessly. I have no idea why the -r 30 is causing it to freak out.

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• Can't configure frame relay T1 on Cisco 1760

  - by sonar

  For the past few days I've been trying to configure a data T1 via a Frame Relay. Now I've been pretty unsuccessful at it, and it's been a while, since I've done this so please bare with me. The ISP provided me the following information: 1. IP address 2. Gateway address 3. Encapsulation Frame Relay 4. DLCI 100 5. BZ8 ESF (I think the bz8 was supposed to be b8zs) 6. Time Slot (1 al 24). And what I have configured up until now is the following: interface Serial0/0 ip address <ip address> 255.255.255.252 encapsulation frame-relay service-module t1 timeslots 1-24 frame-relay interface-dlci 100 sh service-module s0/0 (outputs): Module type is T1/fractional Hardware revision is 0.128, Software revision is 0.2, Image checksum is 0x73D70058, Protocol revision is 0.1 Receiver has no alarms. Framing is **ESF**, Line Code is **B8ZS**, Current clock source is line, Fraction has **24 timeslots** (64 Kbits/sec each), Net bandwidth is 1536 Kbits/sec. Last module self-test (done at startup): Passed Last clearing of alarm counters 00:17:17 loss of signal : 0, loss of frame : 0, AIS alarm : 0, Remote alarm : 2, last occurred 00:10:10 Module access errors : 0, Total Data (last 1 15 minute intervals): 0 Line Code Violations, 0 Path Code Violations 0 Slip Secs, 0 Fr Loss Secs, 0 Line Err Secs, 0 Degraded Mins 0 Errored Secs, 0 Bursty Err Secs, 0 Severely Err Secs, 0 Unavail Secs Data in current interval (138 seconds elapsed): 0 Line Code Violations, 0 Path Code Violations 0 Slip Secs, 0 Fr Loss Secs, 0 Line Err Secs, 0 Degraded Mins 0 Errored Secs, 0 Bursty Err Secs, 0 Severely Err Secs, 0 Unavail Secs sh int: FastEthernet0/0 is up, line protocol is up Hardware is PQUICC_FEC, address is 000d.6516.e5aa (bia 000d.6516.e5aa) Internet address is 10.0.0.1/24 MTU 1500 bytes, BW 100000 Kbit, DLY 100 usec, reliability 255/255, txload 1/255, rxload 1/255 Encapsulation ARPA, loopback not set Keepalive set (10 sec) Full-duplex, 100Mb/s, 100BaseTX/FX ARP type: ARPA, ARP Timeout 04:00:00 Last input 00:20:00, output 00:00:00, output hang never Last clearing of "show interface" counters never Input queue: 0/75/0/0 (size/max/drops/flushes); Total output drops: 0 Queueing strategy: fifo Output queue: 0/40 (size/max) 5 minute input rate 0 bits/sec, 0 packets/sec 5 minute output rate 0 bits/sec, 0 packets/sec 0 packets input, 0 bytes Received 0 broadcasts, 0 runts, 0 giants, 0 throttles 0 input errors, 0 CRC, 0 frame, 0 overrun, 0 ignored 0 watchdog 0 input packets with dribble condition detected 191 packets output, 20676 bytes, 0 underruns 0 output errors, 0 collisions, 1 interface resets 0 babbles, 0 late collision, 0 deferred 0 lost carrier, 0 no carrier 0 output buffer failures, 0 output buffers swapped out Serial0/0 is up, line protocol is down Hardware is PQUICC with Fractional T1 CSU/DSU MTU 1500 bytes, BW 1536 Kbit, DLY 20000 usec, reliability 255/255, txload 1/255, rxload 1/255 Encapsulation FRAME-RELAY, loopback not set Keepalive set (10 sec) LMI enq sent 157, LMI stat recvd 0, LMI upd recvd 0, DTE LMI down LMI enq recvd 23, LMI stat sent 0, LMI upd sent 0 LMI DLCI 1023 LMI type is CISCO frame relay DTE FR SVC disabled, LAPF state down Broadcast queue 0/64, broadcasts sent/dropped 2/0, interface broadcasts 0 Last input 00:24:51, output 00:00:05, output hang never Last clearing of "show interface" counters 00:27:20 Input queue: 0/75/0/0 (size/max/drops/flushes); Total output drops: 0 Queueing strategy: weighted fair Output queue: 0/1000/64/0 (size/max total/threshold/drops) Conversations 0/1/256 (active/max active/max total) Reserved Conversations 0/0 (allocated/max allocated) Available Bandwidth 1152 kilobits/sec 5 minute input rate 0 bits/sec, 0 packets/sec 5 minute output rate 0 bits/sec, 0 packets/sec 23 packets input, 302 bytes, 0 no buffer Received 0 broadcasts, 0 runts, 0 giants, 0 throttles 1725 input errors, 595 CRC, 1099 frame, 0 overrun, 0 ignored, 30 abort 246 packets output, 3974 bytes, 0 underruns 0 output errors, 0 collisions, 48 interface resets 0 output buffer failures, 0 output buffers swapped out 4 carrier transitions DCD=up DSR=up DTR=up RTS=up CTS=up Serial0/0.1 is down, line protocol is down Hardware is PQUICC with Fractional T1 CSU/DSU MTU 1500 bytes, BW 1536 Kbit, DLY 20000 usec, reliability 255/255, txload 1/255, rxload 1/255 Encapsulation FRAME-RELAY Last clearing of "show interface" counters never Serial0/0.100 is down, line protocol is down Hardware is PQUICC with Fractional T1 CSU/DSU Internet address is <ip address>/30 MTU 1500 bytes, BW 1536 Kbit, DLY 20000 usec, reliability 255/255, txload 1/255, rxload 1/255 Encapsulation FRAME-RELAY Last clearing of "show interface" counters never And everything seems to be accounted for to me, but apparently I'm missing something. My issue is that I'm stuck on interface up, line protocol down, so the T1 doesn't go up. Any ideas? Thank you,

