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  • HTG Explains: Why Linux Doesn’t Need Defragmenting

    - by Chris Hoffman
    If you’re a Linux user, you’ve probably heard that you don’t need to defragment your Linux file systems. You’ll also notice that Linux distributions don’t come with disk-defragmenting utilities. But why is that? To understand why Linux file systems don’t need defragmenting in normal use – and Windows ones do – you’ll need to understand why fragmentation occurs and how Linux and Windows file systems work differently from each other. HTG Explains: Why Linux Doesn’t Need Defragmenting How to Convert News Feeds to Ebooks with Calibre How To Customize Your Wallpaper with Google Image Searches, RSS Feeds, and More

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  • How does ecryptfs impact harddisk performance?

    - by Freddi
    I have my home directy encrypted with ecryptfs. Does ecryptfs lead to fragmentation? I have the feeling that reading files, displaying folders and login became continuously slower and slower (although it was not noticeably slow at the beginning). The hard disk makes a lot of seek noise even if I open only a text file. In /home/.ecryptfs I see many big archives (that probably contain the encrypted files), so I'm wondering if Linux file system online defragmentation gains anything here. What options do I have to increase performance? Should I decide whether I maybe better do without encryption?

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  • file acess slow after deletion of many files

    - by stefan
    I recently accidentally created millions of files in one folder (rougly 5 million) and due to limitations I couldn't process them correctly (maximum argument count exceeded for wc / ls and such stuff). So I deleted them, which took quite a while, but now they're gone. I deleted the files with a regular rm. It weren't any system files. So the files are definitively deleted, but the system is very slow on file stuff now. ls, cat and auto-complete by pressing tab freezes the terminal for several seconds. Is this some sort of fragmentation issue? Is it an issue with the files beeing still somehow present?

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  • Bring 2 GB Large Pages to Solaris 10

    - by Giri Mandalika
    Few facts: 8 KB is the default page size on Oracle Solaris 10 and 11 as of this writing Both hardware and software must have support for 2 GB large pages SPARC T4 processors are capable of supporting 2 GB pages Oracle Solaris 11 kernel has in-built support for 2 GB pages Oracle Solaris 10 has no default support for 2 GB pages Memory intensive 64-bit applications may benefit the most from using 2 GB pages Prerequisites: OS: Oracle Solaris 10 8/11 (Update 10) or later Hardware: Oracle servers with SPARC T4 processors e.g., SPARC T4-1, T4-2 or T4-4, SPARC SuperCluster T4-4 Steps to enable 2 GB large pages on Oracle Solaris 10: Install the latest kernel patch or ensure that 147440-04 or later was installed Check the patch download instructions Add the following line to /etc/system and reboot set max_uheap_lpsize=0x80000000 Finally check the output of the following command when the system is back online pagesize -a eg., % pagesize -a 8192 <-- 8K 65536 <-- 64K 4194304 <-- 4M 268435456 <-- 256M 2147483648 <-- 2G % uname -a SunOS jar-jar 5.10 Generic_147440-21 sun4v sparc sun4v Also See: Solaris 9 or later: More performance with Large Pages (MPSS) Large page support for instructions (text) in Solaris 10 1/06 Solaris: How To Disable Out Of The Box (OOB) Large Page Support? Memory fragmentation / Large Pages on Solaris x86

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  • Les appareils sous Android seraient plus sujets aux pannes matérielles que ceux sous Windows Phone ou iOS selon WDS

    Les appareils sous Android seraient plus sujets aux pannes matérielles Que Windows Phone et iOS selon WDS Le résultat d'une étude menée par la société WDS montre que les dispositifs sous Android seraient plus victimes de pannes matérielles que les autres. L'enquête a été menée pendant un an en Europe, en Amérique du Nord, en Australie et en Afrique du Sud. Elle s'appuie sur environ 600 000 appels clients et révèle que le taux de pannes des smartphones sous Android s'élèverait à 14%. Selon WDS, les pannes matérielles les plus fréquentes sur les dispositifs Android, seraient dues à la fragmentation de la plate-forme mobile et à son adoption par des constructeurs divers. ...

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  • Google : nouvelles applications Gmail pour Android et iOS et nouveau Youtube pour les iDevices d'Apple

    Gmail : nouvelles applications pour Android et iOS Et nouveau Youtube pour les iDevices d'Apple La fragmentation touche également les applications de Google pour son propre OS mobile. Son nouveau Gmail pour Android - qui vient de sortir - n'est en effet disponible que pour les versions 4.0 et ultérieures (aujourd'hui la 4.1) du système. Parmi les améliorations du client de messagerie mobile, on notera pêle-mêle : un nouvel aperçu des photos dans les mails, la possibilité de mettre une vidéo ou une photo directement en pièce jointe, l'ajustement automatique de la taille de la police à l'écran ou le glisser des messages vers la droite pour les trier ou les supprimer. ...

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  • Which Git-based MIS to track workflow like Trac/Redmine but on console minimastically?

