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  • JIT compiler for C, C++, and the likes

    - by Ebrahim
    Is there any just-in-time compiler out there for compiled languages, such as C and C++? (The first names that come to mind are Clang and LLVM! But I don't think they currently support it.) Explanation: I think the software could benefit from runtime profiling feedback and aggressively optimized recompilation of hotspots at runtime, even for compiled-to-machine languages like C and C++. Profile-guided optimization does a similar job, but with the difference a JIT would be more flexible in different environments. In PGO you run your binary prior to releasing it. After you released it, it would use no environment/input feedbacks collected at runtime. So if the input pattern is changed, it is probe to performance penalty. But JIT works well even in that conditions. However I think it is controversial wether the JIT compiling performance benefit outweights its own overhead. Edit: Grammar

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  • Advanced MySQL Replication - Improving Performance

    MySQL Replication can be made quite reliable and robust if the right tools are used to keep it running smoothly--but what if enormous loads on the primary server are overloading the slave server. Are there ways to speed up performance, so the slave can keep up?

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  • SQL SERVER – DMV – sys.dm_exec_query_optimizer_info – Statistics of Optimizer

    - by pinaldave
    Incredibly, SQL Server has so much information to share with us. Every single day, I am amazed with this SQL Server technology. Sometimes I find several interesting information by just querying few of the DMV. And when I present this info in front of my client during performance tuning consultancy, they are surprised with my findings. Today, I am going to share one of the hidden gems of DMV with you, the one which I frequently use to understand what’s going on under the hood of SQL Server. SQL Server keeps the record of most of the operations of the Query Optimizer. We can learn many interesting details about the optimizer which can be utilized to improve the performance of server. SELECT * FROM sys.dm_exec_query_optimizer_info WHERE counter IN ('optimizations', 'elapsed time','final cost', 'insert stmt','delete stmt','update stmt', 'merge stmt','contains subquery','tables', 'hints','order hint','join hint', 'view reference','remote query','maximum DOP', 'maximum recursion level','indexed views loaded', 'indexed views matched','indexed views used', 'indexed views updated','dynamic cursor request', 'fast forward cursor request') All occurrence values are cumulative and are set to 0 at system restart. All values for value fields are set to NULL at system restart. I have removed a few of the internal counters from the script above, and kept only documented details. Let us check the result of the above query. As you can see, there is so much vital information that is revealed in above query. I can easily say so many things about how many times Optimizer was triggered and what the average time taken by it to optimize my queries was. Additionally, I can also determine how many times update, insert or delete statements were optimized. I was able to quickly figure out that my client was overusing the Query Hints using this dynamic management view. If you have been reading my blog, I am sure you are aware of my series related to SQL Server Views SQL SERVER – The Limitations of the Views – Eleven and more…. With this, I can take a quick look and figure out how many times Views were used in various solutions within the query. Moreover, you can easily know what fraction of the optimizations has been involved in tuning server. For example, the following query would tell me, in total optimizations, what the fraction of time View was “reference“. As this View also includes system Views and DMVs, the number is a bit higher on my machine. SELECT (SELECT CAST (occurrence AS FLOAT) FROM sys.dm_exec_query_optimizer_info WHERE counter = 'view reference') / (SELECT CAST (occurrence AS FLOAT) FROM sys.dm_exec_query_optimizer_info WHERE counter = 'optimizations') AS ViewReferencedFraction Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • Performance Tuning Tips for Apache

    Apache is one of the most successful open source projects of our times. A big advantage of this popularity is that over the years people have spent a great deal of time fine tuning the software for better performance. Read on to learn more.