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• Speaker at the German Visual FoxPro Developer Conference 2004

  The following is an excerpt from the UniversalThread conference coverage of the German Visual FoxPro Developer Conference 2004 written by Hans-Otto Lochmann, Armin Neudert and myself. TRACK Active FoxPro Pages Back in 1996 Peter Herzog invented a FoxPro based solution to provide intranet capabilities for one of his customers. Nearly at the same time Rick Strahl had the same task and created WestWind Web Connection (WWWC). The aspect that developers have to have a full Visual FoxPro development environment to create WWWC solutions was the starting point of a "personal sportive competition" of Peter to write his own solution. But the main aspect has to be that it doesn't rely on a full VFP version in order to run. The VFP runtime should enough and the source code has to be compiled and interpreted on the fly. So, as Microsoft released Active Server Pages a name for Peter's solution was found: Active FoxPro Pages (AFP). During the years many drawbacks, design aspects as well as technological hassles forced ProLib Software to refactor the product. This way many limits like DCOM configuration, file-based information transfer between Web server and AFP, missing features (like upload forms or other Web servers than IIS) and extensibility were eliminated. As a consequence ProLib Software decided to rewrite Active FoxPro Pages in mid of 2002 completely. Christof Wollenhaupt, before his marriage known as Christof Lange, and Jochen Kirstätter had to solve this task. AFP 3.0 was officially released at German Devcon in November 2002. Today AFP has six distributors world-wide and there is a lot more information available online than before version 3.0. Directly after a short welcome speech by Rainer Becker, Jochen Kirstätter - aka JoKi - opened today's AFP track and introduced the basic concepts how Active FoxPro Pages works in general, explained the AFP terminilogy and every single component, and presented a small Walk-Through about how to write an AFP-based Web solution. Actually his presentation slides themselves were an AFP Web application. This way it was easy to integrate accompanying AFP samples on the fly. Additionally it was shown that no Visual FoxPro development environment is needed to create a Web application. A simple text editor like NotePad or any WYSIWYG editor on the market is usable to fullfil customer's requirements.Welcome at least two new speakers - Nina Schwanzer and Bernhard Reiter. Both are working at ProLib Software and this year's conference is their first time as speakers. And they did their job very well. The whole session was kind of a "ping pong" game and those two complemented each other to keep the audience in tension. First, they described typical requirements a modern desktop application should fullfil - online registration and activation, auto-update capabilities, or even frontend to administer a Web application on a remote system via internet, and explained how possible solutions like Web Services (using the SOAP interface), DCOM, and even .NET might solve those requirements. But any of those ways has different drawbacks like complicated installation or configuration, or extraordinary download sizes. Next, they introduced a technology they developed and used in a customer's project: Active FoxPro Pages Remote Procedure Call (AFP RPC). [...]   In the next session JoKi described how to extend Active FoxPro Pages. On the one hand AFP provides a plugin interface, and on the other hand any addon for Visual FoxPro might be usable as well. During the first half he spoke about the plugin interface and wrote live a new AFP extension - the Devcon plugin. Later he questioned any former step and showed that a single AFP document may solve the problem as well. So, developing extensions is only interesting if they are re-usable and generic. At the end he talked about multiple interfaces for the same business logic. For instance plain VFP class, COM server and .NET integration. Currently there are several specialized AFP extensions for sending mail, for using cryptographic routines (ie. based on .NET classes), or enhanced methods to handle HTML/XML strings.Rainer Becker and Peter Herzog introduced a new development for Visual Extend (VFX) - an AFP form builder. With this builder creating an AFP Web form designed with Visual FoxPro's form designer was a matter of seconds. The builder itself is currently in pre-release status and will be part of the VFX framework in the future. It was very impressive to see that the whole design of a form as well as most parts of its functionality were exported to a combination of HTML, JavaScript and Active FoxPro Pages. At half-time Jürgen "wOOdy" Wondzinski and JoKi changed places with Rainer and Peter, and presented some Web solutions in AFP. [...] Visual FoxPro 9.0 und Linux Is Linux still a topic for Visual FoxPro developers based on the activities during this year? In his session Jochen Kirstätter - aka JoKi - went not through the technical steps and requirements on how to setup and run FoxPro on a Linux client. Instead, he explained what Linux actually is, and talked about the high variety of distributions. In fact there are a lot of distributions around but since some several years there are some specialized ones available: Live Distributions (aka LiveCDs).The intension of LiveCDs is to run a full-featured Linux operating system on any personal computer directly from a bootable medium, like CD, DVD, or even USB memory stick, without installation on a hard disk. One of the first Linux LiveCDs was made by Klaus Knopper and is well-known as Knoppix. Today, many other LiveCDs are based on the concepts of Knoppix. During the session Jochen booted Morphix, a very light-weighted LiveCD, on his notebook, and actually showed the attendees that testing and playing around with Linux is absolutely easy. Running a text processing application swept away most of the contrary aspects the audience had. Okay, where is the part about FoxPro? Well, there are several scenarios a customer might require usage of Linux, and actually with all of them FoxPro could deal with. I guess that one of the more common ones is the situation that a customer has a heterogeneous intranet with Windows clients and Linux servers, i.e. Windows XP Professional and any Linux distribution on their servers. Even in this scenario there are two variants hidden! Why? Well, on the one hand there is a software package called Samba, that provides Windows server capabilities to a Linux system, and on the other hand there are several SQL servers for Linux, like PostgreSQL, DB2 and MySQL. Either way, FoxPro is able to deal with these scenarios, but you as developer have to know what you are talking about with your customers. And even if there's no Windows operating system, you are able to provide a FoxPro-based solution. Using the wine library - wine stands for Wine Is Not an Emulator - you are able to run your VFP applications on Linux clients, too; but not without reading VFP's EULA. Licenses were also part the session, and Jochen discussed the meaning of Open Source and its misunderstanding throughout most developers. Open Source does not mean that it's without a fee. Instead, it stands for access to the source code of an application or tool. And, VFP itself is one of the best samples to explain Open Source due to fact that since years, VFP is shipped with the xSource.zip archive. [...]