    - by hhh
    Definitions MIS = management information system Some list about console based solutions here and some GUI-hacks here. Been fed up to install all those dependencies and no make -files with GUI -things so which console-based MIS would you suggest for a game-development team with graphical -repo, animation -repo, code -repo, stories -repo, etc ? P.s. I do use Git -submodules and the reason for repo -fragmentation is due to roles and size, certain repos such as graphic -repos tend to be quite large so better to keep them separate. Perhaps useful to readers interested about this http://stackoverflow.com/questions/5881578/trac-vs-redmine https://github.com/jchris/sofa

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  • VDC Research Webcast: Engineering Business Value in the IoT with Java 8

    - by tangelucci
    Date: Thursday, June 19, 2014 Time: 9:30 AM PDT, 12:30 PM EDT, 17:30 GMT The growth of the Internet of Things (IoT) opens up new service-driven opportunities, delivering increased efficiencies, better customer value, and improved quality of life. Realizing the full potential of the Internet of Things requires that we change how we view and build devices. These next-generation systems provide the core foundation of the services, rapidly transforming data to information to value. From healthcare to building control systems to vehicle telematic systems, the IoT focuses on how conneted devices can become more intelligent, enhance interoperability with other devices, systems and services, and drive timely decisions while delivering real business return for all. Join this webcast to learn about: Driving both revenue opportunities and operational efficiencies for the IoT value chain Leveraging Java to make devices more secure How Java can help overcome resource gaps around intelligent connected devices Suggestions on how to better manage fragmentation in embedded devices Register here: http://event.on24.com/r.htm?e=793757&s=1&k=4EA8426D0D31C60A2EDB139635FF75AB

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  • D'après-vous, quels sont les smartphones les plus réussis sous Android ? Participez à notre sondage

    D'après-vous, quels sont les smartphones les plus réussis sous Android ? Participez à notre sondage La fragmentation d'Android est un problème délicat pour les développeurs. D'autant plus qu'il est double : des versions de l'OS pour smartphones d'un coté et pour tablettes de l'autre, et des versions différentes de l'OS au sein de chaque catégorie. A cette complexité s'en ajoute une autre (contrairement à iOS) : la diversité des hardwares. Dont la première pour les développeurs est la différence des tailles d'écran. Enfin (contrairement à Windows Phone dont l'UI (Metro) n'est pas modifiable par les constructeurs), Android permet à chaque industriel de personnaliser l'interfa...

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  • MXMLC Ant task results in java.lang.OutOFMemoryError

    - by Mims H. Wright
    I'm making a change to a set of code for a Flex project that I didn't write and was set up to compile using ant tasks. I assume that the codebase was stable at the last checkin but I'm running into memory issues when trying to build a project using MXMLC and ant (see stack trace below). Before, I was just getting an out of memory error. I tried using a different machine and got this more verbose exception (including problems with the image fetcher). I've tried using various versions of the SDK, I've tried replacing the <mxmlc> tag with <exec executable="mxmlc"> with no luck. Here is my java version in case that has anything to do with it: » java -version java version "1.6.0_20" Java(TM) SE Runtime Environment (build 1.6.0_20-b02-279-10M3065) Java HotSpot(TM) 64-Bit Server VM (build 16.3-b01-279, mixed mode) Any help would be appreciated. Thanks! Buildfile: build.xml compileSWF: [echo] Compiling main.swf... [mxmlc] Loading configuration file /Applications/Adobe Flash Builder 4 Plug-in/sdks/4.0.0beta2/frameworks/flex-config.xml [mxmlc] Exception in thread "Image Fetcher 0" java.lang.OutOfMemoryError: Java heap space [mxmlc] at java.awt.image.PixelGrabber.setDimensions(PixelGrabber.java:360) [mxmlc] at sun.awt.image.ImageDecoder.setDimensions(ImageDecoder.java:62) [mxmlc] at sun.awt.image.JPEGImageDecoder.sendHeaderInfo(JPEGImageDecoder.java:71) [mxmlc] at sun.awt.image.JPEGImageDecoder.readImage(Native Method) [mxmlc] at sun.awt.image.JPEGImageDecoder.produceImage(JPEGImageDecoder.java:119) [mxmlc] at sun.awt.image.InputStreamImageSource.doFetch(InputStreamImageSource.java:246) [mxmlc] at sun.awt.image.ImageFetcher.fetchloop(ImageFetcher.java:172) [mxmlc] at sun.awt.image.ImageFetcher.run(ImageFetcher.java:136) [mxmlc] /src/com/amtrak/components/map/MapAsset.mxml: Error: exception during transcoding: Failed to grab pixels for image /src/assets/embed_assets/images/zoomed_map_wide.jpg [mxmlc] [mxmlc] /src/com/amtrak/components/map/MapAsset.mxml: Error: Unable to transcode /assets/embed_assets/images/zoomed_map_wide.jpg. [mxmlc] [mxmlc] Error: Java heap space [mxmlc] [mxmlc] java.lang.OutOfMemoryError: Java heap space [mxmlc] at java.util.ArrayList.<init>(ArrayList.java:112) [mxmlc] at macromedia.asc.util.ObjectList.<init>(ObjectList.java:30) [mxmlc] at macromedia.asc.parser.ArgumentListNode.<init>(ArgumentListNode.java:30) [mxmlc] at macromedia.asc.parser.NodeFactory.argumentList(NodeFactory.java:116) [mxmlc] at macromedia.asc.parser.NodeFactory.argumentList(NodeFactory.java:97) [mxmlc] at flex2.compiler.mxml.ImplementationGenerator.generateBinding(ImplementationGenerator.java:563) [mxmlc] at flex2.compiler.mxml.ImplementationGenerator.generateBindingsSetupFunction(ImplementationGenerator.java:864) [mxmlc] at flex2.compiler.mxml.ImplementationGenerator.generateBindingsSetup(ImplementationGenerator.java:813) [mxmlc] at flex2.compiler.mxml.ImplementationGenerator.generateInitializerSupportDefs(ImplementationGenerator.java:1813) [mxmlc] at flex2.compiler.mxml.ImplementationGenerator.generateClassDefinition(ImplementationGenerator.java:1005) [mxmlc] at flex2.compiler.mxml.ImplementationGenerator.<init>(ImplementationGenerator.java:201) [mxmlc] at flex2.compiler.mxml.ImplementationCompiler.generateImplementationAST(ImplementationCompiler.java:498) [mxmlc] at flex2.compiler.mxml.ImplementationCompiler.parse1(ImplementationCompiler.java:196) [mxmlc] at flex2.compiler.mxml.MxmlCompiler.parse1(MxmlCompiler.java:168) [mxmlc] at flex2.compiler.CompilerAPI.parse1(CompilerAPI.java:2851) [mxmlc] at flex2.compiler.CompilerAPI.parse1(CompilerAPI.java:2804) [mxmlc] at flex2.compiler.CompilerAPI.batch2(CompilerAPI.java:446) [mxmlc] at flex2.compiler.CompilerAPI.batch(CompilerAPI.java:1274) [mxmlc] at flex2.compiler.CompilerAPI.compile(CompilerAPI.java:1488) [mxmlc] at flex2.compiler.CompilerAPI.compile(CompilerAPI.java:1375) [mxmlc] at flex2.tools.Mxmlc.mxmlc(Mxmlc.java:282) [mxmlc] at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) [mxmlc] at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) [mxmlc] at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) [mxmlc] at java.lang.reflect.Method.invoke(Method.java:597) [mxmlc] at flex.ant.FlexTask.executeInProcess(FlexTask.java:280) [mxmlc] at flex.ant.FlexTask.execute(FlexTask.java:225) [mxmlc] at org.apache.tools.ant.UnknownElement.execute(UnknownElement.java:288) [mxmlc] at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) [mxmlc] at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) [mxmlc] at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) [mxmlc] at java.lang.reflect.Method.invoke(Method.java:597) BUILD FAILED /src/build.xml:49: mxmlc task failed

