Search Results

Search found 16731 results on 670 pages for 'memory limit'.

Page 60/670 | < Previous Page | 56 57 58 59 60 61 62 63 64 65 66 67  | Next Page >

  • 64kb limit on the size of MSMQ Multicast Messages

    - by John Breakwell
    When Windows 2003 came out, Microsoft introduced the ability to broadcast messages to any machines that were listening back. All you had to do was send out a message on a particular port and IP address and any client that had set up a Multicast queue with matching port and IP address would get a copy. Since its introduction, there have been a couple of security vulnerabilities that needed to be removed: Microsoft Security Bulletin MS06-052 Vulnerability in Pragmatic General Multicast (PGM) Could Allow Remote Code Execution (919007) Microsoft Security Bulletin MS08-036 Vulnerabilities in Pragmatic General Multicast (PGM) could allow denial of service (950762) The second of these, MS08-036, was resolved through an undocumented change in functionality. Basically, a limit of 64kb was put on the maximum size of a message that could be broadcast using the Multicast method. Obviously this has caused a few problems for any existing MSMQ Multicast applications that expected to be able to send larger messages. A hotfix has been developed to resolve this problem. 961605 FIX: Multicast messages larger than 64 kilobytes (KB) are not delivered as expected by using Message Queuing 3.0 after security update MS08-036 is installed A registry change is required: Open the registry with Regedit Navigate to HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\RMCAST\Parameters\ Create a DWord called MaxpacketSize Set the value to the desired number of bytes. You can set it to a value between zero and 4MB. If you specify anything above 4MB, it will default to 64K. A reboot is needed after adding this value.

    Read the article

  • How do I know if my PHP application is using too much memory?

    - by John
    I'm working on a PHP web application that let's users network with each other, book events, message etc... I launched it a few months ago and at the moment, there's only about 100 users. I set up the application on a VPS with ubuntu 9.10, apache 2, mysql 5 and php 5. I had 360 Mb of RAM, but upgraded to 720 MB a few minutes ago. Lately, my web application has been experiencing outages due to excessive memory usage. From what I can tell in error logs, it seems the server automatically kills apache processes that consume too much memory. As a result, I upgraded memory from 360 MB to 720 MB as a stop-gap measure. So my question is, how do I go about resolving these outage issues? How do I know if my website's need for more memory is due to poor code or if it's part of the website's natural growth? What's the most efficient way to determine which PHP scripts consume the most memory?

    Read the article

  • Are spinlocks a good choice for a memory allocator?

    - by dsimcha
    I've suggested to the maintainers of the D programming language runtime a few times that the memory allocator/garbage collector should use spinlocks instead of regular OS critical sections. This hasn't really caught on. Here are the reasons I think spinlocks would be better: At least in synthetic benchmarks that I did, it's several times faster than OS critical sections when there's contention for the memory allocator/GC lock. Edit: Empirically, using spinlocks didn't even have measurable overhead in a single-core environment, probably because locks need to be held for such a short period of time in a memory allocator. Memory allocations and similar operations usually take a small fraction of a timeslice, and even a small fraction of the time a context switch takes, making it silly to context switch in the case of contention. A garbage collection in the implementation in question stops the world anyhow. There won't be any spinning during a collection. Are there any good reasons not to use spinlocks in a memory allocator/garbage collector implementation?

    Read the article

  • Why do dicts of defaultdict(int)'s use so much memory? (and other simple python performance question

    - by dukhat
    import numpy as num from collections import defaultdict topKeys = range(16384) keys = range(8192) table = dict((k,defaultdict(int)) for k in topKeys) dat = num.zeros((16384,8192), dtype="int32") print "looping begins" #how much memory should this use? I think it shouldn't use more that a few #times the memory required to hold (16384*8192) int32's (512 mb), but #it uses 11 GB! for k in topKeys: for j in keys: dat[k,j] = table[k][j] print "done" What is going on here? Furthermore, this similar script takes eons to run compared to the first one, and also uses an absurd quantity of memory. topKeys = range(16384) keys = range(8192) table = [(j,0) for k in topKeys for j in keys] I guess python ints might be 64 bit ints, which would account for some of this, but do these relatively natural and simple constructions really produce such a massive overhead? I guess these scripts show that they do, so my question is: what exactly is causing the high memory usage in the first script and the long runtime and high memory usage of the second script and is there any way to avoid these costs?

