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• Performance of Multiple Joins

  - by geeko

  Greetings Overflowers, I need to query against objects with many/complex spacial conditions. In relational databases that is translated to many joins (possibly 10+). I'm new to this business and wondering whether to go with MS SQL Server 2008 R2 or Oracle 11g or document-based solutions such as RavenDB or simply go with some spacial database (GIS)... Any thoughts ? Regards UPDATE: Thank you all for your answers. Would anybody opt for document/spatial databases ? My database would consist of tens of millions to few billion records. Mostly read-only. Almost no updates unless in case of mistakes in input. Overnight inserts and not that frequent. The join tables are predicted beforehand but the number of self joins (tables joining themselves multiple times) is not. Small pages of results from such queries are going to be viewed on an highly interactive website so response time is critical. Any predictions on how this can perform on MS SQL Server 2008 R2 or Oracle 11g ? I'm also concerned about boosting performance by adding more servers, which one scales better ? How about PostgresQL ?

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• Why doesn't this CompiledQuery give a performance improvement?

  - by Grammarian

  I am trying to speed up an often used query. Using a CompiledQuery seemed to be the answer. But when I tried the compiled version, there was no difference in performance between the compiled and non-compiled versions. Can someone please tell me why using Queries.FindTradeByTradeTagCompiled is not faster than using Queries.FindTradeByTradeTag? static class Queries { // Pre-compiled query, as per http://msdn.microsoft.com/en-us/library/bb896297 private static readonly Func<MyEntities, int, IQueryable<Trade>> mCompiledFindTradeQuery = CompiledQuery.Compile<MyEntities, int, IQueryable<Trade>>( (entities, tag) => from trade in entities.TradeSet where trade.trade_tag == tag select trade); public static Trade FindTradeByTradeTagCompiled(MyEntities entities, int tag) { IQueryable<Trade> tradeQuery = mCompiledFindTradeQuery(entities, tag); return tradeQuery.FirstOrDefault(); } public static Trade FindTradeByTradeTag(MyEntities entities, int tag) { IQueryable<Trade> tradeQuery = from trade in entities.TradeSet where trade.trade_tag == tag select trade; return tradeQuery.FirstOrDefault(); } }

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• Javascript, IE, Strings, and Performance problems

  - by Infinity

  Hey guys, So we have this product, and it's really slow in IE. We've already applied a lot of the practices advised by the IE guys themselves (like this, and this), and try to sacrifice clean code for performance in the critical parts like DOM manipulation. However, as you can see in this IE profiler screenshot.. Just "String" is the biggest offender. Almost 750ms of exclusive time. Does this mean IE is spending 750ms just instantiating Strings? I also read this stuff on the Opera dev blog: A build script can remove whitespace, comments, replace strings with Array lookups (to avoid MSIE creating a string object for every single instance of a string — even in conditions) But no more info regarding this. Anyone can clarify? It seems like IE has to create a full String instance every time you have " " in your code, which could explain this, but I don't know what the array lookup optimization would look like. BTW- we don't really do much of string concatenation anywhere in the code. The library we use is MooTools 1.2.4 Any suggestions will be appreciated! Thx

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• Performance of inter-database query (between linked servers)

  - by Swoosh

  I have an import between 2 linked servers. I basically got to get the data from a multiple join into a table on my side. The current query is something like this: select a.* from db1.dbo.tbl1 a inner join db1.dbo.tbl2 on ... inner join db1.dbo.tbl3 on ... inner join db1.dbo.tbl4 on ... inner join db2.dbo.myside on ... db1 = linked server db2 = my own database After this one, I am using an insert into + select to add this data in my table which is located in db2. (usually few hundred records - this import running once a minute) My question is related to performance. The tables on the linked server (tbl1, tbl2, tbl3, tbl4) are huge tables, with millions of records, and it is slowing down the import process. I was told that, if I do the join on the "other" side (db1 - linked server) for example in a stored procedure, than, even if the query looks the same, it would run faster. Is that right? This is kinda hard to test. Note that the join contains a table from my database too. Also. are there other "tricks" I could use in order to make this run faster? Thanks

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• Performance when querying a View

  - by Nate Bross

  I'm wondering if this is a bad practice or if in general this is the correct approach. Lets say that I've created a view that combines a few attributes from a few tables. My question, what do I need to do so I can query against this view as if it were a table without worrying about performance? All attributes in the original tables are indexed, my concern is that the result view will have hundreds of thousands of records, which I will want to narrow down quite a bit based on user input. What I'd like to avoid, is having multiple versions of the code that generates this view floating around with a few extra "where" conditions to facilitate the user input filtering. For example, assume my view has this header VIEW(Name, Type, DateEntered) this may have 100,000+ rows (possibly millions). I'd like to be able to make this view in SQL Server, and then in my application write querlies like this: SELECT Name, Type, DateEntered FROM MyView WHERE DateEntered BETWEEN @date1 and @date2; Basically, I am denormalizing my data for a series of reports that need to be run, and I'd like to centralize where I pull the data from, maybe I'm not looking at this problem from the right angle though, so I'm open to alternative ways to attack this.

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• Determining Best Table Structure for MySQL Performance

  - by Joe Majewski

  I'm working on a browser-based RPG for one of my websites, and right now I'm trying to determine the best way to organize my SQL tables for performance and maintenance. Here's my question: Does the number of columns in an SQL table affect the speed in which it can be queried? I am not a newbie when it comes to PHP or MySQL. I used to develop things with the common goal of getting them to work, but I've recently advanced to the stage where a functional program is not good enough unless it's fast and reliable. Anyways, right now I have a members table that has around 15 columns. It contains information such as the player's username, password, email, logins, page views, etcetera. It doesn't contain any information on the player's progress in the game, however. If I added columns for things such as army size, gold, turns, and whatnot, then it could easily rise to around 40 or 50 total columns. Oh, and my database structure IS normalized. Will a table with 50 columns that gets constantly queried be a bad idea? Should I split it into two tables; one for the user's general information and one for the user's game statistics? I know I could check the query time myself, but I haven't actually created the tables yet and I think I'd be better off with some professional advice on this important decision for my game. Thank you for your time! :)

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• Timeout Considerations for Solicit Response – Part 2