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  • Have suggestions for these assembly mnemonics?

    - by Noctis Skytower
    Greetings! Last semester in college, my teacher in the Computer Languages class taught us the esoteric language named Whitespace. In the interest of learning the language better with a very busy schedule (midterms), I wrote an interpreter and assembler in Python. An assembly language was designed to facilitate writing programs easily, and a sample program was written with the given assembly mnemonics. Now that it is summer, a new project has begun with the objective being to rewrite the interpreter and assembler for Whitespace 0.3, with further developments coming afterwards. Since there is so much extra time than before to work on its design, you are presented here with an outline that provides a revised set of mnemonics for the assembly language. This post is marked as a wiki for their discussion. Have you ever had any experience with assembly languages in the past? Were there some instructions that you thought should have been renamed to something different? Did you find yourself thinking outside the box and with a different paradigm than in which the mnemonics were named? If you can answer yes to any of those questions, you are most welcome here. Subjective answers are appreciated! Stack Manipulation (IMP: [Space]) Stack manipulation is one of the more common operations, hence the shortness of the IMP [Space]. There are four stack instructions. hold N Push the number onto the stack copy Duplicate the top item on the stack copy N Copy the nth item on the stack (given by the argument) onto the top of the stack swap Swap the top two items on the stack drop Discard the top item on the stack drop N Slide n items off the stack, keeping the top item Arithmetic (IMP: [Tab][Space]) Arithmetic commands operate on the top two items on the stack, and replace them with the result of the operation. The first item pushed is considered to be left of the operator. add Addition sub Subtraction mul Multiplication div Integer Division mod Modulo Heap Access (IMP: [Tab][Tab]) Heap access commands look at the stack to find the address of items to be stored or retrieved. To store an item, push the address then the value and run the store command. To retrieve an item, push the address and run the retrieve command, which will place the value stored in the location at the top of the stack. save Store load Retrieve Flow Control (IMP: [LF]) Flow control operations are also common. Subroutines are marked by labels, as well as the targets of conditional and unconditional jumps, by which loops can be implemented. Programs must be ended by means of [LF][LF][LF] so that the interpreter can exit cleanly. L: Mark a location in the program call L Call a subroutine goto L Jump unconditionally to a label if=0 L Jump to a label if the top of the stack is zero if<0 L Jump to a label if the top of the stack is negative return End a subroutine and transfer control back to the caller halt End the program I/O (IMP: [Tab][LF]) Finally, we need to be able to interact with the user. There are IO instructions for reading and writing numbers and individual characters. With these, string manipulation routines can be written. The read instructions take the heap address in which to store the result from the top of the stack. print chr Output the character at the top of the stack print int Output the number at the top of the stack input chr Read a character and place it in the location given by the top of the stack input int Read a number and place it in the location given by the top of the stack Question: How would you redesign, rewrite, or rename the previous mnemonics and for what reasons?

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  • Are their any suggestions for this new assembly language?