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  • Windows Azure Use Case: High-Performance Computing (HPC)

    - by BuckWoody
    This is one in a series of posts on when and where to use a distributed architecture design in your organization's computing needs. You can find the main post here: http://blogs.msdn.com/b/buckwoody/archive/2011/01/18/windows-azure-and-sql-azure-use-cases.aspx  Description: High-Performance Computing (also called Technical Computing) at its most simplistic is a layout of computer workloads where a “head node” accepts work requests, and parses them out to “worker nodes'”. This is useful in cases such as scientific simulations, drug research, MatLab work and where other large compute loads are required. It’s not the immediate-result type computing many are used to; instead, a “job” or group of work requests is sent to a cluster of computers and the worker nodes work on individual parts of the calculations and return the work to the scheduler or head node for the requestor in a batch-request fashion. This is typical to the way that many mainframe computing use-cases work. You can use commodity-based computers to create an HPC Cluster, such as the Linux application called Beowulf, and Microsoft has a server product for HPC using standard computers, called the Windows Compute Cluster that you can read more about here. The issue with HPC (from any vendor) that some organization have is the amount of compute nodes they need. Having too many results in excess infrastructure, including computers, buildings, storage, heat and so on. Having too few means that the work is slower, and takes longer to return a result to the calling application. Unless there is a consistent level of work requested, predicting the number of nodes is problematic. Implementation: Recently, Microsoft announced an internal partnership between the HPC group (Now called the Technical Computing Group) and Windows Azure. You now have two options for implementing an HPC environment using Windows. You can extend the current infrastructure you have for HPC by adding in Compute Nodes in Windows Azure, using a “Broker Node”.  You can then purchase time for adding machines, and then stop paying for them when the work is completed. This is a common pattern in groups that have a constant need for HPC, but need to “burst” that load count under certain conditions. The second option is to install only a Head Node and a Broker Node onsite, and host all Compute Nodes in Windows Azure. This is often the pattern for organizations that need HPC on a scheduled and periodic basis, such as financial analysis or actuarial table calculations. References: Blog entry on Hybrid HPC with Windows Azure: http://blogs.msdn.com/b/ignitionshowcase/archive/2010/12/13/high-performance-computing-on-premise-and-in-the-windows-azure-cloud.aspx  Links for further research on HPC, includes Windows Azure information: http://blogs.msdn.com/b/ncdevguy/archive/2011/02/16/handy-links-for-hpc-and-azure.aspx 

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  • SQL SERVER – Public Training and Private Training – Differences and Similarities

    - by pinaldave
    Earlier this year, I was on Road SQL Server Seminars. I did many SQL Server Performance Trainings and SQL Server Performance Consultations throughout the year but I feel the most rewarding exercise is always the one when instructor learns something from students, too. I was just talking to my wife, Nupur – she manages my logistics and administration related activities – and she pointed out that this year I have done 62% consultations and 38% trainings. I was bit surprised as I thought the numbers would be reversed. Every time I review the year, I think of training done at organizations. Well, I cannot argue with reality, I have done more consultations (some would call them projects) than training. I told my wife that I enjoy consultations more than training. She promptly asked me a question which was not directly related but made me think for long time, and in the end resulted in this blog post. Nupur asked me: what do I enjoy the most, public training or private training? I had a long conversation with her on this subject. I am not going to write long blog post which can change your life here. This is rather a small post condensing my one hour discussion into 200 words. Public Training is fun because… There are lots of different kinds of attendees There are always vivid questions Lots of questions on questions Less interest in theory and more interest in demos Good opportunity of future business Private Training is fun because… There is a focused interest One question is discussed deeply because of existing company issues More interest in “how it happened” concepts – under the hood operations Good connection with attendees This is also a good opportunity of future business Here I will stop my monologue and I want to open up this question to all of you: Question to Attendees - Which one do you enjoy the most – Public Training or Private Training? Question to Trainers - What do you enjoy the most – Public Training or Private Training? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Training, SQLAuthority News, T SQL, Technology

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  • World Record Performance on PeopleSoft Enterprise Financials Benchmark on SPARC T4-2

