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  • computed column calculate a value based on different table

    - by adnan
    i've a table c_const code | nvalue -------------- 1 | 10000 2 | 20000 and i've another table t_anytable rec_id | s_id | n_code --------------------- 2 | x | 1 now i want to calculate the x value with computed column, based on rec_id*(select nvalue from c_const where code=ncode) but i get error "Subqueries are not allowed in this context. Only scalar expressions are allowed." how can i calculate the value in this computed column ? thanks.

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  • Knowing the type of the stored proc when invoking from C#

    - by dotnetdev
    I am making a windows service to be able to run operations on a sql server database (insert, edit, etc) and invoke Stored Procs. However, is there a way for me to know the type of the SP? When invoking from C#, I need to knof if it is returning 1 value, or more, or none (so I can use executereader, scalar, etc)? Thanks

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  • T-SQL get SELECTed value of stored procedure

    - by David
    In T-SQL, this is allowed: DECLARE @SelectedValue int SELECT @SelectedValue = MyIntField FROM MyTable WHERE MyPrimaryKeyField = 1 So, it's possible to get the value of a SELECT and stuff it in a variable (provided it's scalar, obviously). If I put the same select logic in a stored procedure: CREATE PROCEDURE GetMyInt AS SELECT MyIntField FROM MyTable WHERE MyPrimaryKeyField = 1 Can I get the output of this stored procedure and stuff it in a variable? Something like: DECLARE @SelectedValue int SELECT @SelectedValue = EXEC GetMyInt (I know the syntax above is not allowed because I tried it!)

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  • How do call this symbolic code transformation ?

    - by erric
    Hi, I often cross this kind of code transformation (or even mathematical transformation) (python example, but applies to any language) I've go a function def f(x): return x I use it into another one. def g(x): return f(x)*f(x) print g(2) leads to 4 But I want to remove the functional dependency, and I change the function g into def g(f): return f*f print g( f(2) ) leads to 4 too How do you call this kind of transformation, locally turning a function into a scalar ?

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  • How do Perl FIRSTKEY and NEXTKEY work

    - by mmccoo
    Tie::Hash has these: sub FIRSTKEY { my $a = scalar keys %{$_[0]}; each %{$_[0]} } sub NEXTKEY { each %{$_[0]} } NEXTKEY takes two arguments, one of which is the last key but that arg is never referenced? The various Tie docs don't shed any light on this other than this in perltie: my $a = keys %{$self->{LIST}}; # reset each() iterator looking at the doc for each doesn't add to this. What's going on?

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  • sql user defined function

    - by nectar
    for a table valued function in sql why cant we write sql statements inside begin and end tags like- create function dbo.emptable() returns Table as BEGIN --it throws an error return (select id, name, salary from employee) END go while in scalar valued function we can use these tags like create function dbo.countemp() returns int as begin return (select count(*) from employee) end go is there any specific rule where we should use BEGIN & END tags

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  • What does L == 2 mean in MATLAB?

    - by Matlaber
    BW = logical([1 1 1 0 0 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 1 0 1 1 1 0 0 0 0 0]); L = bwlabel(BW,4); [r,c] = find(L == 2); How can a matrix been compared with scalar?

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  • Locating Rogue Perl Script