    Read the article

  • On Windows and Windows 7's Task Manager, why Memory is 1118MB Available but only 62MB Free? [closed]

    - by Jian Lin
    Possible Duplicate: Windows 7 memory usage What are the "Cached", "Available", and "Free" memory in the following picture (From Windows 7's Task Manager). If it is 1118MB Available, then why isn't it Free (to use)? As I understand it, if a bowl of noodle is available, that doesn't mean it is free... it may still cost $7. But what about in the Task Manager, when it is Available, it is also not Free? Does it cost $2 per MB? What about the "Cached"... What exactly is the Cached Memory? We may put some hard disk data in RAM and so we cache the data in RAM, for faster access (that's the operating system's job). So the Total Physical RAM is 6GB, what is the 1106 Cached? Cached in where? Caching physical RAM in ... some where? It is also strange that the Cached value is sometimes higher and sometimes lower than the Available value. Can somebody who is knowledgeable about this shred some light on these meanings?

    Read the article

  • ROracle support for TimesTen In-Memory Database

    - by Sam Drake
    Today's guest post comes from Jason Feldhaus, a Consulting Member of Technical Staff in the TimesTen Database organization at Oracle.  He shares with us a sample session using ROracle with the TimesTen In-Memory database.  Beginning in version 1.1-4, ROracle includes support for the Oracle Times Ten In-Memory Database, version 11.2.2. TimesTen is a relational database providing very fast and high throughput through its memory-centric architecture.  TimesTen is designed for low latency, high-volume data, and event and transaction management. A TimesTen database resides entirely in memory, so no disk I/O is required for transactions and query operations. TimesTen is used in applications requiring very fast and predictable response time, such as real-time financial services trading applications and large web applications. TimesTen can be used as the database of record or as a relational cache database to Oracle Database. ROracle provides an interface between R and the database, providing the rich functionality of the R statistical programming environment using the SQL query language. ROracle uses the OCI libraries to handle database connections, providing much better performance than standard ODBC.The latest ROracle enhancements include: Support for Oracle TimesTen In-Memory Database Support for Date-Time using R's POSIXct/POSIXlt data types RAW, BLOB and BFILE data type support Option to specify number of rows per fetch operation Option to prefetch LOB data Break support using Ctrl-C Statement caching support Times Ten 11.2.2 contains enhanced support for analytics workloads and complex queries: Analytic functions: AVG, SUM, COUNT, MAX, MIN, DENSE_RANK, RANK, ROW_NUMBER, FIRST_VALUE and LAST_VALUE Analytic clauses: OVER PARTITION BY and OVER ORDER BY Multidimensional grouping operators: Grouping clauses: GROUP BY CUBE, GROUP BY ROLLUP, GROUP BY GROUPING SETS Grouping functions: GROUP, GROUPING_ID, GROUP_ID WITH clause, which allows repeated references to a named subquery block Aggregate expressions over DISTINCT expressions General expressions that return a character string in the source or a pattern within the LIKE predicate Ability to order nulls first or last in a sort result (NULLS FIRST or NULLS LAST in the ORDER BY clause) Note: Some functionality is only available with Oracle Exalytics, refer to the TimesTen product licensing document for details. Connecting to TimesTen is easy with ROracle. Simply install and load the ROracle package and load the driver. > install.packages("ROracle") > library(ROracle) Loading required package: DBI > drv <- dbDriver("Oracle") Once the ROracle package is installed, create a database connection object and connect to a TimesTen direct driver DSN as the OS user. > conn <- dbConnect(drv, username ="", password="", dbname = "localhost/SampleDb_1122:timesten_direct") You have the option to report the server type - Oracle or TimesTen? > print (paste ("Server type =", dbGetInfo (conn)$serverType)) [1] "Server type = TimesTen IMDB" To create tables in the database using R data frame objects, use the function dbWriteTable. In the following example we write the built-in iris data frame to TimesTen. The iris data set is a small example data set containing 150 rows and 5 columns. We include it here not to highlight performance, but so users can easily run this example in their R session. > dbWriteTable (conn, "IRIS", iris, overwrite=TRUE, ora.number=FALSE) [1] TRUE Verify that the newly created IRIS table is available in the database. To list the available tables and table columns in the database, use dbListTables and dbListFields, respectively. > dbListTables (conn) [1] "IRIS" > dbListFields (conn, "IRIS") [1] "SEPAL.LENGTH" "SEPAL.WIDTH" "PETAL.LENGTH" "PETAL.WIDTH" "SPECIES" To retrieve a summary of the data from the database we need to save the results to a local object. The following call saves the results of the query as a local R object, iris.summary. The ROracle function dbGetQuery is used to execute an arbitrary SQL statement against the database. When connected to TimesTen, the SQL statement is processed completely within main memory for the fastest response time. > iris.summary <- dbGetQuery(conn, 'SELECT SPECIES, AVG ("SEPAL.LENGTH") AS AVG_SLENGTH, AVG ("SEPAL.WIDTH") AS AVG_SWIDTH, AVG ("PETAL.LENGTH") AS AVG_PLENGTH, AVG ("PETAL.WIDTH") AS AVG_PWIDTH FROM IRIS GROUP BY ROLLUP (SPECIES)') > iris.summary SPECIES AVG_SLENGTH AVG_SWIDTH AVG_PLENGTH AVG_PWIDTH 1 setosa 5.006000 3.428000 1.462 0.246000 2 versicolor 5.936000 2.770000 4.260 1.326000 3 virginica 6.588000 2.974000 5.552 2.026000 4 <NA> 5.843333 3.057333 3.758 1.199333 Finally, disconnect from the TimesTen Database. > dbCommit (conn) [1] TRUE > dbDisconnect (conn) [1] TRUE We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for our software: Times Ten In-Memory Database,  ROracle.  As always, we welcome comments and questions on the TimesTen and  Oracle R technical forums.