    - by Noctis Skytower
    Greetings! Last semester in college, my teacher in the Computer Languages class taught us the esoteric language named Whitespace. In the interest of learning the language better with a very busy schedule (midterms), I wrote an interpreter and assembler in Python. An assembly language was designed to facilitate writing programs easily, and a sample program was written with the given assembly mnemonics. Now that it is summer, a new project has begun with the objective being to rewrite the interpreter and assembler for Whitespace 0.3, with further developments coming afterwards. Since there is so much extra time than before to work on its design, you are presented here with an outline that provides a revised set of mnemonics for the assembly language. This post is marked as a wiki for their discussion. Have you ever had any experience with assembly languages in the past? Were there some instructions that you thought should have been renamed to something different? Did you find yourself thinking outside the box and with a different paradigm than in which the mnemonics were named? If you can answer yes to any of those questions, you are most welcome here. Subjective answers are appreciated! Stack Manipulation (IMP: [Space]) Stack manipulation is one of the more common operations, hence the shortness of the IMP [Space]. There are four stack instructions. hold N Push the number onto the stack copy Duplicate the top item on the stack copy N Copy the nth item on the stack (given by the argument) onto the top of the stack swap Swap the top two items on the stack drop Discard the top item on the stack drop N Slide n items off the stack, keeping the top item Arithmetic (IMP: [Tab][Space]) Arithmetic commands operate on the top two items on the stack, and replace them with the result of the operation. The first item pushed is considered to be left of the operator. add Addition sub Subtraction mul Multiplication div Integer Division mod Modulo Heap Access (IMP: [Tab][Tab]) Heap access commands look at the stack to find the address of items to be stored or retrieved. To store an item, push the address then the value and run the store command. To retrieve an item, push the address and run the retrieve command, which will place the value stored in the location at the top of the stack. save Store load Retrieve Flow Control (IMP: [LF]) Flow control operations are also common. Subroutines are marked by labels, as well as the targets of conditional and unconditional jumps, by which loops can be implemented. Programs must be ended by means of [LF][LF][LF] so that the interpreter can exit cleanly. L: Mark a location in the program call L Call a subroutine goto L Jump unconditionally to a label if=0 L Jump to a label if the top of the stack is zero if<0 L Jump to a label if the top of the stack is negative return End a subroutine and transfer control back to the caller exit End the program I/O (IMP: [Tab][LF]) Finally, we need to be able to interact with the user. There are IO instructions for reading and writing numbers and individual characters. With these, string manipulation routines can be written. The read instructions take the heap address in which to store the result from the top of the stack. print chr Output the character at the top of the stack print int Output the number at the top of the stack input chr Read a character and place it in the location given by the top of the stack input int Read a number and place it in the location given by the top of the stack Question: How would you redesign, rewrite, or rename the previous mnemonics and for what reasons?

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  • Are there any suggestions for these new assembly mnemonics?

    - by Noctis Skytower
    Greetings! Last semester in college, my teacher in the Computer Languages class taught us the esoteric language named Whitespace. In the interest of learning the language better with a very busy schedule (midterms), I wrote an interpreter and assembler in Python. An assembly language was designed to facilitate writing programs easily, and a sample program was written with the given assembly mnemonics. Now that it is summer, a new project has begun with the objective being to rewrite the interpreter and assembler for Whitespace 0.3, with further developments coming afterwards. Since there is so much extra time than before to work on its design, you are presented here with an outline that provides a revised set of mnemonics for the assembly language. This post is marked as a wiki for their discussion. Have you ever had any experience with assembly languages in the past? Were there some instructions that you thought should have been renamed to something different? Did you find yourself thinking outside the box and with a different paradigm than in which the mnemonics were named? If you can answer yes to any of those questions, you are most welcome here. Subjective answers are appreciated! Stack Manipulation (IMP: [Space]) Stack manipulation is one of the more common operations, hence the shortness of the IMP [Space]. There are four stack instructions. hold N Push the number onto the stack copy Duplicate the top item on the stack copy N Copy the nth item on the stack (given by the argument) onto the top of the stack swap Swap the top two items on the stack drop Discard the top item on the stack drop N Slide n items off the stack, keeping the top item Arithmetic (IMP: [Tab][Space]) Arithmetic commands operate on the top two items on the stack, and replace them with the result of the operation. The first item pushed is considered to be left of the operator. add Addition sub Subtraction mul Multiplication div Integer Division mod Modulo Heap Access (IMP: [Tab][Tab]) Heap access commands look at the stack to find the address of items to be stored or retrieved. To store an item, push the address then the value and run the store command. To retrieve an item, push the address and run the retrieve command, which will place the value stored in the location at the top of the stack. save Store load Retrieve Flow Control (IMP: [LF]) Flow control operations are also common. Subroutines are marked by labels, as well as the targets of conditional and unconditional jumps, by which loops can be implemented. Programs must be ended by means of [LF][LF][LF] so that the interpreter can exit cleanly. L: Mark a location in the program call L Call a subroutine goto L Jump unconditionally to a label if=0 L Jump to a label if the top of the stack is zero if<0 L Jump to a label if the top of the stack is negative return End a subroutine and transfer control back to the caller halt End the program I/O (IMP: [Tab][LF]) Finally, we need to be able to interact with the user. There are IO instructions for reading and writing numbers and individual characters. With these, string manipulation routines can be written. The read instructions take the heap address in which to store the result from the top of the stack. print chr Output the character at the top of the stack print int Output the number at the top of the stack input chr Read a character and place it in the location given by the top of the stack input int Read a number and place it in the location given by the top of the stack Question: How would you redesign, rewrite, or rename the previous mnemonics and for what reasons?

    Read the article

  • Do you have suggestions for these assembly mnemonics?