  - by Michael Stephenson

  To follow up a previous article about timeouts and how they can affect your application I have extended the sample we were using to include WCF. I will execute some test scenarios and discuss the results. The sample We begin by consuming exactly the same web service which is sitting on a remote server. This time I have created a .net 3.5 application which will consume the web service using the basichttp binding. To show you the configuration for the consumption of this web service please refer to the below diagram. You can see like before we also have the connectionManagement element in the configuration file. I have added a WCF service reference (also using the asynchronous proxy methods) and have the below code sample in the application which will asynchronously make the web service calls and handle the responses on a call back method invoked by a delegate. If you have read the previous article you will notice that the code is almost the same.   Sample 1 – WCF with Default Timeouts In this test I set about recreating the same scenario as previous where we would run the test but this time using WCF as the messaging component. For the first test I would use the default configuration settings which WCF had setup when we added a reference to the web service. The timeout values for this test are: closeTimeout="00:01:00" openTimeout="00:01:00" receiveTimeout="00:10:00" sendTimeout="00:01:00"   The Test We simulated 21 calls to the web service Test Results The client-side trace is as follows:   The server-side trace is as follows: Some observations on the results are as follows: The timeouts happened quicker than in the previous tests because some calls were timing out before they attempted to connect to the server The first few calls that timed out did actually connect to the server and did execute successfully on the server   Test 2 – Increase Open Connection Timeout & Send Timeout In this test I wanted to increase both the send and open timeout values to try and give everything a chance to go through. The timeout values for this test are: closeTimeout="00:01:00" openTimeout="00:10:00" receiveTimeout="00:10:00" sendTimeout="00:10:00"   The Test We simulated 21 calls to the web service   Test Results The client side trace for this test was   The server-side trace for this test was: Some observations on this test are: This test proved if the timeouts are high enough everything will just go through   Test 3 – Increase just the Send Timeout In this test we wanted to increase just the send timeout. The timeout values for this test are: closeTimeout="00:01:00" openTimeout="00:01:00" receiveTimeout="00:10:00" sendTimeout="00:10:00"   The Test We simulated 21 calls to the web service   Test Results The below is the client side trace The below is the server side trace Some observations on this test are: In this test from both the client and server perspective everything ran through fine The open connection timeout did not seem to have any effect   Test 4 – Increase Just the Open Connection Timeout In this test I wanted to validate the change to the open connection setting by increasing just this on its own. The timeout values for this test are: closeTimeout="00:01:00" openTimeout="00:10:00" receiveTimeout="00:10:00" sendTimeout="00:01:00"   The Test We simulated 21 calls to the web service Test Results The client side trace was The server side trace was Some observations on this test are: In this test you can see that the open connection which relates to opening the channel timeout increase was not the thing which stopped the calls timing out It's the send of data which is timing out On the server you can see that the successful few calls were fine but there were also a few calls which hit the server but timed out on the client You can see that not all calls hit the server which was one of the problems with the WSE and ASMX options   Test 5 – Smaller Increase in Send Timeout In this test I wanted to make a smaller increase to the send timeout than previous just to prove that it was the key setting which was controlling what was timing out. The timeout values for this test are: openTimeout="00:01:00" receiveTimeout="00:10:00" sendTimeout="00:02:30"   The Test We simulated 21 calls to the web service Test Results The client side trace was   The server side trace was Some observations on this test are: You can see that most of the calls got through fine On the client you can see that call 20 timed out but still hit the server and executed fine.   Summary At this point between the two articles we have quite a lot of scenarios showing the different way the timeout setting have played into our original performance issue, and now we can see how WCF could offer an improved way to handle the problem. To summarise the differences in the timeout properties for the three technology stacks: ASMX The timeout value only applies to the execution time of your request on the server. The timeout does not consider how long your code might be waiting client side to get a connection. WSE The timeout value includes both the time to obtain a connection and also the time to execute the request. A timeout will not be thrown as an error until an attempt to connect to the server is made. This means a 40 second timeout setting may not throw the error until 60 seconds when the connection to the server is made. If the connection to the server is made you should be aware that your message will be processed and you should design for this. WCF The WCF send timeout is the setting most equivalent to the settings we were looking at previously. Like WSE this setting the counter includes the time to get a connection as well as the time to execute on a server. Unlike WSE and ASMX an error will be thrown as soon as the send timeout from making your call from user code has elapsed regardless of whether we are waiting for a connection or have an open connection to the server. This may to a user appear to have better latency in getting an error response compared to WSE or ASMX.

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• Visual Studio Load Testing using Windows Azure

  - by Tarun Arora

  In my opinion the biggest adoption barrier in performance testing on smaller projects is not the tooling but the high infrastructure and administration cost that comes with this phase of testing. Only if a reusable solution was possible and infrastructure management wasn’t as expensive, adoption would certainly spike. It certainly is possible if you bring Visual Studio and Windows Azure into the equation. It is possible to run your test rig in the cloud without getting tangled in SCVMM or Lab Management. All you need is an active Azure subscription, Windows Azure endpoint enabled developer workstation running visual studio ultimate on premise, windows azure endpoint enabled worker roles on azure compute instances set up to run as test controllers and test agents. My test rig is running SQL server 2012 and Visual Studio 2012 RC agents. The beauty is that the solution is reusable, you can open the azure project, change the subscription and certificate, click publish and *BOOM* in less than 15 minutes you could have your own test rig running in the cloud. In this blog post I intend to show you how you can use the power of Windows Azure to effectively abstract the administration cost of infrastructure management and lower the total cost of Load & Performance Testing. As a bonus, I will share a reusable solution that you can use to automate test rig creation for both VS 2010 agents as well as VS 2012 agents. Introduction The slide show below should help you under the high level details of what we are trying to achive... Leveraging Azure for Performance Testing View more PowerPoint from Avanade Scenario 1 – Running a Test Rig in Windows Azure To start off with the basics, in the first scenario I plan to discuss how to, - Automate deployment & configuration of Windows Azure Worker Roles for Test Controller and Test Agent - Automate deployment & configuration of SQL database on Test Controller on the Test Controller Worker Role - Scaling Test Agents on demand - Creating a Web Performance Test and a simple Load Test - Managing Test Controllers right from Visual Studio on Premise Developer Workstation - Viewing results of the Load Test - Cleaning up - Have the above work in the shape of a reusable solution for both VS2010 and VS2012 Test Rig Scenario 2 – The scaled out Test Rig and sharing data using SQL Azure A scaled out version of this implementation would involve running multiple test rigs running in the cloud, in this scenario I will show you how to sync the load test database from these distributed test rigs into one SQL Azure database using Azure sync. The selling point for this scenario is being able to collate the load test efforts from across the organization into one data store. - Deploy multiple test rigs using the reusable solution from scenario 1 - Set up and configure Windows Azure Sync - Test SQL Azure Load Test result database created as a result of Windows Azure Sync - Cleaning up - Have the above work in the shape of a reusable solution for both VS2010 and VS2012 Test Rig The Ingredients Though with an active MSDN ultimate subscription you would already have access to everything and more, you will essentially need the below to try out the scenarios, 1. Windows Azure Subscription 2. Windows Azure Storage – Blob Storage 3. Windows Azure Compute – Worker Role 4. SQL Azure Database 5. SQL Data Sync 6. Windows Azure Connect – End points 7. SQL 2012 Express or SQL 2008 R2 Express 8. Visual Studio All Agents 2012 or Visual Studio All Agents 2010 9. A developer workstation set up with Visual Studio 2012 – Ultimate or Visual Studio 2010 – Ultimate 10. Visual Studio Load Test Unlimited Virtual User Pack. Walkthrough To set up the test rig in the cloud, the test controller, test agent and SQL express installers need to be available when the worker role set up starts, the easiest and most efficient way is to pre upload the required software into Windows Azure Blob storage. SQL express, test controller and test agent expose various switches which we can take advantage of including the quiet install switch. Once all the 3 have been installed the test controller needs to be registered with the test agents and the SQL database needs to be associated to the test controller. By enabling Windows Azure connect on the machines in the cloud and the developer workstation on premise we successfully create a virtual network amongst the machines enabling 2 way communication. All of the above can be done programmatically, let’s see step by step how… Scenario 1 Video Walkthrough–Leveraging Windows Azure for performance Testing Scenario 2 Work in progress, watch this space for more… Solution If you are still reading and are interested in the solution, drop me an email with your windows live id. I’ll add you to my TFS preview project which has a re-usable solution for both VS 2010 and VS 2012 test rigs as well as guidance and demo performance tests.   Conclusion Other posts and resources available here. Possibilities…. Endless!