    - by Brian
    Oracle's SPARC T4-2 server achieved World Record performance on Oracle's PeopleSoft Enterprise Financials 9.1 executing 20 Million Journals lines in 8.92 minutes on Oracle Database 11g Release 2 running on Oracle Solaris 11. This is the first result published on this version of the benchmark. The SPARC T4-2 server was able to process 20 million general ledger journal edit and post batch jobs in 8.92 minutes on this benchmark that reflects a large customer environment that utilizes a back-end database of nearly 500 GB. This benchmark demonstrates that the SPARC T4-2 server with PeopleSoft Financials 9.1 can easily process 100 million journal lines in less than 1 hour. The SPARC T4-2 server delivered more than 146 MB/sec of IO throughput with Oracle Database 11g running on Oracle Solaris 11. Performance Landscape Results are presented for PeopleSoft Financials Benchmark 9.1. Results obtained with PeopleSoft Financials Benchmark 9.1 are not comparable to the the previous version of the benchmark, PeopleSoft Financials Benchmark 9.0, due to significant change in data model and supports only batch. PeopleSoft Financials Benchmark, Version 9.1 Solution Under Test Batch (min) SPARC T4-2 (2 x SPARC T4, 2.85 GHz) 8.92 Results from PeopleSoft Financials Benchmark 9.0. PeopleSoft Financials Benchmark, Version 9.0 Solution Under Test Batch (min) Batch with Online (min) SPARC Enterprise M4000 (Web/App) SPARC Enterprise M5000 (DB) 33.09 34.72 SPARC T3-1 (Web/App) SPARC Enterprise M5000 (DB) 35.82 37.01 Configuration Summary Hardware Configuration: 1 x SPARC T4-2 server 2 x SPARC T4 processors, 2.85 GHz 128 GB memory Storage Configuration: 1 x Sun Storage F5100 Flash Array (for database and redo logs) 2 x Sun Storage 2540-M2 arrays and 2 x Sun Storage 2501-M2 arrays (for backup) Software Configuration: Oracle Solaris 11 11/11 SRU 7.5 Oracle Database 11g Release 2 (11.2.0.3) PeopleSoft Financials 9.1 Feature Pack 2 PeopleSoft Supply Chain Management 9.1 Feature Pack 2 PeopleSoft PeopleTools 8.52 latest patch - 8.52.03 Oracle WebLogic Server 10.3.5 Java Platform, Standard Edition Development Kit 6 Update 32 Benchmark Description The PeopleSoft Enterprise Financials 9.1 benchmark emulates a large enterprise that processes and validates a large number of financial journal transactions before posting the journal entry to the ledger. The validation process certifies that the journal entries are accurate, ensuring that ChartFields values are valid, debits and credits equal out, and inter/intra-units are balanced. Once validated, the entries are processed, ensuring that each journal line posts to the correct target ledger, and then changes the journal status to posted. In this benchmark, the Journal Edit & Post is set up to edit and post both Inter-Unit and Regular multi-currency journals. The benchmark processes 20 million journal lines using AppEngine for edits and Cobol for post processes. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN PeopleSoft Financial Management oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition 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 1 October 2012.

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  • Of C# Iterators and Performance