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• SPARC T3-1 Record Results Running JD Edwards EnterpriseOne Day in the Life Benchmark with Added Batch Component

  - by Brian

  Using Oracle's SPARC T3-1 server for the application tier and Oracle's SPARC Enterprise M3000 server for the database tier, a world record result was produced running the Oracle's JD Edwards EnterpriseOne applications Day in the Life benchmark run concurrently with a batch workload. The SPARC T3-1 server based result has 25% better performance than the IBM Power 750 POWER7 server even though the IBM result did not include running a batch component. The SPARC T3-1 server based result has 25% better space/performance than the IBM Power 750 POWER7 server as measured by the online component. The SPARC T3-1 server based result is 5x faster than the x86-based IBM x3650 M2 server system when executing the online component of the JD Edwards EnterpriseOne 9.0.1 Day in the Life benchmark. The IBM result did not include a batch component. The SPARC T3-1 server based result has 2.5x better space/performance than the x86-based IBM x3650 M2 server as measured by the online component. The combination of SPARC T3-1 and SPARC Enterprise M3000 servers delivered a Day in the Life benchmark result of 5000 online users with 0.875 seconds of average transaction response time running concurrently with 19 Universal Batch Engine (UBE) processes at 10 UBEs/minute. The solution exercises various JD Edwards EnterpriseOne applications while running Oracle WebLogic Server 11g Release 1 and Oracle Web Tier Utilities 11g HTTP server in Oracle Solaris Containers, together with the Oracle Database 11g Release 2. The SPARC T3-1 server showed that it could handle the additional workload of batch processing while maintaining the same number of online users for the JD Edwards EnterpriseOne Day in the Life benchmark. This was accomplished with minimal loss in response time. JD Edwards EnterpriseOne 9.0.1 takes advantage of the large number of compute threads available in the SPARC T3-1 server at the application tier and achieves excellent response times. The SPARC T3-1 server consolidates the application/web tier of the JD Edwards EnterpriseOne 9.0.1 application using Oracle Solaris Containers. Containers provide flexibility, easier maintenance and better CPU utilization of the server leaving processing capacity for additional growth. A number of Oracle advanced technology and features were used to obtain this result: Oracle Solaris 10, Oracle Solaris Containers, Oracle Java Hotspot Server VM, Oracle WebLogic Server 11g Release 1, Oracle Web Tier Utilities 11g, Oracle Database 11g Release 2, the SPARC T3 and SPARC64 VII+ based servers. This is the first published result running both online and batch workload concurrently on the JD Enterprise Application server. No published results are available from IBM running the online component together with a batch workload. The 9.0.1 version of the benchmark saw some minor performance improvements relative to 9.0. When comparing between 9.0.1 and 9.0 results, the reader should take this into account when the difference between results is small. Performance Landscape JD Edwards EnterpriseOne Day in the Life Benchmark Online with Batch Workload This is the first publication on the Day in the Life benchmark run concurrently with batch jobs. The batch workload was provided by Oracle's Universal Batch Engine. System RackUnits Online Users Resp Time (sec) BatchConcur(# of UBEs) BatchRate(UBEs/m) Version SPARC T3-1, 1xSPARC T3 (1.65 GHz), Solaris 10 M3000, 1xSPARC64 VII+ (2.86 GHz), Solaris 10 4 5000 0.88 19 10 9.0.1 Resp Time (sec) — Response time of online jobs reported in seconds Batch Concur (# of UBEs) — Batch concurrency presented in the