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• Performance issues when using SSD for a developer notebook (WAMP/LAMP stack)?

  - by András Szepesházi

  I'm a web application developer using my notebook as a standalone development environment (WAMP stack). I just switched from a Core2-duo Vista 32 bit notebook with 2Gb RAM and SATA HDD, to an i5-2520M Win7 64 bit with 4Gb RAM and 128 GB SDD (Corsair P3 128). My initial experience was what I expected, fast boot, quick load of all the applications (Eclipse takes now 5 seconds as opposed to 30s on my old notebook), overall great experience. Then I started to build up my development stack, both as LAMP (using VirtualBox with a debian guest) and WAMP (windows native apache + mysql + php). I wanted to compare those two. This still all worked great out, then I started to pull in my projects to these stacks. And here came the nasty surprise, one of those projects produced a lot worse response times than on my old notebook (that was true for both the VirtualBox and WAMP stack). Apache, php and mysql configurations were practically identical in all environments. I started to do a lot of benchmarking and profiling, and here is what I've found: All general benchmarks (Performance Test 7.0, HDTune Pro, wPrime2 and some more) gave a big advantage to the new notebook. Nothing surprising here. Disc specific tests showed that read/write operations peaked around 380M/160M for the SSD, and all the different sized block operations also performed very well. Started apache performance benchmarking with Apache Benchmark for a small static html file (10 concurrent threads, 500 iterations). Old notebook: min 47ms, median 111ms, max 156ms New WAMP stack: min 71ms, median 135ms, max 296ms New LAMP stack (in VirtualBox): min 6ms, median 46ms, max 175ms Right here I don't get why the native WAMP stack performed so bad, but at least the LAMP environment brought the expected speed. Apache performance measurement for non-cached php content. The php runs a loop of 1000 and generates sha1(uniqid()) inisde. Again, 10 concurrent threads, 500 iterations were used for the benchmark. Old notebook: min 0ms, median 39ms, max 218ms New WAMP stack: min 20ms, median 61ms, max 186ms New LAMP stack (in VirtualBox): min 124ms, median 704ms, max 2463ms What the hell? The new LAMP performed miserably, and even the new native WAMP was outperformed by the old notebook. php + mysql test. The test consists of connecting to a database and reading a single record form a table using INNER JOIN on 3 more (indexed) tables, repeated 100 times within a loop. Databases were identical. 10 concurrent threads, 100 iterations were used for the benchmark. Old notebook: min 1201ms, median 1734ms, max 3728ms New WAMP stack: min 367ms, median 675ms, max 1893ms New LAMP stack (in VirtualBox): min 1410ms, median 3659ms, max 5045ms And the same test with concurrency set to 1 (instead of 10): Old notebook: min 1201ms, median 1261ms, max 1357ms New WAMP stack: min 399ms, median 483ms, max 539ms New LAMP stack (in VirtualBox): min 285ms, median 348ms, max 444ms Strictly for my purposes, as I'm using a self contained development environment (= low concurrency) I could be satisfied with the second test's result. Though I have no idea why the VirtualBox environment performed so bad with higher concurrency. Finally I performed a test of including many php files. The application that I mentioned at the beginning, the one that was performing so bad, has a heavy bootstrap, loads hundreds of small library and configuration files while initializing. So this test does nothing else just includes about 100 files. Concurrency set to 1, 100 iterations: Old notebook: min 140ms, median 168ms, max 406ms New WAMP stack: min 434ms, median 488ms, max 604ms New LAMP stack (in VirtualBox): min 413ms, median 1040ms, max 1921ms Even if I consider that VirtualBox reached those files via shared folders, and that slows things down a bit, I still don't see how could the old notebook outperform so heavily both new configurations. And I think this is the real root of the slow performance, as the application uses even more includes, and the whole bootstrap will occur several times within a page request (for each ajax call, for example). To sum it up, here I am with a brand new high-performance notebook that loads the same page in 20 seconds, that my old notebook can do in 5-7 seconds. Needless to say, I'm not a very happy person right now. Why do you think I experience these poor performance values? What are my options to remedy this situation?

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• Why Swift is 100 times slower than C in this image processing test?