    - by James Michael Hare
    Some of you reading this will be wondering, "what is an iterator" and think I'm locked in the world of C++.  Nope, I'm talking C# iterators.  No, not enumerators, iterators.   So, for those of you who do not know what iterators are in C#, I will explain it in summary, and for those of you who know what iterators are but are curious of the performance impacts, I will explore that as well.   Iterators have been around for a bit now, and there are still a bunch of people who don't know what they are or what they do.  I don't know how many times at work I've had a code review on my code and have someone ask me, "what's that yield word do?"   Basically, this post came to me as I was writing some extension methods to extend IEnumerable<T> -- I'll post some of the fun ones in a later post.  Since I was filtering the resulting list down, I was using the standard C# iterator concept; but that got me wondering: what are the performance implications of using an iterator versus returning a new enumeration?   So, to begin, let's look at a couple of methods.  This is a new (albeit contrived) method called Every(...).  The goal of this method is to access and enumeration and return every nth item in the enumeration (including the first).  So Every(2) would return items 0, 2, 4, 6, etc.   Now, if you wanted to write this in the traditional way, you may come up with something like this:       public static IEnumerable<T> Every<T>(this IEnumerable<T> list, int interval)     {         List<T> newList = new List<T>();         int count = 0;           foreach (var i in list)         {             if ((count++ % interval) == 0)             {                 newList.Add(i);             }         }           return newList;     }     So basically this method takes any IEnumerable<T> and returns a new IEnumerable<T> that contains every nth item.  Pretty straight forward.   The problem?  Well, Every<T>(...) will construct a list containing every nth item whether or not you care.  What happens if you were searching this result for a certain item and find that item after five tries?  You would have generated the rest of the list for nothing.   Enter iterators.  This C# construct uses the yield keyword to effectively defer evaluation of the next item until it is asked for.  This can be very handy if the evaluation itself is expensive or if there's a fair chance you'll never want to fully evaluate a list.   We see this all the time in Linq, where many expressions are chained together to do complex processing on a list.  This would be very expensive if each of these expressions evaluated their entire possible result set on call.    Let's look at the same example function, this time using an iterator:       public static IEnumerable<T> Every<T>(this IEnumerable<T> list, int interval)     {         int count = 0;         foreach (var i in list)         {             if ((count++ % interval) == 0)             {                 yield return i;             }         }     }   Notice it does not create a new return value explicitly, the only evidence of a return is the "yield return" statement.  What this means is that when an item is requested from the enumeration, it will enter this method and evaluate until it either hits a yield return (in which case that item is returned) or until it exits the method or hits a yield break (in which case the iteration ends.   Behind the scenes, this is all done with a class that the CLR creates behind the scenes that keeps track of the state of the iteration, so that every time the next item is asked for, it finds that item and then updates the current position so it knows where to start at next time.   It doesn't seem like a big deal, does it?  