    - by Noctis Skytower
    Greetings! Last semester in college, my teacher in the Computer Languages class taught us the esoteric language named Whitespace. In the interest of learning the language better with a very busy schedule (midterms), I wrote an interpreter and assembler in Python. An assembly language was designed to facilitate writing programs easily, and a sample program was written with the given assembly mnemonics. Now that it is summer, a new project has begun with the objective being to rewrite the interpreter and assembler for Whitespace 0.3, with further developments coming afterwards. Since there is so much extra time than before to work on its design, you are presented here with an outline that provides a revised set of mnemonics for the assembly language. This post is marked as a wiki for their discussion. Have you ever had any experience with assembly languages in the past? Were there some instructions that you thought should have been renamed to something different? Did you find yourself thinking outside the box and with a different paradigm than in which the mnemonics were named? If you can answer yes to any of those questions, you are most welcome here. Subjective answers are appreciated! Stack Manipulation (IMP: [Space]) Stack manipulation is one of the more common operations, hence the shortness of the IMP [Space]. There are four stack instructions. hold N Push the number onto the stack copy Duplicate the top item on the stack copy N Copy the nth item on the stack (given by the argument) onto the top of the stack swap Swap the top two items on the stack drop Discard the top item on the stack drop N Slide n items off the stack, keeping the top item Arithmetic (IMP: [Tab][Space]) Arithmetic commands operate on the top two items on the stack, and replace them with the result of the operation. The first item pushed is considered to be left of the operator. add Addition sub Subtraction mul Multiplication div Integer Division mod Modulo Heap Access (IMP: [Tab][Tab]) Heap access commands look at the stack to find the address of items to be stored or retrieved. To store an item, push the address then the value and run the store command. To retrieve an item, push the address and run the retrieve command, which will place the value stored in the location at the top of the stack. save Store load Retrieve Flow Control (IMP: [LF]) Flow control operations are also common. Subroutines are marked by labels, as well as the targets of conditional and unconditional jumps, by which loops can be implemented. Programs must be ended by means of [LF][LF][LF] so that the interpreter can exit cleanly. L: Mark a location in the program call L Call a subroutine goto L Jump unconditionally to a label if=0 L Jump to a label if the top of the stack is zero if<0 L Jump to a label if the top of the stack is negative return End a subroutine and transfer control back to the caller halt End the program I/O (IMP: [Tab][LF]) Finally, we need to be able to interact with the user. There are IO instructions for reading and writing numbers and individual characters. With these, string manipulation routines can be written. The read instructions take the heap address in which to store the result from the top of the stack. print chr Output the character at the top of the stack print int Output the number at the top of the stack input chr Read a character and place it in the location given by the top of the stack input int Read a number and place it in the location given by the top of the stack Question: How would you redesign, rewrite, or rename the previous mnemonics and for what reasons?

    Read the article

  • How to safely operate on parameters in threads, using C++ & Pthreads?

    - by ChrisCphDK
    Hi. I'm having some trouble with a program using pthreads, where occassional crashes occur, that could be related to how the threads operate on data So I have some basic questions about how to program using threads, and memory layout: Assume that a public class function performs some operations on some strings, and returns the result as a string. The prototype of the function could be like this: std::string SomeClass::somefunc(const std::string &strOne, const std::string &strTwo) { //Error checking of strings have been omitted std::string result = strOne.substr(0,5) + strTwo.substr(0,5); return result; } Is it correct to assume that strings, being dynamic, are stored on the heap, but that a reference to the string is allocated on the stack at runtime? Stack: [Some mem addr] pointer address to where the string is on the heap Heap: [Some mem addr] memory allocated for the initial string which may grow or shrink To make the function thread safe, the function is extended with the following mutex (which is declared as private in the "SomeClass") locking: std::string SomeClass::somefunc(const std::string &strOne, const std::string &strTwo) { pthread_mutex_lock(&someclasslock); //Error checking of strings have been omitted std::string result = strOne.substr(0,5) + strTwo.substr(0,5); pthread_mutex_unlock(&someclasslock); return result; } Is this a safe way of locking down the operations being done on the strings (all three), or could a thread be stopped by the scheduler in the following cases, which I'd assume would mess up the intended logic: a. Right after the function is called, and the parameters: strOne & strTwo have been set in the two reference pointers that the function has on the stack, the scheduler takes away processing time for the thread and lets a new thread in, which overwrites the reference pointers to the function, which then again gets stopped by the scheduler, letting the first thread back in? b. Can the same occur with the "result" string: the first string builds the result, unlocks the mutex, but before returning the scheduler lets in another thread which performs all of it's work, overwriting the result etc. Or are the reference parameters / result string being pushed onto the stack while another thread is doing performing it's task? Is the safe / correct way of doing this in threads, and "returning" a result, to pass a reference to a string that will be filled with the result instead: void SomeClass::somefunc(const std::string &strOne, const std::string &strTwo, std::string result) { pthread_mutex_lock(&someclasslock); //Error checking of strings have been omitted result = strOne.substr(0,5) + strTwo.substr(0,5); pthread_mutex_unlock(&someclasslock); } The intended logic is that several objects of the "SomeClass" class creates new threads and passes objects of themselves as parameters, and then calls the function: "someFunc": int SomeClass::startNewThread() { pthread_attr_t attr; pthread_t pThreadID; if(pthread_attr_init(&attr) != 0) return -1; if(pthread_attr_setdetachstate(&attr, PTHREAD_CREATE_DETACHED) != 0) return -2; if(pthread_create(&pThreadID, &attr, proxyThreadFunc, this) != 0) return -3; if(pthread_attr_destroy(&attr) != 0) return -4; return 0; } void* proxyThreadFunc(void* someClassObjPtr) { return static_cast<SomeClass*> (someClassObjPtr)->somefunc("long string","long string"); } Sorry for the long description. But I hope the questions and intended purpose is clear, if not let me know and I'll elaborate. Best regards. Chris

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  • Inappropriate Updates?