  - by xiaobai

  Like many other developers I have been very excited at the new Swift language from Apple. Apple has boasted its speed is faster than Objective C and can be used to write operating system. And from what I learned so far, it's a very type-safe language and able to have precisely control over the exact data type (like integer length). So it does look like having good potential handling performance critical tasks, like image processing, right? That's what I thought before I carried out a quick test. The result really surprised me. Here is a much simplified image alpha blending code snippet in C: test.c: #include <stdio.h> #include <stdint.h> #include <string.h> uint8_t pixels[640*480]; uint8_t alpha[640*480]; uint8_t blended[640*480]; void blend(uint8_t* px, uint8_t* al, uint8_t* result, int size) { for(int i=0; i<size; i++) { result[i] = (uint8_t)(((uint16_t)px[i]) *al[i] /255); } } int main(void) { memset(pixels, 128, 640*480); memset(alpha, 128, 640*480); memset(blended, 255, 640*480); // Test 10 frames for(int i=0; i<10; i++) { blend(pixels, alpha, blended, 640*480); } return 0; } I compiled it on my Macbook Air 2011 with the following command: gcc -O3 test.c -o test The 10 frame processing time is about 0.01s. In other words, it takes the C code 1ms to process one frame: $ time ./test real 0m0.010s user 0m0.006s sys 0m0.003s Then I have a Swift version of the same code: test.swift: let pixels = UInt8[](count: 640*480, repeatedValue: 128) let alpha = UInt8[](count: 640*480, repeatedValue: 128) let blended = UInt8[](count: 640*480, repeatedValue: 255) func blend(px: UInt8[], al: UInt8[], result: UInt8[], size: Int) { for(var i=0; i<size; i++) { var b = (UInt16)(px[i]) * (UInt16)(al[i]) result[i] = (UInt8)(b/255) } } for i in 0..10 { blend(pixels, alpha, blended, 640*480) } The build command line is: xcrun swift -O3 test.swift -o test Here I use the same O3 level optimization flag to make the comparison hopefully fair. However, the resulting speed is 100 time slower: $ time ./test real 0m1.172s user 0m1.146s sys 0m0.006s In other words, it takes Swift ~120ms to processing one frame which takes C just 1 ms. I also verified the memory initialization time in both test code are very small compared to the blend processing function time. What happened?

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• How can you know what is w3wp.exe doing? (or how to diagnose a performance problem)

  - by Daniel Magliola

  I'm having a performance problem in a site we've made, and I'm not exactly sure how to start diagnosing it. The short description is: We have a very small site (http://hearablog.com) with very little traffic, in a crappy dedicated server, CPU is always very high, sometimes it stays at 100% for minutes, and w3wp.exe is taking most of it. A typical scenario is w3wp.exe takes 60%, and SQL Server takes about 30%. Our DB is pretty small too. Long description and more details: The site is hosted in a very crappy server by Cari.Net. From the beginning we had the feeling that the server didn't quite behave correctly, like some things would take just too long, so this could be a configuration problem from the get go. It may also be that we are getting a virtual server while we're supposed to have a dedicated one, although we have no evidence that'd indicate this, except for the fact that the server tends to be quite slow. The server is Windows 2008 Standard 64-bit, with SQL 2008 Express Hardware is a Celeron 2.80 GHz, 1Gb RAM The website is developed in ASP.Net MVC, using Entity Framework for data access. Now, this is pretty crappy hardware, but i've had other servers with these guys, with equivalent (or worse) HW, and performance is much better than this one. That said, the other servers have W2003 and SQL2005, and I'm using ASP.Net "WebForms" 2.0, no MVC, no LINQ, no EF; so I'm not sure whether going to 2008 / the other stuff means a big performance penalty is expected. I'm serving MP3 files (5-20 Mb) regularly, which is a slightly unusual load, maybe that is causing some kind of problems? Would that cause w3wp to use a lot of CPU? Disk usage seems very low. Memory is usually around 90%, but disk usage seems to indicate it's not paging much. I get tons of e-mails every day about SQL timeouts, for queries taking over 30 seconds, although all our queries are pretty straightforward (or should be, but EF may be screwing it up). This is what resource monitor looks like in one of these "sprints" of 100% CPU, in case there's anything useful there. And a snapshot of some performance counters: Now, what confuses me very much is that CPU usage of w3wp is just so high. It shouldn't be doing much really... So my questions are... Is there any way of finding out "what" it is doing? Maybe even profile it? Any performance counters I should be looking at? Is this to be expected given this hardware/software configuration? Is this could be cause by some kind of configuration failure, where would you start looking? Thank you VERY much. Daniel Magliola

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• Oracle T4CPreparedStatement memory leaks?

  - by Jay

  A little background on the application that I am gonna talk about in the next few lines: XYZ is a data masking workbench eclipse RCP application: You give it a source table column, and a target table column, it would apply a trasformation (encryption/shuffling/etc) and copy the row data from source table to target table. Now, when I mask n tables at a time, n threads are launched by this app. Here is the issue: I have run into a production issue on first roll out of the above said app. Unfortunately, I don't have any logs to get to the root. However, I tried to run this app in test region and do a stress test. When I collected .hprof files and ran 'em through an analyzer (yourKit), I noticed that objects of oracle.jdbc.driver.T4CPreparedStatement was retaining heap. The analysis also tells me that one of my classes is holding a reference to this preparedstatement object and thereby, n threads have n such objects. T4CPreparedStatement seemed to have character arrays: lastBoundChars and bindChars each of size char[300000]. So, I researched a bit (google!), obtained ojdbc6.jar and tried decompiling T4CPreparedStatement. I see that T4CPreparedStatement extends OraclePreparedStatement, which dynamically manages array size of lastBoundChars and bindChars. So, my questions here are: Have you ever run into an issue like this? Do you know the significance of lastBoundChars / bindChars? I am new to profiling, so do you think I am not doing it correct? (I also ran the hprofs through MAT - and this was the main identified issue - so, I don't really think I could be wrong?) I have found something similar on the web here: http://forums.oracle.com/forums/thread.jspa?messageID=2860681 Appreciate your suggestions / advice.

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• Defining jUnit Test cases Correctly

  - by Epitaph

  I am new to Unit Testing and therefore wanted to do some practical exercise to get familiar with the jUnit framework. I created a program that implements a String multiplier public String multiply(String number1, String number2) In order to test the multiplier method, I created a test suite consisting of the following test cases (with all the needed integer parsing, etc) @Test public class MultiplierTest { Multiplier multiplier = new Multiplier(); // Test for 2 positive integers assertEquals("Result", 5, multiplier.multiply("5", "1")); // Test for 1 positive integer and 0 assertEquals("Result", 0, multiplier.multiply("5", "0")); // Test for 1 positive and 1 negative integer assertEquals("Result", -1, multiplier.multiply("-1", "1")); // Test for 2 negative integers assertEquals("Result", 10, multiplier.multiply("-5", "-2")); // Test for 1 positive integer and 1 non number assertEquals("Result", , multiplier.multiply("x", "1")); // Test for 1 positive integer and 1 empty field assertEquals("Result", , multiplier.multiply("5", "")); // Test for 2 empty fields assertEquals("Result", , multiplier.multiply("", "")); In a similar fashion, I can create test cases involving boundary cases (considering numbers are int values) or even imaginary values. 1) But, what should be the expected value for the last 3 test cases above? (a special number indicating error?) 2) What additional test cases did I miss? 3) Is assertEquals() method enough for testing the multiplier method or do I need other methods like assertTrue(), assertFalse(), assertSame() etc 4) Is this the RIGHT way to go about developing test cases? How am I "exactly" benefiting from this exercise? 5)What should be the ideal way to test the multiplier method? I am pretty clueless here. If anyone can help answer these queries I'd greatly appreciate it. Thank you.