    - by Gary Garside
    I've been trying to source the location of a perl script which is causing havoc on a server which i control. I'm also trying to find out exactly how this script was installed on the server - my best guess is through a wordpress exploit. The server is a basic web setup running Ubuntu 9.04, Apache and MySQL. I use IPTables for firewall, the site runs around 20 sites and the load never really creeps above 0.7. From what i can see the script is making outbound connection to other servers (most likely trying to brute force entry). Here is a top dump of one of the processes: PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 22569 www-data 20 0 22784 3216 780 R 100 0.2 47:00.60 perl The command the process is running is /usr/sbin/sshd . I've tried to find an exact file name but im having no luck... i've ran a lsof -p PID and here is the output: COMMAND PID USER FD TYPE DEVICE SIZE NODE NAME perl 22569 www-data cwd DIR 8,6 4096 2 / perl 22569 www-data rtd DIR 8,6 4096 2 / perl 22569 www-data txt REG 8,6 10336 162220 /usr/bin/perl perl 22569 www-data mem REG 8,6 26936 170219 /usr/lib/perl/5.10.0/auto/Socket/Socket.so perl 22569 www-data mem REG 8,6 22808 170214 /usr/lib/perl/5.10.0/auto/IO/IO.so perl 22569 www-data mem REG 8,6 39112 145112 /lib/libcrypt-2.9.so perl 22569 www-data mem REG 8,6 1502512 145124 /lib/libc-2.9.so perl 22569 www-data mem REG 8,6 130151 145113 /lib/libpthread-2.9.so perl 22569 www-data mem REG 8,6 542928 145122 /lib/libm-2.9.so perl 22569 www-data mem REG 8,6 14608 145125 /lib/libdl-2.9.so perl 22569 www-data mem REG 8,6 1503704 162222 /usr/lib/libperl.so.5.10.0 perl 22569 www-data mem REG 8,6 135680 145116 /lib/ld-2.9.so perl 22569 www-data 0r FIFO 0,6 157216 pipe perl 22569 www-data 1w FIFO 0,6 197642 pipe perl 22569 www-data 2w FIFO 0,6 197642 pipe perl 22569 www-data 3w FIFO 0,6 197642 pipe perl 22569 www-data 4u IPv4 383991 TCP outsidesoftware.com:56869->server12.34.56.78.live-servers.net:www (ESTABLISHED) My gut feeling is outsidesoftware.com is also under attacK? Or possibly being used as a tunnel. I've managed to find a number of rouge files in /tmp and /var/tmp, here is a brief output of one of these files: #!/usr/bin/perl # this spreader is coded by xdh # xdh@xxxxxxxxxxx # only for testing... my @nickname = ("vn"); my $nick = $nickname[rand scalar @nickname]; my $ircname = $nickname[rand scalar @nickname]; #system("kill -9 `ps ax |grep httpdse |grep -v grep|awk '{print $1;}'`"); my $processo = '/usr/sbin/sshd'; The full file contents can be viewed here: http://pastebin.com/yenFRrGP Im trying to achieve a couple of things here... Firstly i need to stop these processes from running. Either by disabling outbound SSH or any IP Tables rules etc... these scripts have been running for around 36 hours now and my main concern is to stop these things running and respawning by themselves. Secondly i need to try and source where and how these scripts have been installed. If anybody has any advise on what to look for in access logs or anything else i would be really grateful. Thanks in advance

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  • What's the best way to format an external HDD for both OSX and Windows ?

    - by George Profenza
    I have an external HDD (1TB) and I'd like to use it on OSX and Windows. I had another external HDD using NTFS and I used NTFS-3G on osx to write files, but I found the reading/writing very slow. Googling a bit I see many people recommend HFS+ in conjuction with HFS Explorer for Windows. Is this the best way ? Is it possible to have two partitions, one HFS+ and one NTFS ? Is it a good option or is it better to use one partition ? I've seen this thread on using UDF for USB flash drive. Would that be suited for an USB external HDD ?

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  • How can I view updatedb database content, and then exclude certain files/paths?

    - by rubo77
    The updatedb database on my debian server is quite slow. where is the database located and how can I view its content and find out if there are some paths with useless stuff, that I could add to the prunepaths? my /etc/updatedb.conf looks like this: ... # filesystems which are pruned from updatedb database PRUNEFS="NFS nfs nfs4 afs binfmt_misc proc smbfs autofs iso9660 ncpfs coda devpts ftpfs devfs mfs shfs sysfs cifs lustre_lite tmpfs usbfs udf" export PRUNEFS # paths which are pruned from updatedb database PRUNEPATHS="/tmp /usr/tmp /var/tmp /afs /amd /alex /var/spool /sfs /media /var/backups/rsnapshot /var/mod_pagespeed/" ... and how can I prune all paths that contain */.git/* and */.svn/* ?

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  • Ubuntu 6.06 Boot problem

    - by nijikunai
    I tried to boot my pc using ubuntu 6.06 in the live cd mode but it refuses to boot. It throws the error Uncompressing Linux.. ok, booting from kernel [ 54.168828] ACPI Unable to load the System Descriptor Tables The live cd works perfectly okay in other computers. Out of curiosity, I also tried to boot using Slax live cd, It too threw some errors incomplete literal tree invalid compressed format (err=1) UDF-fs: No partition found (1) XFS: bade magic number XFS: SB validate failed Kernel panic - not syncing: VFS: Unable to mount root fs on unknown-block(1,0) The slax errors are a bit worrying to me. Thanks for the help in advance!