    Read the article

  • Am I getting the right memory for my motherboard?

    - by Daniel Carvalho
    Hi technophiles; I have a Gigabyte GA-EP45-DS motherboard. Also, the memory that came with my computer was two Transcend aXe RAM 1066MHZ 1GB modules. The thing is, I noticed that my motherboard has "DDR2 1200" written on it. This concerns me, have I bought slower memory than my computer is supposed to have ideally? Now, I'm not super concerned at a granular level about the best optimal RAM with the best CAS latency etc... but I do hope at least that I've got the right speed. Now, as far as I know, there is no such thing as ram at 1200MHZ? Am I right? You see, because I'm thinking of getting more RAM now, before I can't find the same type or speed any-more and just want to make sure it's the right thing. Furthermore, if the memory is slower than what I should be getting for my motherboard, what RAM should I be getting, and will that new RAM play nice with my old RAM? If I get new RAM at a different speed, would it be better / more beneficial performance-wise to omit the old RAM because of how the whole DUAL channel RAM thing works? I'm not too clued up on this area. Thanks chiefs.

    Read the article

  • ROracle support for TimesTen In-Memory Database

    - by Sherry LaMonica
    Today's guest post comes from Jason Feldhaus, a Consulting Member of Technical Staff in the TimesTen Database organization at Oracle.  He shares with us a sample session using ROracle with the TimesTen In-Memory database.  Beginning in version 1.1-4, ROracle includes support for the Oracle Times Ten In-Memory Database, version 11.2.2. TimesTen is a relational database providing very fast and high throughput through its memory-centric architecture.  TimesTen is designed for low latency, high-volume data, and event and transaction management. A TimesTen database resides entirely in memory, so no disk I/O is required for transactions and query operations. TimesTen is used in applications requiring very fast and predictable response time, such as real-time financial services trading applications and large web applications. TimesTen can be used as the database of record or as a relational cache database to Oracle Database. ROracle provides an interface between R and the database, providing the rich functionality of the R statistical programming environment using the SQL query language. ROracle uses the OCI libraries to handle database connections, providing much better performance than standard ODBC.The latest ROracle enhancements include: Support for Oracle TimesTen In-Memory Database Support for Date-Time using R's POSIXct/POSIXlt data types RAW, BLOB and BFILE data type support Option to specify number of rows per fetch operation Option to prefetch LOB data Break support using Ctrl-C Statement caching support Times Ten 11.2.2 contains enhanced support for analytics workloads and complex queries: Analytic functions: AVG, SUM, COUNT, MAX, MIN, DENSE_RANK, RANK, ROW_NUMBER, FIRST_VALUE and LAST_VALUE Analytic clauses: OVER PARTITION BY and OVER ORDER BY Multidimensional grouping operators: Grouping clauses: GROUP BY CUBE, GROUP BY ROLLUP, GROUP BY GROUPING SETS Grouping functions: GROUP, GROUPING_ID, GROUP_ID WITH clause, which allows repeated references to a named subquery block Aggregate expressions over DISTINCT expressions General expressions that return a character string in the source or a pattern within the LIKE predicate Ability to order nulls first or last in a sort result (NULLS FIRST or NULLS LAST in the ORDER BY clause) Note: Some functionality is only available with Oracle Exalytics, refer to the TimesTen product licensing document for details. Connecting to TimesTen is easy with ROracle. Simply install and load the ROracle package and load the driver. > install.packages("ROracle") > library(ROracle) Loading required package: DBI > drv <- dbDriver("Oracle") Once the ROracle package is installed, create a database connection object and connect to a TimesTen direct driver DSN as the OS user. > conn <- dbConnect(drv, username ="", password="", dbname = "localhost/SampleDb_1122:timesten_direct") You have the option to report the server type - Oracle or TimesTen? > print (paste ("Server type =", dbGetInfo (conn)$serverType)) [1] "Server type = TimesTen IMDB" To create tables in the database using R data frame objects, use the function dbWriteTable. In the following example we write the built-in iris data frame to TimesTen. The iris data set is a small example data set containing 150 rows and 5 columns. We include it here not to highlight performance, but so users can easily run this example in their R session. > dbWriteTable (conn, "IRIS", iris, overwrite=TRUE, ora.number=FALSE) [1] TRUE Verify that the newly created IRIS table is available in the database. To list the available tables and table columns in the database, use dbListTables and dbListFields, respectively. > dbListTables (conn) [1] "IRIS" > dbListFields (conn, "IRIS") [1] "SEPAL.LENGTH" "SEPAL.WIDTH" "PETAL.LENGTH" "PETAL.WIDTH" "SPECIES" To retrieve a summary of the data from the database we need to save the results to a local object. The following call saves the results of the query as a local R object, iris.summary. The ROracle function dbGetQuery is used to execute an arbitrary SQL statement against the database. When connected to TimesTen, the SQL statement is processed completely within main memory for the fastest response time. > iris.summary <- dbGetQuery(conn, 'SELECT SPECIES, AVG ("SEPAL.LENGTH") AS AVG_SLENGTH, AVG ("SEPAL.WIDTH") AS AVG_SWIDTH, AVG ("PETAL.LENGTH") AS AVG_PLENGTH, AVG ("PETAL.WIDTH") AS AVG_PWIDTH FROM IRIS GROUP BY ROLLUP (SPECIES)') > iris.summary SPECIES AVG_SLENGTH AVG_SWIDTH AVG_PLENGTH AVG_PWIDTH 1 setosa 5.006000 3.428000 1.462 0.246000 2 versicolor 5.936000 2.770000 4.260 1.326000 3 virginica 6.588000 2.974000 5.552 2.026000 4 <NA> 5.843333 3.057333 3.758 1.199333 Finally, disconnect from the TimesTen Database. > dbCommit (conn) [1] TRUE > dbDisconnect (conn) [1] TRUE We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for our software: Times Ten In-Memory Database,  ROracle.  As always, we welcome comments and questions on the TimesTen and  Oracle R technical forums.