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• Is this slow WPF TextBlock performance expected?

  - by Ben Schoepke

  Hi, I am doing some benchmarking to determine if I can use WPF for a new product. However, early performance results are disappointing. I made a quick app that uses data binding to display a bunch of random text inside of a list box every 100 ms and it was eating up ~15% CPU. So I made another quick app that skipped the data binding/data template scheme and does nothing but update 10 TextBlocks that are inside of a ListBox every 100 ms (the actual product wouldn't require 100 ms updates, more like 500 ms max, but this is a stress test). I'm still seeing ~10-15% CPU usage. Why is this so high? Is it because of all the garbage strings? Here's the XAML: <Grid> <ListBox x:Name="numericsListBox"> <ListBox.Resources> <Style TargetType="TextBlock"> <Setter Property="FontSize" Value="48"/> <Setter Property="Width" Value="300"/> </Style> </ListBox.Resources> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> <TextBlock/> </ListBox> </Grid> Here's the code behind: public partial class Window1 : Window { private int _count = 0; public Window1() { InitializeComponent(); } private void OnLoad(object sender, RoutedEventArgs e) { var t = new DispatcherTimer(TimeSpan.FromSeconds(0.1), DispatcherPriority.Normal, UpdateNumerics, Dispatcher); t.Start(); } private void UpdateNumerics(object sender, EventArgs e) { ++_count; foreach (object textBlock in numericsListBox.Items) { var t = textBlock as TextBlock; if (t != null) t.Text = _count.ToString(); } } } Any ideas for a better way to quickly render text? My computer: XP SP3, 2.26 GHz Core 2 Duo, 4 GB RAM, Intel 4500 HD integrated graphics. And that is an order of magnitude beefier than the hardware I'd need to develop for in the real product.

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• Performance of tokenizing CSS in PHP

  - by Boldewyn

  This is a noob question from someone who hasn't written a parser/lexer ever before. I'm writing a tokenizer/parser for CSS in PHP (please don't repeat with 'OMG, why in PHP?'). The syntax is written down by the W3C neatly here (CSS2.1) and here (CSS3, draft). It's a list of 21 possible tokens, that all (but two) cannot be represented as static strings. My current approach is to loop through an array containing the 21 patterns over and over again, do an if (preg_match()) and reduce the source string match by match. In principle this works really good. However, for a 1000 lines CSS string this takes something between 2 and 8 seconds, which is too much for my project. Now I'm banging my head how other parsers tokenize and parse CSS in fractions of seconds. OK, C is always faster than PHP, but nonetheless, are there any obvious D'Oh! s that I fell into? I made some optimizations, like checking for '@', '#' or '"' as the first char of the remaining string and applying only the relevant regexp then, but this hadn't brought any great performance boosts. My code (snippet) so far: $TOKENS = array( 'IDENT' => '...regexp...', 'ATKEYWORD' => '@...regexp...', 'String' => '"...regexp..."|\'...regexp...\'', //... ); $string = '...CSS source string...'; $stream = array(); // we reduce $string token by token while ($string != '') { $string = ltrim($string, " \t\r\n\f"); // unconsumed whitespace at the // start is insignificant but doing a trim reduces exec time by 25% $matches = array(); // loop through all possible tokens foreach ($TOKENS as $t => $p) { // The '&' is used as delimiter, because it isn't used anywhere in // the token regexps if (preg_match('&^'.$p.'&Su', $string, $matches)) { $stream[] = array($t, $matches[0]); $string = substr($string, strlen($matches[0])); // Yay! We found one that matches! continue 2; } } // if we come here, we have a syntax error and handle it somehow } // result: an array $stream consisting of arrays with // 0 => type of token // 1 => token content

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• agent-based simulation: performance issue: Python vs NetLogo & Repast

  - by max

  I'm replicating a small piece of Sugarscape agent simulation model in Python 3. I found the performance of my code is ~3 times slower than that of NetLogo. Is it likely the problem with my code, or can it be the inherent limitation of Python? Obviously, this is just a fragment of the code, but that's where Python spends two-thirds of the run-time. I hope if I wrote something really inefficient it might show up in this fragment: UP = (0, -1) RIGHT = (1, 0) DOWN = (0, 1) LEFT = (-1, 0) all_directions = [UP, DOWN, RIGHT, LEFT] # point is just a tuple (x, y) def look_around(self): max_sugar_point = self.point max_sugar = self.world.sugar_map[self.point].level min_range = 0 random.shuffle(self.all_directions) for r in range(1, self.vision+1): for d in self.all_directions: p = ((self.point[0] + r * d[0]) % self.world.surface.length, (self.point[1] + r * d[1]) % self.world.surface.height) if self.world.occupied(p): # checks if p is in a lookup table (dict) continue if self.world.sugar_map[p].level > max_sugar: max_sugar = self.world.sugar_map[p].level max_sugar_point = p if max_sugar_point is not self.point: self.move(max_sugar_point) Roughly equivalent code in NetLogo (this fragment does a bit more than the Python function above): ; -- The SugarScape growth and motion procedures. -- to M ; Motion rule (page 25) locals [ps p v d] set ps (patches at-points neighborhood) with [count turtles-here = 0] if (count ps > 0) [ set v psugar-of max-one-of ps [psugar] ; v is max sugar w/in vision set ps ps with [psugar = v] ; ps is legal sites w/ v sugar set d distance min-one-of ps [distance myself] ; d is min dist from me to ps agents set p random-one-of ps with [distance myself = d] ; p is one of the min dist patches if (psugar >= v and includeMyPatch?) [set p patch-here] setxy pxcor-of p pycor-of p ; jump to p set sugar sugar + psugar-of p ; consume its sugar ask p [setpsugar 0] ; .. setting its sugar to 0 ] set sugar sugar - metabolism ; eat sugar (metabolism) set age age + 1 end On my computer, the Python code takes 15.5 sec to run 1000 steps; on the same laptop, the NetLogo simulation running in Java inside the browser finishes 1000 steps in less than 6 sec. EDIT: Just checked Repast, using Java implementation. And it's also about the same as NetLogo at 5.4 sec. Recent comparisons between Java and Python suggest no advantage to Java, so I guess it's just my code that's to blame? EDIT: I understand MASON is supposed to be even faster than Repast, and yet it still runs Java in the end.