number of UBEs Batch Rate (UBEs/m) — Batch transaction rate in UBEs/minute. JD Edwards EnterpriseOne Day in the Life Benchmark Online Workload Only These results are for the Day in the Life benchmark. They are run without any batch workload. System RackUnits Online Users ResponseTime (sec) Version SPARC T3-1, 1xSPARC T3 (1.65 GHz), Solaris 10 M3000, 1xSPARC64 VII (2.75 GHz), Solaris 10 4 5000 0.52 9.0.1 IBM Power 750, 1xPOWER7 (3.55 GHz), IBM i7.1 4 4000 0.61 9.0 IBM x3650M2, 2xIntel X5570 (2.93 GHz), OVM 2 1000 0.29 9.0 IBM result from http://www-03.ibm.com/systems/i/advantages/oracle/, IBM used WebSphere Configuration Summary Hardware Configuration: 1 x SPARC T3-1 server 1 x 1.65 GHz SPARC T3 128 GB memory 16 x 300 GB 10000 RPM SAS 1 x Sun Flash Accelerator F20 PCIe Card, 92 GB 1 x 10 GbE NIC 1 x SPARC Enterprise M3000 server 1 x 2.86 SPARC64 VII+ 64 GB memory 1 x 10 GbE NIC 2 x StorageTek 2540 + 2501 Software Configuration: JD Edwards EnterpriseOne 9.0.1 with Tools 8.98.3.3 Oracle Database 11g Release 2 Oracle 11g WebLogic server 11g Release 1 version 10.3.2 Oracle Web Tier Utilities 11g Oracle Solaris 10 9/10 Mercury LoadRunner 9.10 with Oracle Day in the Life kit for JD Edwards EnterpriseOne 9.0.1 Oracle’s Universal Batch Engine - Short UBEs and Long UBEs Benchmark Description JD Edwards EnterpriseOne is an integrated applications suite of Enterprise Resource Planning (ERP) software. Oracle offers 70 JD Edwards EnterpriseOne application modules to support a diverse set of business operations. Oracle's Day in the Life (DIL) kit is a suite of scripts that exercises most common transactions of JD Edwards EnterpriseOne applications, including business processes such as payroll, sales order, purchase order, work order, and other manufacturing processes, such as ship confirmation. These are labeled by industry acronyms such as SCM, CRM, HCM, SRM and FMS. The kit's scripts execute transactions typical of a mid-sized manufacturing company. The workload consists of online transactions and the UBE workload of 15 short and 4 long UBEs. LoadRunner runs the DIL workload, collects the user’s transactions response times and reports the key metric of Combined Weighted Average Transaction Response time. The UBE processes workload runs from the JD Enterprise Application server. Oracle's UBE processes come as three flavors: Short UBEs < 1 minute engage in Business Report and Summary Analysis, Mid UBEs > 1 minute create a large report of Account, Balance, and Full Address, Long UBEs > 2 minutes simulate Payroll, Sales Order, night only jobs. The UBE workload generates large numbers of PDF files reports and log files. The UBE Queues are categorized as the QBATCHD, a single threaded queue for large UBEs, and the QPROCESS queue for short UBEs run concurrently. One of the Oracle Solaris Containers ran 4 Long UBEs, while another Container ran 15 short UBEs concurrently. The mixed size UBEs ran concurrently from the SPARC T3-1 server with the 5000 online users driven by the LoadRunner. Oracle’s UBE process performance metric is Number of Maximum Concurrent UBE processes at transaction rate, UBEs/minute. Key Points and Best Practices Two JD Edwards EnterpriseOne Application Servers and two Oracle Fusion Middleware WebLogic Servers 11g R1 coupled with two Oracle Fusion Middleware 11g Web Tier HTTP Server instances on the SPARC T3-1 server were hosted in four separate Oracle Solaris Containers to demonstrate consolidation of multiple application and web servers. See Also SPARC T3-1 oracle.com SPARC Enterprise M3000 oracle.com Oracle Solaris oracle.com JD Edwards EnterpriseOne oracle.com Oracle Database 11g Release 2 Enterprise Edition oracle.com Disclosure Statement Copyright 2011, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 6/27/2011.