But keep in mind the key point here: it only returns items as they are requested. Thus if there's a good chance you will only process a portion of the return list and/or if the evaluation of each item is expensive, an iterator may be of benefit.   This is especially true if you intend your methods to be chainable similar to the way Linq methods can be chained.    For example, perhaps you have a List<int> and you want to take every tenth one until you find one greater than 10.  We could write that as:       List<int> someList = new List<int>();         // fill list here         someList.Every(10).TakeWhile(i => i <= 10);     Now is the difference more apparent?  If we use the first form of Every that makes a copy of the list.  It's going to copy the entire list whether we will need those items or not, that can be costly!    With the iterator version, however, it will only take items from the list until it finds one that is > 10, at which point no further items in the list are evaluated.   So, sounds neat eh?  But what's the cost is what you're probably wondering.  So I ran some tests using the two forms of Every above on lists varying from 5 to 500,000 integers and tried various things.    Now, iteration isn't free.  If you are more likely than not to iterate the entire collection every time, iterator has some very slight overhead:   Copy vs Iterator on 100% of Collection (10,000 iterations) Collection Size Num Iterated Type Total ms 5 5 Copy 5 5 5 Iterator 5 50 50 Copy 28 50 50 Iterator 27 500 500 Copy 227 500 500 Iterator 247 5000 5000 Copy 2266 5000 5000 Iterator 2444 50,000 50,000 Copy 24,443 50,000 50,000 Iterator 24,719 500,000 500,000 Copy 250,024 500,000 500,000 Iterator 251,521   Notice that when iterating over the entire produced list, the times for the iterator are a little better for smaller lists, then getting just a slight bit worse for larger lists.  In reality, given the number of items and iterations, the result is near negligible, but just to show that iterators come at a price.  However, it should also be noted that the form of Every that returns a copy will have a left-over collection to garbage collect.   However, if we only partially evaluate less and less through the list, the savings start to show and make it well worth the overhead.  Let's look at what happens if you stop looking after 80% of the list:   Copy vs Iterator on 80% of Collection (10,000 iterations) Collection Size Num Iterated Type Total ms 5 4 Copy 5 5 4 Iterator 5 50 40 Copy 27 50 40 Iterator 23 500 400 Copy 215 500 400 Iterator 200 5000 4000 Copy 2099 5000 4000 Iterator 1962 50,000 40,000 Copy 22,385 50,000 40,000 Iterator 19,599 500,000 400,000 Copy 236,427 500,000 400,000 Iterator 196,010       Notice that the iterator form is now operating quite a bit faster.  But the savings really add up if you stop on average at 50% (which most searches would typically do):     Copy vs Iterator on 50% of Collection (10,000 iterations) Collection Size Num Iterated Type Total ms 5 2 Copy 5 5 2 Iterator 4 50 25 Copy 25 50 25 Iterator 16 500 250 Copy 188 500 250 Iterator 126 5000 2500 Copy 1854 5000 2500 Iterator 1226 50,000 25,000 Copy 19,839 50,000 25,000 Iterator 12,233 500,000 250,000 Copy 208,667 500,000 250,000 Iterator 122,336   Now we see that if we only expect to go on average 50% into the results, we tend to shave off around 40% of the time.  And this is only for one level deep.  If we are using this in a chain of query expressions it only adds to the savings.   So my recommendation?  If you have a resonable expectation that someone may only want to partially consume your enumerable result, I would always tend to favor an iterator.  The cost if they iterate the whole thing does not add much at all -- and if they consume only partially, you reap some really good performance gains.   Next time I'll discuss some of my favorite extensions I've created to make development life a little easier and maintainability a little better.