    Read the article

  • Oracle TimesTen In-Memory Database Performance on SPARC T4-2

    - by Brian
    The Oracle TimesTen In-Memory Database is optimized to run on Oracle's SPARC T4 processor platforms running Oracle Solaris 11 providing unsurpassed scalability, performance, upgradability, protection of investment and return on investment. The following demonstrate the value of combining Oracle TimesTen In-Memory Database with SPARC T4 servers and Oracle Solaris 11: On a Mobile Call Processing test, the 2-socket SPARC T4-2 server outperforms: Oracle's SPARC Enterprise M4000 server (4 x 2.66 GHz SPARC64 VII+) by 34%. Oracle's SPARC T3-4 (4 x 1.65 GHz SPARC T3) by 2.7x, or 5.4x per processor. Utilizing the TimesTen Performance Throughput Benchmark (TPTBM), the SPARC T4-2 server protects investments with: 2.1x the overall performance of a 4-socket SPARC Enterprise M4000 server in read-only mode and 1.5x the performance in update-only testing. This is 4.2x more performance per processor than the SPARC64 VII+ 2.66 GHz based system. 10x more performance per processor than the SPARC T2+ 1.4 GHz server. 1.6x better performance per processor than the SPARC T3 1.65 GHz based server. In replication testing, the two socket SPARC T4-2 server is over 3x faster than the performance of a four socket SPARC Enterprise T5440 server in both asynchronous replication environment and the highly available 2-Safe replication. This testing emphasizes parallel replication between systems. Performance Landscape Mobile Call Processing Test Performance System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 218,400 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 162,900 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 80,400 TimesTen Performance Throughput Benchmark (TPTBM) Read-Only System Processor Sockets/Cores/Threads Tps SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 7.9M SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 6.5M M4000 SPARC64 VII+, 2.66 GHz 4 16 32 3.1M T5440 SPARC T2+, 1.4 GHz 4 32 256 3.1M TimesTen Performance Throughput Benchmark (TPTBM) Update-Only System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 547,800 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 363,800 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 240,500 TimesTen Replication Tests System Processor Sockets/Cores/Threads Asynchronous 2-Safe SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 38,024 13,701 SPARC T5440 SPARC T2+, 1.4 GHz 4 32 256 11,621 4,615 Configuration Summary Hardware Configurations: SPARC T4-2 server 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 4 x 300 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head SPARC T3-4 server 4 x SPARC T3 processors, 1.6 GHz 512 GB memory 1 x 8 Gbs FC Qlogic HBA 8 x 146 GB internal disks 1 x Sun Fire X4275 server configured as COMSTAR head SPARC Enterprise M4000 server 4 x SPARC64 VII+ processors, 2.66 GHz 128 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 2 x 146 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head Software Configuration: Oracle Solaris 11 11/11 Oracle TimesTen 11.2.2.4 Benchmark Descriptions TimesTen Performance Throughput BenchMark (TPTBM) is shipped with TimesTen and measures the total throughput of the system. The workload can test read-only, update-only, delete and insert operations as required. Mobile Call Processing is a customer-based workload for processing calls made by mobile phone subscribers. The workload has a mixture of read-only, update, and insert-only transactions. The peak throughput performance is measured from multiple concurrent processes executing the transactions until a peak performance is reached via saturation of the available resources. Parallel Replication tests using both asynchronous and 2-Safe replication methods. For asynchronous replication, transactions are processed in batches to maximize the throughput capabilities of the replication server and network. In 2-Safe replication, also known as no data-loss or high availability, transactions are replicated between servers immediately emphasizing low latency. For both environments, performance is measured in the number of parallel replication servers and the maximum transactions-per-second for all concurrent processes. See Also SPARC T4-2 Server oracle.com OTN Oracle TimesTen In-Memory Database oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 1 October 2012.