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• Improved Performance on PeopleSoft Combined Benchmark using SPARC T4-4

  - by Brian

  Oracle's SPARC T4-4 server running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved a world record 18,000 concurrent users experiencing subsecond response time while executing a PeopleSoft Payroll batch job of 500,000 employees in 32.4 minutes. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 47% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. Performance Landscape Results are presented for the PeopleSoft HRMS Self-Service and Payroll combined benchmark. The new result with 128 streams shows significant improvement in the payroll batch processing time with little impact on the self-service component response time. PeopleSoft HRMS Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.988 0.539 32.4 128 SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.944 0.503 43.3 64 The following results are for the PeopleSoft HRMS Self-Service benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the payroll component. PeopleSoft HRMS Self-Service 9.1 Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) 2x SPARC T4-2 (db) 18,000 1.048 0.742 N/A N/A The following results are for the PeopleSoft Payroll benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the self-service component. PeopleSoft Payroll (N.A.) 9.1 - 500K Employees (7 Million SQL PayCalc, Unicode) Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-4 (db) N/A N/A N/A 30.84 96 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory Oracle Solaris 11 11/11 Oracle Database 11g Release 2 PeopleTools 8.52 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Micro Focus Server Express (COBOL v 5.1.00) Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. A total of 128 PeopleSoft streams server processes where used on the database node to complete payroll batch job of 500,000 employees in 32.4 minutes. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Managementoracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 8 November 2012.

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• FFmpeg audio dont work in converted videos