    - by Tony Davis
    A recent Simple-talk article by Kathi Kellenberger dissected the fastest SQL solution, submitted by Peter Larsson as part of Phil Factor's SQL Speed Phreak challenge, to the classic "running total" problem. In its analysis of the code, the article re-ignited a heated debate regarding the techniques that should, and should not, be deemed acceptable in your search for fast SQL code. Peter's code for running total calculation uses a variation of a somewhat contentious technique, sometimes referred to as a "quirky update": SET @Subscribers = Subscribers = @Subscribers + PeopleJoined - PeopleLeft This form of the UPDATE statement, @variable = column = expression, is documented and it allows you to set a variable to the value returned by the expression. Microsoft does not guarantee the order in which rows are updated in this technique because, in relational theory, a table doesn’t have a natural order to its rows and the UPDATE statement has no means of specifying the order. Traditionally, in cases where a specific order is requires, such as for running aggregate calculations, programmers who used the technique have relied on the fact that the UPDATE statement, without the WHERE clause, is executed in the order imposed by the clustered index, or in heap order, if there isn’t one. Peter wasn’t satisfied with this, and so used the ingenious device of assuring the order of the UPDATE by the use of an "ordered CTE", based on an underlying temporary staging table (a heap). However, in either case, the ordering is still not guaranteed and, in addition, would be broken under conditions of parallelism, or partitioning. Many argue, with validity, that this reliance on a given order where none can ever be guaranteed is an abuse of basic relational principles, and so is a bad practice; perhaps even irresponsible. More importantly, Microsoft doesn't wish to support the technique and offers no guarantee that it will always work. If you put it into production and it breaks in a later version, you can't file a bug. As such, many believe that the technique should never be tolerated in a production system, under any circumstances. Is this attitude justified? After all, both forms of the technique, using a clustered index to guarantee the order or using an ordered CTE, have been tested rigorously and are proven to be robust; although not guaranteed by Microsoft, the ordering is reliable, provided none of the conditions that are known to break it are violated. In Peter's particular case, the technique is being applied to a temporary table, where the developer has full control of the data ordering, and indexing, and knows that the table will never be subject to parallelism or partitioning. It might be argued that, in such circumstances, the technique is not really "quirky" at all and to ban it from your systems would server no real purpose other than to deprive yourself of a reliable technique that has uses that extend well beyond the running total calculations. Of course, it is doubly important that such a technique, including its unsupported status and the assumptions that underpin its success, is fully and clearly documented, preferably even when posting it online in a competition or forum post. Ultimately, however, this technique has been available to programmers throughout the time Sybase and SQL Server has existed, and so cannot be lightly cast aside, even if one sympathises with Microsoft for the awkwardness of maintaining an archaic way of doing updates. After all, a Table hint could easily be devised that, if specified in the WITH (<Table_Hint_Limited>) clause, could be used to request the database engine to do the update in the conventional order. Then perhaps everyone would be satisfied. Cheers, Tony.

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  • Anatomy of a .NET Assembly - Signature encodings

    - by Simon Cooper
    If you've just joined this series, I highly recommend you read the previous posts in this series, starting here, or at least these posts, covering the CLR metadata tables. Before we look at custom attribute encoding, we first need to have a brief look at how signatures are encoded in an assembly in general. Signature types There are several types of signatures in an assembly, all of which share a common base representation, and are all stored as binary blobs in the #Blob heap, referenced by an offset from various metadata tables. The types of signatures are: Method definition and method reference signatures. Field signatures Property signatures Method local variables. These are referenced from the StandAloneSig table, which is then referenced by method body headers. Generic type specifications. These represent a particular instantiation of a generic type. Generic method specifications. Similarly, these represent a particular instantiation of a generic method. All these signatures share the same underlying mechanism to represent a type Representing a type All metadata signatures are based around the ELEMENT_TYPE structure. This assigns a number to each 'built-in' type in the framework; for example, Uint16 is 0x07, String is 0x0e, and Object is 0x1c. Byte codes are also used to indicate SzArrays, multi-dimensional arrays, custom types, and generic type and method variables. However, these require some further information. Firstly, custom types (ie not one of the built-in types). These require you to specify the 4-byte TypeDefOrRef coded token after the CLASS (0x12) or VALUETYPE (0x11) element type. This 4-byte value is stored in a compressed format before being written out to disk (for more excruciating details, you can refer to the CLI specification). SzArrays simply have the array item type after the SZARRAY byte (0x1d). Multidimensional arrays follow the ARRAY element type with a series of compressed integers indicating the number of dimensions, and the size and lower bound of each dimension. Generic variables are simply followed by the index of the generic variable they refer to. There are other additions as well, for example, a specific byte value indicates a method parameter passed by reference (BYREF), and other values indicating custom modifiers. Some examples... To demonstrate, here's a few examples and what the resulting blobs in the #Blob heap will look like. Each name in capitals corresponds to a particular byte value in the ELEMENT_TYPE or CALLCONV structure, and coded tokens to custom types are represented by the type name in curly brackets. A simple field: int intField; FIELD I4 A field of an array of a generic type parameter (assuming T is the first generic parameter of the containing type): T[] genArrayField FIELD SZARRAY VAR 0 An instance method signature (note how the number of parameters does not include the return type): instance string MyMethod(MyType, int&, bool[][]); HASTHIS DEFAULT 3 STRING CLASS {MyType} BYREF I4 SZARRAY SZARRAY BOOLEAN A generic type instantiation: MyGenericType<MyType, MyStruct> GENERICINST CLASS {MyGenericType} 2 CLASS {MyType} VALUETYPE {MyStruct} For more complicated examples, in the following C# type declaration: GenericType<T> : GenericBaseType<object[], T, GenericType<T>> { ... } the Extends field of the TypeDef for GenericType will point to a TypeSpec with the following blob: GENERICINST CLASS {GenericBaseType} 3 SZARRAY OBJECT VAR 0 GENERICINST CLASS {GenericType} 1 VAR 0 And a static generic method signature (generic parameters on types are referenced using VAR, generic parameters on methods using MVAR): TResult[] GenericMethod<TInput, TResult>( TInput, System.Converter<TInput, TOutput>); GENERIC 2 2 SZARRAY MVAR 1 MVAR 0 GENERICINST CLASS {System.Converter} 2 MVAR 0 MVAR 1 As you can see, complicated signatures are recursively built up out of quite simple building blocks to represent all the possible variations in a .NET assembly. Now we've looked at the basics of normal method signatures, in my next post I'll look at custom attribute application signatures, and how they are different to normal signatures.