    Read the article

  • Lubuntu 13.10 unable to connect to cups localhost:631

    - by user142139
    I am using Lubuntu 13.10 (recently upgraded) and am trying to print to a network printer (HP photosmart 7960) through my router (US Robotics 5461). My printer is connected to the router via USB cable. Normally, I would use the cups configuration interface to set up the wireless connection to the printer. I was able to use the printer through the router wirelessly, using Ubuntu 12.04. Now, with my recently upgraded Lubuntu 13.10, I am unable to get the Cups config webpage (http://localhost:631) to come up. In Chromium, I get: This web page is not available. In Firefox, I get: Unable to connect. Firefox can't establish a connection to the server at localhost:631. The CUPS config file details are below. I have this website to help with the router connections for Linux: http://www.usr.com/support/5461/5461-files/printer_installation_linux/index.html My printer's address through the router is: http://192.168.2.1:1631/printers/My_Printer Can you tell me how to fix this? Or, what to add to the cups configuration file to make this work? Please help. Thanks psychicnut CUPS CONFIG FILE DETAILS: # Show general information in error_log. LogLevel warn MaxLogSize 0 SystemGroup lpadmin Listen /var/run/cups/cups.sock Listen /var/run/cups/cups.sock Listen 192.168.2.1:1631 Browsing Off BrowseLocalProtocols dnssd DefaultAuthType Basic WebInterface Yes <Location /> Order allow,deny </Location> <Location /admin> Order allow,deny </Location> <Location /admin/conf> AuthType Default Require user @SYSTEM Order allow,deny </Location> <Policy default> JobPrivateAccess default JobPrivateValues default SubscriptionPrivateAccess default SubscriptionPrivateValues default <Limit Create-Job Print-Job Print-URI Validate-Job> Order deny,allow </Limit> <Limit Send-Document Send-URI Hold-Job Release-Job Restart-Job Purge-Jobs Set-Job-Attributes Create-Job-Subscription Renew-Subscription Cancel-Subscription Get-Notifications Reprocess-Job Cancel-Current-Job Suspend-Current-Job Resume-Job Cancel-My-Jobs Close-Job CUPS-Move-Job CUPS-Get-Document> Require user @OWNER @SYSTEM Order deny,allow </Limit> <Limit CUPS-Add-Modify-Printer CUPS-Delete-Printer CUPS-Add-Modify-Class CUPS-Delete-Class CUPS-Set-Default CUPS-Get-Devices> AuthType Default Require user @SYSTEM Order deny,allow </Limit> <Limit Pause-Printer Resume-Printer Enable-Printer Disable-Printer Pause-Printer-After-Current-Job Hold-New-Jobs Release-Held-New-Jobs Deactivate-Printer Activate-Printer Restart-Printer Shutdown-Printer Startup-Printer Promote-Job Schedule-Job-After Cancel-Jobs CUPS-Accept-Jobs CUPS-Reject-Jobs> AuthType Default Require user @SYSTEM Order deny,allow </Limit> <Limit Cancel-Job CUPS-Authenticate-Job> Require user @OWNER @SYSTEM Order deny,allow </Limit> <Limit All> Order deny,allow </Limit> </Policy> <Policy authenticated> JobPrivateAccess default JobPrivateValues default SubscriptionPrivateAccess default SubscriptionPrivateValues default <Limit Create-Job Print-Job Print-URI Validate-Job> AuthType Default Order deny,allow </Limit> <Limit Send-Document Send-URI Hold-Job Release-Job Restart-Job Purge-Jobs Set-Job-Attributes Create-Job-Subscription Renew-Subscription Cancel-Subscription Get-Notifications Reprocess-Job Cancel-Current-Job Suspend-Current-Job Resume-Job Cancel-My-Jobs Close-Job CUPS-Move-Job CUPS-Get-Document> AuthType Default Require user @OWNER @SYSTEM Order deny,allow </Limit> <Limit CUPS-Add-Modify-Printer CUPS-Delete-Printer CUPS-Add-Modify-Class CUPS-Delete-Class CUPS-Set-Default> AuthType Default Require user @SYSTEM Order deny,allow </Limit> <Limit Pause-Printer Resume-Printer Enable-Printer Disable-Printer Pause-Printer-After-Current-Job Hold-New-Jobs Release-Held-New-Jobs Deactivate-Printer Activate-Printer Restart-Printer Shutdown-Printer Startup-Printer Promote-Job Schedule-Job-After Cancel-Jobs CUPS-Accept-Jobs CUPS-Reject-Jobs> AuthType Default Require user @SYSTEM Order deny,allow </Limit> <Limit Cancel-Job CUPS-Authenticate-Job> AuthType Default Require user @OWNER @SYSTEM Order deny,allow </Limit> <Limit All> Order deny,allow </Limit> </Policy> JobPrivateAccess default JobPrivateValues default SubscriptionPrivateAccess default SubscriptionPrivateValues default