  - by Juddy Swaft

  NOTICE: when i convert videos via terminal and download them from ftp into pc the audio works fine. I use: if($ext == "avi" && $convert_avi == true) { $convert_source = _VIDEOS_DIR_PATH.$new_name; $conv_name = substr(md5($file['name'].rand(1,888)), 2, 10).".mp4"; $converted_file = _VIDEOS_DIR_PATH.$conv_name; $ffmpeg_command = 'ffmpeg -i '.$convert_source.' -acodec libmp3lame -vcodec libx264 -s 1280x720 -ar 44100 -async 44100 -r 29.970 -ac 2 -qscale 5 '.$converted_file; echo exec($ffmpeg_command); $sql = "UPDATE pm_temp SET url = '".$conv_name."' WHERE url = '".$new_name."' LIMIT 1"; $result = @mysql_query($sql); unlink($convert_source); } This code to convert avi to mp4 ffmpeg concole output: root@1tb:~# ffmpeg -i sample.avi -acodec libmp3lame -vcodec libx264 -s 1280x720 -ar 44100 -async 44100 -r 29.970 -ac 2 -qscale 5 goodsample.mp4 ffmpeg version 0.7.15, Copyright (c) 2000-2013 the FFmpeg developers built on Feb 22 2013 07:18:58 with gcc 4.4.5 configuration: --enable-libdc1394 --prefix=/usr --extra-cflags='-Wall -g ' --cc='ccache cc' --enable-shared --enable-libmp3lame --enable-gpl --enable-libvorbis --enable-pthreads --enable-libfaac --enable-libxvid --enable-postproc --enable-x11grab --enable-libgsm --enable-libtheora --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libx264 --enable-libspeex --enable-nonfree --disable-stripping --enable-avfilter --enable-libdirac --disable-decoder=libdirac --enable-libfreetype --enable-libschroedinger --disable-encoder=libschroedinger - s libavutil 50. 43. 0 / 50. 43. 0 libavcodec 52.123. 0 / 52.123. 0 libavformat 52.111. 0 / 52.111. 0 libavdevice 52. 5. 0 / 52. 5. 0 libavfilter 1. 80. 0 / 1. 80. 0 libswscale 0. 14. 1 / 0. 14. 1 libpostproc 51. 2. 0 / 51. 2. 0 [mp3 @ 0x191d4100] Header missing [mpeg4 @ 0x191d1dc0] Invalid and inefficient vfw-avi packed B frames detected Input #0, avi, from 'sample.avi': Metadata: encoder : VirtualDubMod 1.5.10.2 (build 2540/release) Duration: 00:01:01.81, start: 0.000000, bitrate: 1194 kb/s Stream #0.0: Video: mpeg4, yuv420p, 640x352 [PAR 1:1 DAR 20:11], 23.98 tbr, Stream #0.1: Audio: mp3, 48000 Hz, stereo, s16, 128 kb/s [buffer @ 0x191d1c80] w:640 h:352 pixfmt:yuv420p tb:1/1000000 sar:1/1 sws_param: [scale @ 0x191d6880] w:640 h:352 fmt:yuv420p -> w:1280 h:720 fmt:yuv420p flags:0 [libx264 @ 0x191ce5a0] Default settings detected, using medium profile [libx264 @ 0x191ce5a0] using