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  • Talking JavaOne with Rock Star Kirk Pepperdine

    - by Janice J. Heiss
    Kirk Pepperdine is not only a JavaOne Rock Star but a Java Champion and a highly regarded expert in Java performance tuning who works as a consultant, educator, and author. He is the principal consultant at Kodewerk Ltd. He speaks frequently at conferences and co-authored the Ant Developer's Handbook. In the rapidly shifting world of information technology, Pepperdine, as much as anyone, keeps up with what's happening with Java performance tuning. Pepperdine will participate in the following sessions: CON5405 - Are Your Garbage Collection Logs Speaking to You? BOF6540 - Java Champions and JUG Leaders Meet Oracle Executives (with Jeff Genender, Mattias Karlsson, Henrik Stahl, Georges Saab) HOL6500 - Finding and Solving Java Deadlocks (with Heinz Kabutz, Ellen Kraffmiller Martijn Verburg, Jeff Genender, and Henri Tremblay) I asked him what technological changes need to be taken into account in performance tuning. “The volume of data we're dealing with just seems to be getting bigger and bigger all the time,” observed Pepperdine. “A couple of years ago you'd never think of needing a heap that was 64g, but today there are deployments where the heap has grown to 256g and tomorrow there are plans for heaps that are even larger. Dealing with all that data simply requires more horse power and some very specialized techniques. In some cases, teams are trying to push hardware to the breaking point. Under those conditions, you need to be very clever just to get things to work -- let alone to get them to be fast. We are very quickly moving from a world where everything happens in a transaction to one where if you were to even consider using a transaction, you've lost." When asked about the greatest misconceptions about performance tuning that he currently encounters, he said, “If you have a performance problem, you should start looking at code at the very least and for that extra step, whip out an execution profiler. I'm not going to say that I never use execution profilers or look at code. What I will say is that execution profilers are effective for a small subset of performance problems and code is literally the last thing you should look at.And what is the most exciting thing happening in the world of Java today? “Interesting question because so many people would say that nothing exciting is happening in Java. Some might be disappointed that a few features have slipped in terms of scheduling. But I'd disagree with the first group and I'm not so concerned about the slippage because I still see a lot of exciting things happening. First, lambda will finally be with us and with lambda will come better ways.” For JavaOne, he is proctoring for Heinz Kabutz's lab. “I'm actually looking forward to that more than I am to my own talk,” he remarked. “Heinz will be the third non-Sun/Oracle employee to present a lab and the first since Oracle began hosting JavaOne. He's got a great message. He's spent a ton of time making sure things are going to work, and we've got a great team of proctors to help out. After that, getting my talk done, the Java Champion's panel session and then kicking back and just meeting up and talking to some Java heads."Finally, what should Java developers know that they currently do not know? “’Write Once, Run Everywhere’ is a great slogan and Java has come closer to that dream than any other technology stack that I've used. That said, different hardware bits work differently and as hard as we try, the JVM can't hide all the differences. Plus, if we are to get good performance we need to work with our hardware and not against it. All this implies that Java developers need to know more about the hardware they are deploying to.” Originally published on blogs.oracle.com/javaone.

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  • Talking JavaOne with Rock Star Kirk Pepperdine