    Read the article

  • Help with malloc and free: Glibc detected: free(): invalid pointer

    - by nunos
    I need help with debugging this piece of code. I know the problem is in malloc and free but can't find exactly where, why and how to fix it. Please don't answer: "Use gdb" and that's it. I would use gdb to debug it, but I still don't know much about it and am still learning it, and would like to have, in the meanwhile, another solution. Thanks. #include <stdio.h> #include <stdlib.h> #include <ctype.h> #include <unistd.h> #include <string.h> #include <sys/wait.h> #include <sys/types.h> #define MAX_COMMAND_LENGTH 256 #define MAX_ARGS_NUMBER 128 #define MAX_HISTORY_NUMBER 100 #define PROMPT ">>> " int num_elems; typedef enum {false, true} bool; typedef struct { char **arg; char *infile; char *outfile; int background; } Command_Info; int parse_cmd(char *cmd_line, Command_Info *cmd_info) { char *arg; char *args[MAX_ARGS_NUMBER]; int i = 0; arg = strtok(cmd_line, " "); while (arg != NULL) { args[i] = arg; arg = strtok(NULL, " "); i++; } num_elems = i;precisa em free_mem if (num_elems == 0) return 0; cmd_info->arg = (char **) ( malloc(num_elems * sizeof(char *)) ); cmd_info->infile = NULL; cmd_info->outfile = NULL; cmd_info->background = 0; bool b_infile = false; bool b_outfile = false; int iarg = 0; for (i = 0; i < num_elems; i++) { if ( !strcmp(args[i], "<") ) { if ( b_infile || i == num_elems-1 || !strcmp(args[i+1], "<") || !strcmp(args[i+1], ">") || !strcmp(args[i+1], "&") ) return -1; i++; cmd_info->infile = malloc(strlen(args[i]) * sizeof(char)); strcpy(cmd_info->infile, args[i]); b_infile = true; } else if (!strcmp(args[i], ">")) { if ( b_outfile || i == num_elems-1 || !strcmp(args[i+1], ">") || !strcmp(args[i+1], "<") || !strcmp(args[i+1], "&") ) return -1; i++; cmd_info->outfile = malloc(strlen(args[i]) * sizeof(char)); strcpy(cmd_info->outfile, args[i]); b_outfile = true; } else if (!strcmp(args[i], "&")) { if ( i == 0 || i != num_elems-1 || cmd_info->background ) return -1; cmd_info->background = true; } else { cmd_info->arg[iarg] = malloc(strlen(args[i]) * sizeof(char)); strcpy(cmd_info->arg[iarg], args[i]); iarg++; } } cmd_info->arg[iarg] = NULL; return 0; } void print_cmd(Command_Info *cmd_info) { int i; for (i = 0; cmd_info->arg[i] != NULL; i++) printf("arg[%d]=\"%s\"\n", i, cmd_info->arg[i]); printf("arg[%d]=\"%s\"\n", i, cmd_info->arg[i]); printf("infile=\"%s\"\n", cmd_info->infile); printf("outfile=\"%s\"\n", cmd_info->outfile); printf("background=\"%d\"\n", cmd_info->background); } void get_cmd(char* str) { fgets(str, MAX_COMMAND_LENGTH, stdin); str[strlen(str)-1] = '\0'; } pid_t exec_simple(Command_Info *cmd_info) { pid_t pid = fork(); if (pid < 0) { perror("Fork Error"); return -1; } if (pid == 0) { if ( (execvp(cmd_info->arg[0], cmd_info->arg)) == -1) { perror(cmd_info->arg[0]); exit(1); } } return pid; } void type_prompt(void) { printf("%s", PROMPT); } void syntax_error(void) { printf("msh syntax error\n"); } void free_mem(Command_Info *cmd_info) { int i; for (i = 0; cmd_info->arg[i] != NULL; i++) free(cmd_info->arg[i]); free(cmd_info->arg); free(cmd_info->infile); free(cmd_info->outfile); } int main(int argc, char* argv[]) { char cmd_line[MAX_COMMAND_LENGTH]; Command_Info cmd_info; //char* history[MAX_HISTORY_NUMBER]; while (true) { type_prompt(); get_cmd(cmd_line); if ( parse_cmd(cmd_line, &cmd_info) == -1) { syntax_error(); continue; } if (!strcmp(cmd_line, "")) continue; if (!strcmp(cmd_info.arg[0], "exit")) exit(0); pid_t pid = exec_simple(&cmd_info); waitpid(pid, NULL, 0); free_mem(&cmd_info); } return 0; }