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• Performance Optimization for Matrix Rotation

  - by Summer_More_More_Tea

  Hello everyone: I'm now trapped by a performance optimization lab in the book "Computer System from a Programmer's Perspective" described as following: In a N*N matrix M, where N is multiple of 32, the rotate operation can be represented as: Transpose: interchange elements M(i,j) and M(j,i) Exchange rows: Row i is exchanged with row N-1-i A example for matrix rotation(N is 3 instead of 32 for simplicity): ------- ------- |1|2|3| |3|6|9| ------- ------- |4|5|6| after rotate is |2|5|8| ------- ------- |7|8|9| |1|4|7| ------- ------- A naive implementation is: #define RIDX(i,j,n) ((i)*(n)+(j)) void naive_rotate(int dim, pixel *src, pixel *dst) { int i, j; for (i = 0; i < dim; i++) for (j = 0; j < dim; j++) dst[RIDX(dim-1-j, i, dim)] = src[RIDX(i, j, dim)]; } I come up with an idea by inner-loop-unroll. The result is: Code Version Speed Up original 1x unrolled by 2 1.33x unrolled by 4 1.33x unrolled by 8 1.55x unrolled by 16 1.67x unrolled by 32 1.61x I also get a code snippet from pastebin.com that seems can solve this problem: void rotate(int dim, pixel *src, pixel *dst) { int stride = 32; int count = dim >> 5; src += dim - 1; int a1 = count; do { int a2 = dim; do { int a3 = stride; do { *dst++ = *src; src += dim; } while(--a3); src -= dim * stride + 1; dst += dim - stride; } while(--a2); src += dim * (stride + 1); dst -= dim * dim - stride; } while(--a1); } After carefully read the code, I think main idea of this solution is treat 32 rows as a data zone, and perform the rotating operation respectively. Speed up of this version is 1.85x, overwhelming all the loop-unroll version. Here are the questions: In the inner-loop-unroll version, why does increment slow down if the unrolling factor increase, especially change the unrolling factor from 8 to 16, which does not effect the same when switch from 4 to 8? Does the result have some relationship with depth of the CPU pipeline? If the answer is yes, could the degrade of increment reflect pipeline length? What is the probable reason for the optimization of data-zone version? It seems that there is no too much essential difference from the original naive version. EDIT: My test environment is Intel Centrino Duo processor and the verion of gcc is 4.4 Any advice will be highly appreciated! Kind regards!

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• Neo4j 1.9.4 (REST Server,CYPHER) performance issue

  - by user2968943

  I have Neo4j 1.9.4 installed on 24 core 24Gb ram (centos) machine and for most queries CPU usage spikes goes to 200% with only few concurrent requests. Domain: some sort of social application where few types of nodes(profiles) with 3-30 text/array properties and 36 relationship types with at least 3 properties. Most of nodes currently has ~300-500 relationships. Current data set footprint(from console): LogicalLogSize=4294907 (32MB) ArrayStoreSize=1675520 (12MB) NodeStoreSize=1342170 (10MB) PropertyStoreSize=1739548 (13MB) RelationshipStoreSize=6395202 (48MB) StringStoreSize=1478400 (11MB) which is IMHO really small. most queries looks like this one(with more or less WITH .. MATCH .. statements and few queries with variable length relations but the often fast): START targetUser=node({id}), currentUser=node({current}) MATCH targetUser-[contact:InContactsRelation]->n, n-[:InLocationRelation]->l, n-[:InCategoryRelation]->c WITH currentUser, targetUser,n, l,c, contact.fav is not null as inFavorites MATCH n<-[followers?:InContactsRelation]-() WITH currentUser, targetUser,n, l,c,inFavorites, COUNT(followers) as numFollowers RETURN id(n) as id, n.name? as name, n.title? as title, n._class as _class, n.avatar? as avatar, n.avatar_type? as avatar_type, l.name as location__name, c.name as category__name, true as isInContacts, inFavorites as isInFavorites, numFollowers it runs in ~1s-3s(for first run) and ~1s-70ms (for consecutive and it depends on query) and there is about 5-10 queries runs for each impression. Another interesting behavior is when i try run query from console(neo4j) on my local machine many consecutive times(just press ctrl+enter for few seconds) it has almost constant execution time but when i do it on server it goes slower exponentially and i guess it somehow related with my problem. Problem: So my problem is that neo4j is very CPU greedy(for 24 core machine its may be not an issue but its obviously overkill for small project). First time i used AWS EC2 m1.large instance but over all performance was bad, during testing, CPU always was over 100%. Some relevant parts of configuration: neostore.nodestore.db.mapped_memory=1280M wrapper.java.maxmemory=8192 note: I already tried configuration where all memory related parameters where HIGH and it didn't worked(no change at all). Question: Where to digg? configuration? scheme? queries? what i'm doing wrong? if need more info(logs, configs) just ask ;)

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• Improving performance on data pasting 2000 rows with validations

  - by Lohit

  I have N rows (which could be nothing less than 1000) on an excel spreadsheet. And in this sheet our project has 150 columns like this: Now, our application needs data to be copied (using normal Ctrl+C) and pasted (using Ctrl+V) from the excel file sheet on our GUI sheet. Copy pasting 1000 records takes around 5-6 seconds which is okay for our requirement, but the problem is when we need to make sure the data entered is valid. So we have to validate data in each row generate appropriate error messages and format the data as per requirement. So we need to at runtime parse and evaluate data in each row. Now all the formatting of data and validations come from the back-end database and we have it in a data-table (dtValidateAndFormatConditions). The conditions would be around 50. So you can see how slow this whole process becomes since N X 150 X 50 operations are required to complete this whole process. Initially it took approximately 2-3 minutes but now i have reduced it to 20 - 30 seconds. However i have increased the speed by making an expression parser of my own - and not by any algorithm, is there any other way i can improve performance, by using Divide and Conquer or some other mechanism. Currently i am not really sure how to go about this. Here is what part of my code looks like: public virtual void ValidateAndFormatOnCopyPaste(DataTable DtCopied, int CurRow) { foreach (DataRow dRow in dtValidateAndFormatConditions.Rows) { string Condition = dRow["Condition"]; string FormatValue = Value = dRow["Value"]; GetValidatedFormattedData(DtCopied,ref Condition, ref FormatValue ,iRowIndex); Condition = Parse(Condition); dRow["Condition"] = Condition; FormatValue = Parse(FormatValue ); dRow["Value"] = FormatValue; } } The above code gets called row-wise like this: public override void ValidateAndFormat(DataTable dtChangedRecords, CellRange cr) { int iRowStart = cr.Row, iRowEnd = cr.Row + cr.RowCount; for (int iRow = iRowStart; iRow < iRowEnd; iRow++) { ValidateAndFormatOnCopyPaste(dtChangedRecords,iRow); } } Please know my question needs a more algorithmic solution than code optimization, however any answers containing code related optimizations will be appreciated as well. (Tagged Linq because although not seen i have been using linq in some parts of my code).