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  • Tutorial: Getting Started with the NoSQL JavaScript / Node.js API for MySQL Cluster

    - by Mat Keep
    Tutorial authored by Craig Russell and JD Duncan  The MySQL Cluster team are working on a new NoSQL JavaScript connector for MySQL. The objectives are simplicity and high performance for JavaScript users: - allows end-to-end JavaScript development, from the browser to the server and now to the world's most popular open source database - native "NoSQL" access to the storage layer without going first through SQL transformations and parsing. Node.js is a complete web platform built around JavaScript designed to deliver millions of client connections on commodity hardware. With the MySQL NoSQL Connector for JavaScript, Node.js users can easily add data access and persistence to their web, cloud, social and mobile applications. While the initial implementation is designed to plug and play with Node.js, the actual implementation doesn't depend heavily on Node, potentially enabling wider platform support in the future. Implementation The architecture and user interface of this connector are very different from other MySQL connectors in a major way: it is an asynchronous interface that follows the event model built into Node.js. To make it as easy as possible, we decided to use a domain object model to store the data. This allows for users to query data from the database and have a fully-instantiated object to work with, instead of having to deal with rows and columns of the database. The domain object model can have any user behavior that is desired, with the NoSQL connector providing the data from the database. To make it as fast as possible, we use a direct connection from the user's address space to the database. This approach means that no SQL (pun intended) is needed to get to the data, and no SQL server is between the user and the data. The connector is being developed to be extensible to multiple underlying database technologies, including direct, native access to both the MySQL Cluster "ndb" and InnoDB storage engines. The connector integrates the MySQL Cluster native API library directly within the Node.js platform itself, enabling developers to seamlessly couple their high performance, distributed applications with a high performance, distributed, persistence layer delivering 99.999% availability. The following sections take you through how to connect to MySQL, query the data and how to get started. Connecting to the database A Session is the main user access path to the database. You can get a Session object directly from the connector using the openSession function: var nosql = require("mysql-js"); var dbProperties = {     "implementation" : "ndb",     "database" : "test" }; nosql.openSession(dbProperties, null, onSession); The openSession function calls back into the application upon creating a Session. The Session is then used to create, delete, update, and read objects. Reading data The Session can read data from the database in a number of ways. If you simply want the data from the database, you provide a table name and the key of the row that you want. For example, consider this schema: create table employee (   id int not null primary key,   name varchar(32),   salary float ) ENGINE=ndbcluster; Since the primary key is a number, you can provide the key as a number to the find function. function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find('employee', 0, onData); }; function onData = function(err, data) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(data));   ... use data in application }; If you want to have the data stored in your own domain model, you tell the connector which table your domain model uses, by specifying an annotation, and pass your domain model to the find function. var annotations = new nosql.Annotations(); function Employee = function(id, name, salary) {   this.id = id;   this.name = name;   this.salary = salary;   this.giveRaise = function(percent) {     this.salary *= percent;   } }; annotations.mapClass(Employee, {'table' : 'employee'}); function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find(Employee, 0, onData); }; Updating data You can update the emp instance in memory, but to make the raise persistent, you need to write it back to the database, using the update function. function onData = function(err, emp) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(emp));   emp.giveRaise(0.12); // gee, thanks!   session.update(emp); // oops, session is out of scope here }; Using JavaScript can be tricky because it does not have the concept of block scope for variables. You can create a closure to handle these variables, or use a feature of the connector to remember your variables. The connector api takes a fixed number of parameters and returns a fixed number of result parameters to the callback function. But the connector will keep track of variables for you and return them to the callback. So in the above example, change the onSession function to remember the session variable, and you can refer to it in the onData function: function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find(Employee, 0, onData, session); }; function onData = function(err, emp, session) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(emp));   emp.giveRaise(0.12); // gee, thanks!   session.update(emp, onUpdate); // session is now in scope }; function onUpdate = function(err, emp) {   if (err) {     console.log(err);     ... error handling   } Inserting data Inserting data requires a mapped JavaScript user function (constructor) and a session. Create a variable and persist it: function onSession = function(err, session) {   var data = new Employee(999, 'Mat Keep', 20000000);   session.persist(data, onInsert);   } }; Deleting data To remove data from the database, use the session remove function. You use an instance of the domain object to identify the row you want to remove. Only the key field is relevant. function onSession = function(err, session) {   var key = new Employee(999);   session.remove(Employee, onDelete);   } }; More extensive queries We are working on the implementation of more extensive queries along the lines of the criteria query api. Stay tuned. How to evaluate The MySQL Connector for JavaScript is available for download from labs.mysql.com. Select the build: MySQL-Cluster-NoSQL-Connector-for-Node-js You can also clone the project on GitHub Since it is still early in development, feedback is especially valuable (so don't hesitate to leave comments on this blog, or head to the MySQL Cluster forum). Try it out and see how easy (and fast) it is to integrate MySQL Cluster into your Node.js platforms. You can learn more about other previewed functionality of MySQL Cluster 7.3 here

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  • Google I/O 2012 - Building High Performance Mobile Web Applications

    Google I/O 2012 - Building High Performance Mobile Web Applications Ryan Fioravanti Learn what it takes to build an HTML5 mobile app that will wow your users. This session will focus on speed, offline support, UI layouts, and the tools necessary to set up a productive development environment. Come to this session if you're looking to make a killer mobile web app that stands out amongst the competition. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 33 0 ratings Time: 49:43 More in Science & Technology

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  • Android XML Parser Performance

    Shane Conder will show us how different XML parsers affect performance with Android and the answers might surprise you. The article provides developers with data for choosing a particular XML parser and Android code that demonstrates all three parsers.

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  • When to use shared libraries for a web framework?

    - by CamelBlues
    tl;dr: I've found myself hosting a bunch of sites running on the same web framework (symfony 1.4). Would it be helpful if I moved all of the shared library code into the same directory and shared it across the sites? more I see some advantages to this: Each site takes up less disk space Library updates (an unlikely scenario) can take place across all sites I also see some disadvantages, mostly in terms of a single point of failure and the inability to have sites using different versions of the framework. My real concern, though, is performance. I hypothesize that I will see a performance increase, since the PHP code will already be cached for all sites when they call the framework. Is this a correct hypothesis?