    Read the article

  • NSMutableDictionary, alloc, init and reiniting...

    - by Marcos Issler
    In the following code: //anArray is a Array of Dictionary with 5 objs. //here we init with the first NSMutableDictionary *anMutableDict = [[NSMutableDictionary alloc] initWithDictionary:[anArray objectAtIndex:0]]; ... use of anMutableDict ... //then want to clear the MutableDict and assign the other dicts that was in the array of dicts for (int i=1;i<5;i++) { [anMutableDict removeAllObjects]; [anMutableDict initWithDictionary:[anArray objectAtIndex:i]]; } Why this crash? How is the right way to clear an nsmutabledict and the assign a new dict? Thanks guy's. Marcos.

    Read the article

  • Finding leaks under GeneralBlock-16?

    - by erastusnjuki
    If ObjectAlloc cannot deduce type information for the block, it uses 'GeneralBlock'. Any strategies to get leaks from this block that may eliminate the need of my 'trial and error' methods that I use? The Extended Detail thing doesn't really do it for me as I just keep guessing.

    Read the article

  • An interesting case of delete and destructor (C++)

    - by Viet
    I have a piece of code where I can call destructor multiple times and access member functions even the destructor was called with member variables' values preserved. I was still able to access member functions after I called delete but the member variables were nullified (all to 0). And I can't double delete. Please kindly explain this. Thanks. #include <iostream> using namespace std; template <typename T> void destroy(T* ptr) { ptr->~T(); } class Testing { public: Testing() : test(20) { } ~Testing() { printf("Testing is being killed!\n"); } int getTest() const { return test; } private: int test; }; int main() { Testing *t = new Testing(); cout << "t->getTest() = " << t->getTest() << endl; destroy(t); cout << "t->getTest() = " << t->getTest() << endl; t->~Testing(); cout << "t->getTest() = " << t->getTest() << endl; delete t; cout << "t->getTest() = " << t->getTest() << endl; destroy(t); cout << "t->getTest() = " << t->getTest() << endl; t->~Testing(); cout << "t->getTest() = " << t->getTest() << endl; //delete t; // <======== Don't do it! Double free/delete! cout << "t->getTest() = " << t->getTest() << endl; return 0; }

    Read the article

  • How to manipulate *huge* amounts of data

    - by Alejandro
    Hi there! I'm having the following problem. I need to store huge amounts of information (~32 GB) and be able to manipulate it as fast as possible. I'm wondering what's the best way to do it (combinations of programming language + OS + whatever you think its important). The structure of the information I'm using is a 4D array (NxNxNxN) of double-precission floats (8 bytes). Right now my solution is to slice the 4D array into 2D arrays and store them in separate files in the HDD of my computer. This is really slow and the manipulation of the data is unbearable, so this is no solution at all! I'm thinking on moving into a Supercomputing facility in my country and store all the information in the RAM, but I'm not sure how to implement an application to take advantage of it (I'm not a professional programmer, so any book/reference will help me a lot). An alternative solution I'm thinking on is to buy a dedicated server with lots of RAM, but I don't know for sure if that will solve the problem. So right now my ignorance doesn't let me choose the best way to proceed. What would you do if you were in this situation? I'm open to any idea. Thanks in advance!