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  • SQL SERVER – Removing Leading Zeros From Column in Table

    - by pinaldave
    Some questions surprises me and make me write code which I have never explored before. Today was similar experience as well. I have always received the question regarding how to reserve leading zeroes in SQL Server while displaying them on the SSMS or another application. I have written articles on this subject over here. SQL SERVER – Pad Ride Side of Number with 0 – Fixed Width Number Display SQL SERVER – UDF – Pad Ride Side of Number with 0 – Fixed Width Number Display SQL SERVER – Preserve Leading Zero While Coping to Excel from SSMS Today I received a very different question where the user wanted to remove leading zero and white space. I am using the same sample sent by user in this example. USE tempdb GO -- Create sample table CREATE TABLE Table1 (Col1 VARCHAR(100)) INSERT INTO Table1 (Col1) SELECT '0001' UNION ALL SELECT '000100' UNION ALL SELECT '100100' UNION ALL SELECT '000 0001' UNION ALL SELECT '00.001' UNION ALL SELECT '01.001' GO -- Original data SELECT * FROM Table1 GO -- Remove leading zeros SELECT SUBSTRING(Col1, PATINDEX('%[^0 ]%', Col1 + ' '), LEN(Col1)) FROM Table1 GO -- Clean up DROP TABLE Table1 GO Here is the resultset of above script. It will remove any leading zero or space and will display the number accordingly. This problem is a very generic problem and I am confident there are alternate solutions to this problem as well. If you have an alternate solution or can suggest a sample data which does not satisfy the SUBSTRING solution proposed, I will be glad to include them in follow up blog post with due credit. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQL SERVER – Get Date and Time From Current DateTime – SQL in Sixty Seconds #025 – Video

    - by pinaldave
    This is 25th video of series SQL in Sixty Seconds we started a few months ago. Even though this is 25th video it seems like we have just started this few days ago. The best part of this SQL in Sixty Seconds is that one can learn something new in less than sixty seconds. There are many concepts which are not new for many but just we all have 60 seconds to refresh our memories. In this video I have touched a very simple question which I receive very frequently on this blog. Q1) How to get current date time? Q2) How to get Only Date from datetime? Q3) How to get Only Time from datetime? I have created a sixty second video on this subject and hopefully this will help many beginners in the SQL Server field. This sixty second video describes the same. Here is a similar script which I have used in the video. SELECT GETDATE() GO -- SQL Server 2000/2005 SELECT CONVERT(VARCHAR(8),GETDATE(),108) AS HourMinuteSecond, CONVERT(VARCHAR(8),GETDATE(),101) AS DateOnly; GO -- SQL Server 2008 Onwards SELECT CONVERT(TIME,GETDATE()) AS HourMinuteSeconds; SELECT CONVERT(DATE,GETDATE()) AS DateOnly; GO Related Tips in SQL in Sixty Seconds: Retrieve Current Date Time in SQL Server CURRENT_TIMESTAMP, GETDATE(), {fn NOW()} Get Time in Hour:Minute Format from a Datetime – Get Date Part Only from Datetime Get Current System Date Time Get Date Time in Any Format – UDF – User Defined Functions Date and Time Functions – EOMONTH() – A Quick Introduction DATE and TIME in SQL Server 2008 I encourage you to submit your ideas for SQL in Sixty Seconds. We will try to accommodate as many as we can. If we like your idea we promise to share with you educational material. Image Credit: Movie Gone in 60 Seconds Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Query, SQL Scripts, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology, Video

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  • SQL SERVER – Weekly Series – Memory Lane – #039