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• Strange performance behaviour

  - by plastilino

  I'm puzzled with this. In my machine Direct calculation: 375 ms Method calculation: 3594 ms, about TEN times SLOWER If I place the method calulation BEFORE the direct calculation, both times are SIMILAR. Woud you check it in your machine? class Test { static long COUNT = 50000 * 10000; private static long BEFORE; /*--------METHOD---------*/ public static final double hypotenuse(double a, double b) { return Math.sqrt(a * a + b * b); } /*--------TIMER---------*/ public static void getTime(String text) { if (BEFORE == 0) { BEFORE = System.currentTimeMillis(); return; } long now = System.currentTimeMillis(); long elapsed = (now - BEFORE); BEFORE = System.currentTimeMillis(); if (text.equals("")) { return; } String message = "\r\n" + text + "\r\n" + "Elapsed time: " + elapsed + " ms"; System.out.println(message); } public static void main(String[] args) { double a = 0.2223221101; double b = 122333.167; getTime(""); /*--------DIRECT CALCULATION---------*/ for (int i = 1; i < COUNT; i++) { Math.sqrt(a * a + b * b); } getTime("Direct: "); /*--------METHOD---------*/ for (int k = 1; k < COUNT; k++) { hypotenuse(a, b); } getTime("Method: "); } }

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• runtime error while running test program in cppunit Test framework

  - by user323422

  error is Application has failed to start because MSVCP90D.dll was not found.

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• Is Linq Faster, Slower or the same?

  - by Vaccano

  Is this: Box boxToFind = AllBoxes.Where(box => box.BoxNumber == boxToMatchTo.BagNumber); Faster or slower than this: Box boxToFind ; foreach (Box box in AllBoxes) { if (box.BoxNumber == boxToMatchTo.BoxNumber) { boxToFind = box; } } Both give me the result I am looking for (boxToFind). This is going to run on a mobile device that I need to be performance conscientious of.

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• High accuracy cpu timers

  - by John Robertson

  An expert in highly optimized code once told me that an important part of his strategy was the availability of extremely high performance timers on the CPU. Does anyone know what those are and how one can access them to test various code optimizations? While I am interested regardless, I also wanted to ask whether it is possible to access them from something higher than assembly (or with only a little assembly) via visual studio C++?

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• SQL SERVER – Faster SQL Server Databases and Applications – Power and Control with SafePeak Caching Options

  - by Pinal Dave

  Update: This blog post is written based on the SafePeak, which is available for free download. Today, I’d like to examine more closely one of my preferred technologies for accelerating SQL Server databases, SafePeak. Safepeak’s software provides a variety of advanced data caching options, techniques and tools to accelerate the performance and scalability of SQL Server databases and applications. I’d like to look more closely at some of these options, as some of these capabilities could help you address lagging database and performance on your systems. To better understand the available options, it is best to start by understanding the difference between the usual “Basic Caching” vs. SafePeak’s “Dynamic Caching”. Basic Caching Basic Caching (or the stale and static cache) is an ability to put the results from a query into cache for a certain period of time. It is based on TTL, or Time-to-live, and is designed to stay in cache no matter what happens to the data. For example, although the actual data can be modified due to DML commands (update/insert/delete), the cache will still hold the same obsolete query data. Meaning that with the Basic Caching is really static / stale cache.  As you can tell, this approach has its limitations. Dynamic Caching Dynamic Caching (or the non-stale cache) is an ability to put the results from a query into cache while maintaining the cache transaction awareness looking for possible data modifications. The modifications can come as a result of: DML commands (update/insert/delete), indirect modifications due to triggers on other tables, executions of stored procedures with internal DML commands complex cases of stored procedures with multiple levels of internal stored procedures logic. When data modification commands arrive, the caching system identifies the related cache items and evicts them from cache immediately. In the dynamic caching option the TTL setting still exists, although its importance is reduced, since the main factor for cache invalidation (or cache eviction) become the actual data updates commands. Now that we have a basic understanding of the differences between “basic” and “dynamic” caching, let’s dive in deeper. SafePeak: A comprehensive and versatile caching platform SafePeak comes with a wide range of caching options. Some of SafePeak’s caching options are automated, while others require manual configuration. Together they provide a complete solution for IT and Data managers to reach excellent performance acceleration and application scalability for  a wide range of business cases and applications. Automated caching of SQL Queries: Fully/semi-automated caching of all “read” SQL queries, containing any types of data, including Blobs, XMLs, Texts as well as all other standard data types. SafePeak automatically analyzes the incoming queries, categorizes them into SQL Patterns, identifying directly and indirectly accessed tables, views, functions and stored procedures; Automated caching of Stored Procedures: Fully or semi-automated caching of all read” stored procedures, including procedures with complex sub-procedure logic as well as procedures with complex dynamic SQL code. All procedures are analyzed in advance by SafePeak’s  Metadata-Learning process, their SQL schemas are parsed – resulting with a full understanding of the underlying code, objects dependencies (tables, views, functions, sub-procedures) enabling automated or semi-automated (manually review and activate by a mouse-click) cache activation, with full understanding of the transaction logic for cache real-time invalidation; Transaction aware cache: Automated cache awareness for SQL transactions (SQL and in-procs); Dynamic SQL Caching: Procedures with dynamic SQL are pre-parsed, enabling easy cache configuration, eliminating SQL Server load for parsing time and delivering high response time value even in most complicated use-cases; Fully Automated Caching: SQL Patterns (including SQL queries and stored procedures) that are categorized by SafePeak as “read and deterministic” are automatically activated for caching; Semi-Automated Caching: SQL Patterns categorized as “Read and Non deterministic” are patterns of SQL queries and stored procedures that contain reference to non-deterministic functions, like getdate(). Such SQL Patterns are reviewed by the SafePeak administrator and in usually most of them are activated manually for caching (point and click activation); Fully Dynamic Caching: Automated detection of all dependent tables in each SQL Pattern, with automated real-time eviction of the relevant cache items in the event of “write” commands (a DML or a stored procedure) to one of relevant tables. A default setting; Semi Dynamic Caching: A manual cache configuration option enabling reducing the sensitivity of specific SQL Patterns to “write” commands to certain tables/views. An optimization technique relevant for cases when the query data is either known to be static (like archive order details), or when the application sensitivity to fresh data is not critical and can be stale for short period of time (gaining better performance and reduced load); Scheduled Cache Eviction: A manual cache configuration option enabling scheduling SQL Pattern cache eviction based on certain time(s) during a day. A very useful optimization technique when (for example) certain SQL Patterns can be cached but are time sensitive. Example: “select customers that today is their birthday”, an SQL with getdate() function, which can and should be cached, but the data stays relevant only until 00:00 (midnight); Parsing Exceptions Management: Stored procedures that were not fully parsed by SafePeak (due to too complex dynamic SQL or unfamiliar syntax), are signed as “Dynamic Objects” with highest transaction safety settings (such as: Full global cache eviction, DDL Check = lock cache and check for schema changes, and more). The SafePeak solution points the user to the Dynamic Objects that are important for cache effectiveness, provides easy configuration interface, allowing you to improve cache hits and reduce cache global evictions. Usually this is the first configuration in a deployment; Overriding Settings of Stored Procedures: Override the settings of stored procedures (or other object types) for cache optimization. For example, in case a stored procedure SP1 has an “insert” into table T1, it will not be allowed to be cached. However, it is possible that T1 is just a “logging or instrumentation” table left by developers. By overriding the settings a user can allow caching of the problematic stored procedure; Advanced Cache Warm-Up: Creating an XML-based list of queries and stored procedure (with lists of parameters) for periodically automated pre-fetching and caching. An advanced tool allowing you to handle more rare but very performance sensitive queries pre-fetch them into cache allowing high performance for users’ data access; Configuration Driven by Deep SQL Analytics: All SQL queries are continuously logged and analyzed, providing users with deep SQL Analytics and Performance Monitoring. Reduce troubleshooting from days to minutes with database objects and SQL Patterns heat-map. The performance driven configuration helps you to focus on the most important settings that bring you the highest performance gains. Use of SafePeak SQL Analytics allows continuous performance monitoring and analysis, easy identification of bottlenecks of both real-time and historical data; Cloud Ready: Available for instant deployment on Amazon Web Services (AWS). As you can see, there are many options to configure SafePeak’s SQL Server database and application acceleration caching technology to best fit a lot of situations. If you’re not familiar with their technology, they offer free-trial software you can download that comes with a free “help session” to help get you started. You can access the free trial here. Also, SafePeak is available to use on Amazon Cloud. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Loading PNGs into OpenGL performance issues - Java & JOGL much slower than C# & Tao.OpenGL