    Read the article

  • Oracle Database In-Memory

    - by Mike.Hallett(at)Oracle-BI&EPM
    Normal 0 false false false EN-GB X-NONE X-NONE Larry Ellison unveiled the next major milestone in database technology, Oracle Database In-Memory, on June 10, 2014. Oracle Database In-Memory will be generally available in July 2014 and can be used with all hardware platforms on which Oracle Database 12c is supported. This option will accelerate database performance by orders of magnitude for analytics, data warehousing, and reporting while also speeding up online transaction processing (OLTP). It allows any existing Oracle Database-compatible application to automatically and transparently take advantage of columnar in-memory processing, without additional programming or application changes. Benefits Fast ad-hoc analytics without the need to pre-create indexes Completely transparent to existing applications Faster mixed workload OLTP No database size limit Industrial strength availability and security Robustness and maturity of Oracle Database 12c To find out more see Oracle Database In-Memory Comment from Rittman Mead on Oracle In-Memory Option Launch  ... and I will let you know how this unfolds in regards to advantages for OBI11g and Exalytics and Big Data over the coming months. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

    Read the article

  • Remove pointer object whose reference is mantained in three different lists

    - by brainydexter
    I am not sure how to approach this problem: 'Player' class mantains a list of Bullet* objects: class Player { protected: std::list< Bullet* > m_pBullet_list; } When the player fires a Bullet, it is added to this list. Also, inside the constructor of bullet, a reference of the same object is updated in CollisionMgr, where CollisionMgr also mantains a list of Bullet*. Bullet::Bullet(GameGL*a_pGameGL, Player*a_pPlayer) : GameObject( a_pGameGL ) { m_pPlayer = a_pPlayer; m_pGameGL->GetCollisionMgr()->AddBullet(this); } class CollisionMgr { void AddBullet(Bullet* a_pBullet); protected: std::list< Bullet*> m_BulletPList; } In CollisionMgr.Update(); based on some conditions, I populate class Cell which again contain a list of Bullet*. Finally, certain conditions qualify a Bullet to be deleted. Now, these conditions are tested upon while iterating through a Cell's list. So, if I have to delete the Bullet object, from all these places, how should I do it so that there are no more dangling references to it? std::list< Bullet*>::iterator bullet_it; for( bullet_it = (a_pCell->m_BulletPList).begin(); bullet_it != (a_pCell->m_BulletPList).end(); bullet_it++) { bool l_Bullet_trash = false; Bullet* bullet1 = *bullet_it; // conditions would set this to true if ( l_Bullet_Trash ) // TrashBullet( bullet1 ); continue; } Also, I was reading about list::remove, and it mentions that it calls the destructor of the object we are trying to delete. Given this info, if I delete from one list, the object does not exist, but the list would still contain a reference to it..How do I handle all these problems ? Can someone please help me here ? Thanks PS: If you want me to post more code or provide explanation, please do let me know.

    Read the article

  • JVMTI: FollowReferences : how to skip Soft/Weak/Phantom references?

    - by Jayan
    I am writing a small code to detect number of objects left behind after certain actions in our tool. This uses FollowReferences() JVMTI-API. This counts instances reachable by all paths. How can I skip paths that included weak/soft/phantom reference? (IterateThroughHeap counts all objects at the moment, so the number is not fully reliable) Thanks, Jayan

    Read the article

  • iPhone objective-c autoreleasing leaking

    - by okami
    I do this: NSString *fullpath = [[NSBundle mainBundle] pathForResource:@"text_file" ofType:@"txt"]; Why the following message appear? Is my code leaking? 2010-03-31 13:44:18.649 MJIPhone[2175:207] *** _NSAutoreleaseNoPool(): Object 0x3909ba0 of class NSPathStore2 autoreleased with no pool in place - just leaking Stack: (0x1656bf 0xc80d0 0xcf2ad 0xcee0e 0xd3327 0x2482 0x2426) 2010-03-31 13:44:18.653 MJIPhone[2175:207] *** _NSAutoreleaseNoPool(): Object 0x390b0b0 of class NSPathStore2 autoreleased with no pool in place - just leaking Stack: (0x1656bf 0xc80d0 0xc7159 0xd0c6f 0xd3421 0x2482 0x2426) 2010-03-31 13:44:18.672 MJIPhone[2175:207] *** _NSAutoreleaseNoPool(): Object 0x390d140 of class NSCFString autoreleased with no pool in place - just leaking Stack: (0x1656bf 0xc6e62 0xcec1b 0xd4386 0x24ac 0x2426)

    Read the article

< Previous Page | 56 57 58 59 60 61 62 63 64 65 66 67  | Next Page >