    - by Janice J. Heiss
    Kirk Pepperdine is not only a JavaOne Rock Star but a Java Champion and a highly regarded expert in Java performance tuning who works as a consultant, educator, and author. He is the principal consultant at Kodewerk Ltd. He speaks frequently at conferences and co-authored the Ant Developer's Handbook. In the rapidly shifting world of information technology, Pepperdine, as much as anyone, keeps up with what's happening with Java performance tuning. Pepperdine will participate in the following sessions: CON5405 - Are Your Garbage Collection Logs Speaking to You? BOF6540 - Java Champions and JUG Leaders Meet Oracle Executives (with Jeff Genender, Mattias Karlsson, Henrik Stahl, Georges Saab) HOL6500 - Finding and Solving Java Deadlocks (with Heinz Kabutz, Ellen Kraffmiller Martijn Verburg, Jeff Genender, and Henri Tremblay) I asked him what technological changes need to be taken into account in performance tuning. “The volume of data we're dealing with just seems to be getting bigger and bigger all the time,” observed Pepperdine. “A couple of years ago you'd never think of needing a heap that was 64g, but today there are deployments where the heap has grown to 256g and tomorrow there are plans for heaps that are even larger. Dealing with all that data simply requires more horse power and some very specialized techniques. In some cases, teams are trying to push hardware to the breaking point. Under those conditions, you need to be very clever just to get things to work -- let alone to get them to be fast. We are very quickly moving from a world where everything happens in a transaction to one where if you were to even consider using a transaction, you've lost." When asked about the greatest misconceptions about performance tuning that he currently encounters, he said, “If you have a performance problem, you should start looking at code at the very least and for that extra step, whip out an execution profiler. I'm not going to say that I never use execution profilers or look at code. What I will say is that execution profilers are effective for a small subset of performance problems and code is literally the last thing you should look at.And what is the most exciting thing happening in the world of Java today? “Interesting question because so many people would say that nothing exciting is happening in Java. Some might be disappointed that a few features have slipped in terms of scheduling. But I'd disagree with the first group and I'm not so concerned about the slippage because I still see a lot of exciting things happening. First, lambda will finally be with us and with lambda will come better ways.” For JavaOne, he is proctoring for Heinz Kabutz's lab. “I'm actually looking forward to that more than I am to my own talk,” he remarked. “Heinz will be the third non-Sun/Oracle employee to present a lab and the first since Oracle began hosting JavaOne. He's got a great message. He's spent a ton of time making sure things are going to work, and we've got a great team of proctors to help out. After that, getting my talk done, the Java Champion's panel session and then kicking back and just meeting up and talking to some Java heads."Finally, what should Java developers know that they currently do not know? “’Write Once, Run Everywhere’ is a great slogan and Java has come closer to that dream than any other technology stack that I've used. That said, different hardware bits work differently and as hard as we try, the JVM can't hide all the differences. Plus, if we are to get good performance we need to work with our hardware and not against it. All this implies that Java developers need to know more about the hardware they are deploying to.”

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  • "Chunked" MemoryStream

    - by Karol Kolenda
    I'm looking for the implementation of MemoryStream which does not allocate memory as one big block, but rather a collection of chunks. I want to store a few GB of data in memory (64 bit) and avoid limitation of memory fragmentation.

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  • Reconstructing data from PCAP sniff

    - by Ishi
    Hi everyone !! I am trying to sniff HTTP data through libpcap and get all the http contents (header+payload) after processing the TCP payload. As per my discussion at http://stackoverflow.com/questions/2905430/writing-an-http-sniffer-or-any-other-application-level-sniffer , I am facing problems due to fragmentation - I need to reconstruct the whole stream (or defragment it) to get a complete HTTP packet, and this is where I need some help. Thanks in anticipation !!

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  • Shrink Sql Server database

    - by hani
    My SQL Server 2008 database file (.mdf) file is nearly 24 MB but the log file grown upto 15 GB. If I want to shrink database what are the important points to take into consideration? Will shrink causes any index fragmentation and does it affect my database performance?

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  • How to I serialize a large graph of .NET object into a SQL Server BLOB without creating a large bu

    - by Ian Ringrose
    We have code like: ms = New IO.MemoryStream bin = New System.Runtime.Serialization.Formatters.Binary.BinaryFormatter bin.Serialize(ms, largeGraphOfObjects) dataToSaveToDatabase = ms.ToArray() // put dataToSaveToDatabase in a Sql server BLOB But the memory steam allocates a large buffer from the large memory heap that is giving us problems. So how can we stream the data without needing enough free memory to hold the serialized objects. I am looking for a way to get a Stream from SQL server that can then be passed to bin.Serialize() so avoiding keeping all the data in my processes memory. Likewise for reading the data back... Some more background. This is part of a complex numerical processing system that processes data in near real time looking for equipment problems etc, the serialization is done to allow a restart when there is a problem with data quality from a data feed etc. (We store the data feeds and can rerun them after the operator has edited out bad values.) Therefore we serialize the object a lot more often then we de-serialize them. The objects we are serializing include very large arrays mostly of doubles as well as a lot of small “more normal” objects. We are pushing the memory limit on a 32 bit system and make the garage collector work very hard. (Effects are being made elsewhere in the system to improve this, e.g. reusing large arrays rather then create new arrays.) Often the serialization of the state is the last straw that courses an out of memory exception; our peak memory usage is while this serialization is being done. I think we get large memory pool fragmentation when we de-serialize the object, I expect there are also other problem with large memory pool fragmentation given the size of the arrays. (This has not yet been investigated, as the person that first looked at this is a numerical processing expert, not a memory management expert.) Are customers use a mix of Sql Server 2000, 2005 and 2008 and we would rather not have different code paths for each version of Sql Server if possible. We can have many active models at a time (in different process, across many machines), each model can have many saved states. Hence the saved state is stored in a database blob rather then a file. As the spread of saving the state is important, I would rather not serialize the object to a file, and then put the file in a BLOB one block at a time. Other related questions I have asked How to Stream data from/to SQL Server BLOB fields? Is there a SqlFileStream like class that works with Sql Server 2005?

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  • Sequential GUID in Linq-to-Sql?

    - by JacobE
    I just read a blog post about NHibernate's ability to create a GUID from the system time (Guid.Comb), thus avoiding a good amount of database fragmentation. You could call it the client-side equivalent to the SQL Server Sequential ID. Is there a way I could use a similar strategy in my Linq-to-Sql project (by generating the Guid in code)?

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