  - by Edward Cresswell

  I am noticing a large performance difference between Java & JOGL and C# & Tao.OpenGL when both loading PNGs from storage into memory, and when loading that BufferedImage (java) or Bitmap (C# - both are PNGs on hard drive) 'into' OpenGL. This difference is quite large, so I assumed I was doing something wrong, however after quite a lot of searching and trying different loading techniques I've been unable to reduce this difference. With Java I get an image loaded in 248ms and loaded into OpenGL in 728ms The same on C# takes 54ms to load the image, and 34ms to load/create texture. The image in question above is a PNG containing transparency, sized 7200x255, used for a 2D animated sprite. I realise the size is really quite ridiculous and am considering cutting up the sprite, however the large difference is still there (and confusing). On the Java side the code looks like this: BufferedImage image = ImageIO.read(new File(fileName)); texture = TextureIO.newTexture(image, false); texture.setTexParameteri(GL.GL_TEXTURE_MIN_FILTER, GL.GL_LINEAR); texture.setTexParameteri(GL.GL_TEXTURE_MAG_FILTER, GL.GL_LINEAR); The C# code uses: Bitmap t = new Bitmap(fileName); t.RotateFlip(RotateFlipType.RotateNoneFlipY); Rectangle r = new Rectangle(0, 0, t.Width, t.Height); BitmapData bd = t.LockBits(r, ImageLockMode.ReadOnly, PixelFormat.Format32bppArgb); Gl.glBindTexture(Gl.GL_TEXTURE_2D, tID); Gl.glTexImage2D(Gl.GL_TEXTURE_2D, 0, Gl.GL_RGBA, t.Width, t.Height, 0, Gl.GL_BGRA, Gl.GL_UNSIGNED_BYTE, bd.Scan0); Gl.glTexParameteri(Gl.GL_TEXTURE_2D, Gl.GL_TEXTURE_MIN_FILTER, Gl.GL_LINEAR); Gl.glTexParameteri(Gl.GL_TEXTURE_2D, Gl.GL_TEXTURE_MAG_FILTER, Gl.GL_LINEAR); t.UnlockBits(bd); t.Dispose(); After quite a lot of testing I can only come to the conclusion that Java/JOGL is just slower here - PNG reading might not be as quick, or that I'm still doing something wrong. Thanks. Edit2: I have found that creating a new BufferedImage with format TYPE_INT_ARGB_PRE decreases OpenGL texture load time by almost half - this includes having to create the new BufferedImage, getting the Graphics2D from it and then rendering the previously loaded image to it. Edit3: Benchmark results for 5 variations. I wrote a small benchmarking tool, the following results come from loading a set of 33 pngs, most are very wide, 5 times. testStart: ImageIO.read(file) -> TextureIO.newTexture(image) result: avg = 10250ms, total = 51251 testStart: ImageIO.read(bis) -> TextureIO.newTexture(image) result: avg = 10029ms, total = 50147 testStart: ImageIO.read(file) -> TextureIO.newTexture(argbImage) result: avg = 5343ms, total = 26717 testStart: ImageIO.read(bis) -> TextureIO.newTexture(argbImage) result: avg = 5534ms, total = 27673 testStart: TextureIO.newTexture(file) result: avg = 10395ms, total = 51979 ImageIO.read(bis) refers to the technique described in James Branigan's answer below. argbImage refers to the technique described in my previous edit: img = ImageIO.read(file); argbImg = new BufferedImage(img.getWidth(), img.getHeight(), TYPE_INT_ARGB_PRE); g = argbImg.createGraphics(); g.drawImage(img, 0, 0, null); texture = TextureIO.newTexture(argbImg, false); Any more methods of loading (either images from file, or images to OpenGL) would be appreciated, I will update these benchmarks.

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