SAR=45/44 [libx264 @ 0x191ce5a0] using cpu capabilities: MMX2 SSE2Fast SSSE3 FastShuffle S [libx264 @ 0x191ce5a0] profile High, level 3.1 [libx264 @ 0x191ce5a0] 264 - core 118 - H.264/MPEG-4 AVC codec - Copyleft 2003-2 6 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_off 1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_l Output #0, mp4, to 'goodsample.mp4': Metadata: encoder : Lavf52.111.0 Stream #0.0: Video: libx264, yuv420p, 1280x720 [PAR 45:44 DAR 20:11], q=2-31 Stream #0.1: Audio: libmp3lame, 44100 Hz, stereo, s16, 64 kb/s Stream mapping: Stream #0.0 -> #0.0 Stream #0.1 -> #0.1 Press [q] to stop, [?] for help [mp3 @ 0x191d4100] Header missing Error while decoding stream #0.1 [mpeg4 @ 0x191d1dc0] Invalid and inefficient vfw-avi packed B frames detected [mp3 @ 0x191d4100] incomplete frame 9467kB time=00:01:00.32 bitrate=1285.5kbits/ Error while decoding stream #0.1 frame= 1852 fps= 20 q=29.0 Lsize= 9652kB time=00:01:01.72 bitrate=1280.9kbits video:9121kB audio:483kB global headers:0kB muxing overhead 0.499688% frame I:11 Avg QP:16.78 size: 51456 [libx264 @ 0x191ce5a0] frame P:784 Avg QP:20.81 size: 8954 [libx264 @ 0x191ce5a0] frame B:1057 Avg QP:26.06 size: 1659 [libx264 @ 0x191ce5a0] consecutive B-frames: 22.0% 3.1% 7.5% 67.4% [libx264 @ 0x191ce5a0] mb I I16..4: 31.1% 59.8% 9.1% [libx264 @ 0x191ce5a0] mb P I16..4: 1.8% 2.6% 0.2% P16..4: 24.3% 7.0% 4.0 [libx264 @ 0x191ce5a0] mb B I16..4: 0.1% 0.1% 0.0% B16..8: 22.7% 0.8% 0.2 [libx264 @ 0x191ce5a0] 8x8 transform intra:57.0% inter:72.6% [libx264 @ 0x191ce5a0] coded y,uvDC,uvAC intra: 44.4% 33.3% 10.3% inter: 7.6% 5. [libx264 @ 0x191ce5a0] i16 v,h,dc,p: 68% 14% 8% 10% [libx264 @ 0x191ce5a0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 21% 14% 27% 5% 7% 7% 6 [libx264 @ 0x191ce5a0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 28% 14% 14% 6% 10% 9% 7 [libx264 @ 0x191ce5a0] i8c dc,h,v,p: 67% 13% 17% 3% [libx264 @ 0x191ce5a0] Weighted P-Frames: Y:1.9% UV:0.4% [libx264 @ 0x191ce5a0] ref P L0: 62.2% 12.8% 10.3% 14.5% 0.2% [libx264 @ 0x191ce5a0] ref B L0: 88.1% 5.5% 6.4% [libx264 @ 0x191ce5a0] ref B L1: 95.7% 4.3% [libx264 @ 0x191ce5a0] kb/s:1209.03 I know there is couple errors tough, but i dont know hot to fix it. Also i would be very thankfull if someone can help reduce video size but is not main problem video weights as original avi but sill.

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