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  • Oracle ETPM v2.3.1 Examachine Performance Benchmark Data Sheet

    - by Paula Speranza-Hadley
    Oracle Tax is pleased to announce the exceptional results of the Oracle ETPM v2.3.1 Examachine performance benchmark.   The benchmark achieved the following results:  · Processed8M outpayments and 2M payments in  6 hours · Processed 1M forms in 4 hours · Near  linear scalability of batch processing For the complete data sheet, please click on the following link:  https://blogs.oracle.com/tax/resource/OracleETPMv231ExamachinePerformanceBenchmarkDataSheet.pdf

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  • Multithreaded UI desktop application issues

    - by igor
    I am involved into development a rich UI project: desktop windows application. Application uses asynchronous invocations and in its turn it should be ready to process external messages (events). The problem is clear: at first time it was built as a simple prototype and it was not stress tested and all was fine. Then application was grown: the number of calls to server and number of events from server are high and performance is low. What is more users noticed that sometimes performance is extremal low. Asynchronous invocations based on thread pool (BeginInvoke, EndInvoke), external events are going from WCF service (.NET 3.5). My goal is synchronization of all tasks and putting priorities to every executions in desktop application. My question is: is there any practice how to reach my goal: patterns, task priority list, others? What should I do at first, second and next times? Thanks

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  • Multithreded UI desktop application issues

    - by igor
    I am involved into development a rich UI project: desktop windows application. Application uses asynchronous invocations and in its turn it should be ready to process external messages (events). The problem is clear: at first time it was built as a simple prototype and it was not stress tested and all was fine. Then application was grown: the number of calls to server and number of events from server are high and performance is low. What is more users noticed that sometimes performance is extremal low. Asynchronous invocations based on thread pool (BeginInvoke, EndInvoke), external events are going from WCF service (.NET 3.5). My goal is synchronization of all tasks and putting priorities to every executions in desktop application. My question is: is there any practice how to reach my goal: patterns, task priority list, others? What should I do at first, second and next times? Thanks

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  • HTML5 mobile game development vs. native game apps

    - by Vic Szpilman
    What is the current state of game engines, frameworks, libraries and conversions related to the HTML5 set of technologies (including CSS3 and JavaScript libraries such as RaphaelJS, Impact, gameQuery); and how does the best of that compare with developing a native app (especially for iOS and Android)? Especially in terms of performance, visuals and getting that "native feel". Thoughts on solutions such as Appcelerator and Corona SDK are also appreciated. In regards to Unity3D, is it possible to develop in it and still have the game be playable on a browser (such as current releases of Chrome or Firefox, at least) without any dependencies or having the user install anything (no unity web player). What I'm looking for is how to develop in web standards as to reach the maximum number of platforms (including outside mobile) while still retaining a native experience for mobile without having to implement the game anew for iOS and Android.

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  • The meaning of thermal throttle counters and package power limit notifications in Linux

    - by Trustin Lee
    Whenever I do some performance testing on my Linux-installed MacBook Pro, I often see the following messages in dmesg: Aug 8 09:29:31 infinity kernel: [79791.789404] CPU1: Package power limit notification (total events = 40365) Aug 8 09:29:31 infinity kernel: [79791.789408] CPU3: Package power limit notification (total events = 40367) Aug 8 09:29:31 infinity kernel: [79791.789411] CPU2: Package power limit notification (total events = 40453) Aug 8 09:29:31 infinity kernel: [79791.789414] CPU0: Package power limit notification (total events = 40453) I also see the throttle counters in the sysfs increases over time: trustin@infinity:/sys/devices/system/cpu/cpu0/thermal_throttle $ ls core_power_limit_count package_power_limit_count core_throttle_count package_throttle_count $ cat core_power_limit_count 0 $ cat core_throttle_count 41912 $ cat package_power_limit_count 67945 $ cat package_throttle_count 67565 What do these counters mean? Do they affect the performance of CPU or system? Do they result in increased deviation of performance numbers? (i.e. Do they prevent me from getting reliable performance numbers?) If so, how do I avoid these messages and increasing counters? Would running the performance tests on a well-cooled desktop system help?