    - by Pinal Dave
    Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 FQL – Facebook Query Language Facebook list following advantages of FQL: Condensed XML reduces bandwidth and parsing costs. More complex requests can reduce the number of requests necessary. Provides a single consistent, unified interface for all of your data. It’s fun! UDF – Get the Day of the Week Function The day of the week can be retrieved in SQL Server by using the DatePart function. The value returned by the function is between 1 (Sunday) and 7 (Saturday). To convert this to a string representing the day of the week, use a CASE statement. UDF – Function to Get Previous And Next Work Day – Exclude Saturday and Sunday While reading ColdFusion blog of Ben Nadel Getting the Previous Day In ColdFusion, Excluding Saturday And Sunday, I realize that I use similar function on my SQL Server Database. This function excludes the Weekends (Saturday and Sunday), and it gets previous as well as next work day. Complete Series of SQL Server Interview Questions and Answers Data Warehousing Interview Questions and Answers – Introduction Data Warehousing Interview Questions and Answers – Part 1 Data Warehousing Interview Questions and Answers – Part 2 Data Warehousing Interview Questions and Answers – Part 3 Data Warehousing Interview Questions and Answers Complete List Download 2008 Introduction to Log Viewer In SQL Server all the windows event logs can be seen along with SQL Server logs. Interface for all the logs is same and can be launched from the same place. This log can be exported and filtered as well. DBCC SHRINKFILE Takes Long Time to Run If you are DBA who are involved with Database Maintenance and file group maintenance, you must have experience that many times DBCC SHRINKFILE operations takes a long time but any other operations with Database are relatively quicker. mssqlsystemresource – Resource Database The purpose of resource database is to facilitates upgrading to the new version of SQL Server without any hassle. In previous versions whenever version of SQL Server was upgraded all the previous version system objects needs to be dropped and new version system objects to be created. 2009 Puzzle – Write Script to Generate Primary Key and Foreign Key In SQL Server Management Studio (SSMS), there is no option to script all the keys. If one is required to script keys they will have to manually script each key one at a time. If database has many tables, generating one key at a time can be a very intricate task. I want to throw a question to all of you if any of you have scripts for the same purpose. Maximizing View of SQL Server Management Studio – Full Screen – New Screen I had explained the following two different methods: 1) Open Results in Separate Tab - This is a very interesting method as result pan shows up in a different tab instead of the splitting screen horizontally. 2) Open SSMS in Full Screen - This works always and to its best. Not many people are aware of this method; hence, very few people use it to enhance performance. 2010 Find Queries using Parallelism from Cached Plan T-SQL script gets all the queries and their execution plan where parallelism operations are kicked up. Pay attention there is TOP 10 is used, if you have lots of transactional operations, I suggest that you change TOP 10 to TOP 50 This is the list of the all the articles in the series of computed columns. SQL SERVER – Computed Column – PERSISTED and Storage This article talks about how computed columns are created and why they take more storage space than before. SQL SERVER – Computed Column – PERSISTED and Performance This article talks about how PERSISTED columns give better performance than non-persisted columns. SQL SERVER – Computed Column – PERSISTED and Performance – Part 2 This article talks about how non-persisted columns give better performance than PERSISTED columns. SQL SERVER – Computed Column and Performance – Part 3 This article talks about how Index improves the performance of Computed Columns. SQL SERVER – Computed Column – PERSISTED and Storage – Part 2 This article talks about how creating index on computed column does not grow the row length of table. SQL SERVER – Computed Columns – Index and Performance This article summarized all the articles related to computed columns. 2011 SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Data Warehousing Concepts – Day 21 of 31 What is Data Warehousing? What is Business Intelligence (BI)? What is a Dimension Table? What is Dimensional Modeling? What is a Fact Table? What are the Fundamental Stages of Data Warehousing? What are the Different Methods of Loading Dimension tables? Describes the Foreign Key Columns in Fact Table and Dimension Table? What is Data Mining? What is the Difference between a View and a Materialized View? SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Data Warehousing Concepts – Day 22 of 31 What is OLTP? What is OLAP? What is the Difference between OLTP and OLAP? What is ODS? What is ER Diagram? SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Data Warehousing Concepts – Day 23 of 31 What is ETL? What is VLDB? Is OLTP Database is Design Optimal for Data Warehouse? If denormalizing improves Data Warehouse Processes, then why is the Fact Table is in the Normal Form? What are Lookup Tables? What are Aggregate Tables? What is Real-Time Data-Warehousing? What are Conformed Dimensions? What is a Conformed Fact? How do you Load the Time Dimension? What is a Level of Granularity of a Fact Table? What are Non-Additive Facts? What is a Factless Facts Table? What are Slowly Changing Dimensions (SCD)? SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Data Warehousing Concepts – Day 24 of 31 What is Hybrid Slowly Changing Dimension? What is BUS Schema? What is a Star Schema? What Snow Flake Schema? Differences between the Star and Snowflake Schema? What is Difference between ER Modeling and Dimensional Modeling? What is Degenerate Dimension Table? Why is Data Modeling Important? What is a Surrogate Key? What is Junk Dimension? What is a Data Mart? What is the Difference between OLAP and Data Warehouse? What is a Cube and Linked Cube with Reference to Data Warehouse? What is Snapshot with Reference to Data Warehouse? What is Active Data Warehousing? What is the Difference between Data Warehousing and Business Intelligence? What is MDS? Explain the Paradigm of Bill Inmon and Ralph Kimball. SQL SERVER – Azure Interview Questions and Answers – Guest Post by Paras Doshi – Day 25 of 31 Paras Doshi has submitted 21 interesting question and answers for SQL Azure. 1.What is SQL Azure? 2.What is cloud computing? 3.How is SQL Azure different than SQL server? 4.How many replicas are maintained for each SQL Azure database? 5.How can we migrate from SQL server to SQL Azure? 6.Which tools are available to manage SQL Azure databases and servers? 7.Tell me something about security and SQL Azure. 8.What is SQL Azure Firewall? 9.What is the difference between web edition and business edition? 10.How do we synchronize On Premise SQL server with SQL Azure? 11.How do we Backup SQL Azure Data? 12.What is the current pricing model of SQL Azure? 13.What is the current limitation of the size of SQL Azure DB? 14.How do you handle datasets larger than 50 GB? 15.What happens when the SQL Azure database reaches Max Size? 16.How many databases can we create in a single server? 17.How many servers can we create in a single subscription? 18.How do you improve the performance of a SQL Azure Database? 19.What is code near application topology? 20.What were the latest updates to SQL Azure service? 21.When does a workload on SQL Azure get throttled? SQL SERVER – Interview Questions and Answers – Guest Post by Malathi Mahadevan – Day 26 of 31 Malachi had asked a simple question which has several answers. Each answer makes you think and ponder about the reality of the IT world. Look at the simple question – ‘What is the toughest challenge you have faced in your present job and how did you handle it’? and its various answers. Each answer has its own story. SQL SERVER – Interview Questions and Answers – Guest Post by Rick Morelan – Day 27 of 31 Rick Morelan of Joes2Pros has written an excellent blog post on the subject how to find top N values. Most people are fully aware of how the TOP keyword works with a SELECT statement. After years preparing so many students to pass the SQL Certification I noticed they were pretty well prepared for job interviews too. Yes, they would do well in the interview but not great. There seemed to be a few questions that would come up repeatedly for almost everyone. Rick addresses similar questions in his lucid writing skills. 2012 Observation of Top with Index and Order of Resultset SQL Server has lots of things to learn and share. It is amazing to see how people evaluate and understand different techniques and styles differently when implementing. The real reason may be absolutely different but we may blame something totally different for the incorrect results. Read the blog post to learn more. How do I Record Video and Webcast How to Convert Hex to Decimal or INT Earlier I asked regarding a question about how to convert Hex to Decimal. I promised that I will post an answer with Due Credit to the author but never got around to post a blog post around it. Read the original post over here SQL SERVER – Question – How to Convert Hex to Decimal. Query to Get Unique Distinct Data Based on Condition – Eliminate Duplicate Data from Resultset The natural reaction will be to suggest DISTINCT or GROUP BY. However, not all the questions can be solved by DISTINCT or GROUP BY. Let us see the following example, where a user wanted only latest records to be displayed. Let us see the example to understand further. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Converting a JD Edwards Date to a System.DateTime