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  • Supercharging the Performance of Your Front-Office Applications @ OOW'12

    - by Sanjeev Sharma
    You can increase customer satisfaction, brand equity, and ultimately top-line revenue by deploying  Oracle ATG Web Commerce, Oracle WebCenter Sites, Oracle Endeca applications, Oracle’s  Siebel applications, and other front-office applications on Oracle Exalogic, Oracle’s combination  of hardware and software for applications and middleware. Join me (Sanjeev Sharma) and my colleague, Kelly Goetsch, at the following conference session at Oracle Open World to find out how Customer Experience can be transformed with Oracle Exalogic: Session:  CON9421 - Supercharging the Performance of Your Front-Office Applications with Oracle ExalogicDate: Wednesday, 3 Oct, 2012Time: 10:15 am - 11:15 am (PST)Venue: Moscone South (309)

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  • Windows Azure Recipe: High Performance Computing

    - by Clint Edmonson
    One of the most attractive ways to use a cloud platform is for parallel processing. Commonly known as high-performance computing (HPC), this approach relies on executing code on many machines at the same time. On Windows Azure, this means running many role instances simultaneously, all working in parallel to solve some problem. Doing this requires some way to schedule applications, which means distributing their work across these instances. To allow this, Windows Azure provides the HPC Scheduler. This service can work with HPC applications built to use the industry-standard Message Passing Interface (MPI). Software that does finite element analysis, such as car crash simulations, is one example of this type of application, and there are many others. The HPC Scheduler can also be used with so-called embarrassingly parallel applications, such as Monte Carlo simulations. Whatever problem is addressed, the value this component provides is the same: It handles the complex problem of scheduling parallel computing work across many Windows Azure worker role instances. Drivers Elastic compute and storage resources Cost avoidance Solution Here’s a sketch of a solution using our Windows Azure HPC SDK: Ingredients Web Role – this hosts a HPC scheduler web portal to allow web based job submission and management. It also exposes an HTTP web service API to allow other tools (including Visual Studio) to post jobs as well. Worker Role – typically multiple worker roles are enlisted, including at least one head node that schedules jobs to be run among the remaining compute nodes. Database – stores state information about the job queue and resource configuration for the solution. Blobs, Tables, Queues, Caching (optional) – many parallel algorithms persist intermediate and/or permanent data as a result of their processing. These fast, highly reliable, parallelizable storage options are all available to all the jobs being processed. Training Here is a link to online Windows Azure training labs where you can learn more about the individual ingredients described above. (Note: The entire Windows Azure Training Kit can also be downloaded for offline use.) Windows Azure HPC Scheduler (3 labs)  The Windows Azure HPC Scheduler includes modules and features that enable you to launch and manage high-performance computing (HPC) applications and other parallel workloads within a Windows Azure service. The scheduler supports parallel computational tasks such as parametric sweeps, Message Passing Interface (MPI) processes, and service-oriented architecture (SOA) requests across your computing resources in Windows Azure. With the Windows Azure HPC Scheduler SDK, developers can create Windows Azure deployments that support scalable, compute-intensive, parallel applications. See my Windows Azure Resource Guide for more guidance on how to get started, including links web portals, training kits, samples, and blogs related to Windows Azure.

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  • Downloading large files hangs system

    - by Igor
    When Im trying to download large files, i.e. 1gb or more under FireFox, first of all it starts with very big download speed and in few seconds in almost get up to max (~11 MBps). It is downloading very fast, but when downloaded size becomes near 700-800mb and more, my system almost completely hangs, so I can do nothing - I just have to wait until it finishes downloading. Also when it hangs, I can't see the download progress - it looks like it completely hangs. Sometimes, however, if the file size is near 1gb, the system comes back from hang, finishing download, but sometimes I just cant wait before system comes back and have to kill FF from top (it takes me 2 minutes to do this, because of very slow system performance). I use Firefox as primary browser. If I use wget with direct link to file - everything is fine. Speed at max, no performance decrease. So what can I do?

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