    - by Christopher House
    I'm working on moving some data from JD Edwards to a SQL Server database using SSIS and needed to deal with the way in which JDE stores dates.  The format is CYYDDD, where: C = century, 1 for >= 2000 and 0 for < 2000 YY = the last two digits of the year DDD = the number of the day.  Jan 1 = 1, Dec. 31 = 365 (or 366 in a leap year) The .Net base class library has lots of good support for handling dates, but nothing as specific as the JD Edwards format, so I needed to write a bit of code to translate the JDE format to System.DateTime.  The function is below: public static DateTime FromJdeDate(double jdeDate) {   DateTime convertedDate = DateTime.MinValue;   if (jdeDate >= 30001 && jdeDate <= 200000)   {     short yearValue = (short)(jdeDate / 1000d + 1900d);     short dayValue = (short)((jdeDate % 1000) - 1);     convertedDate = DateTime.Parse("01/01/" + yearValue.ToString()).AddDays(dayValue);   }   else   {     throw new ArgumentException("The value provided does not represent a valid JDE date", "jdeDate");   }   return convertedDate; }  I'd love to take credit for this myself, but this is an adaptation of a TSQL UDF that I got from another consultant at the client site.

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  • updatedb & locate command problem - Files from external hard drive are no longer indexed after rebooting

    - by user784637
    Files from my external hard drive are no longer indexed after rebooting. I have to remount and then run # updatedb after each reboot. The problem is updatedb takes a few minutes for my external hard drives. Is there any way I can retain indexing for my externals after I reboot so that the locate command can search through my externals? EDIT: Per Request here are my specs: $ cat /etc/updatedb.conf PRUNE_BIND_MOUNTS="yes" # PRUNENAMES=".git .bzr .hg .svn" PRUNEPATHS="/tmp /var/spool /media" PRUNEFS="NFS nfs nfs4 rpc_pipefs afs binfmt_misc proc smbfs autofs iso9660 ncpfs coda devpts ftpfs devfs mfs shfs sysfs cifs lustre_lite tmpfs usbfs udf fuse.glusterfs fuse.sshfs ecryptfs fusesmb devtmpfs" # mount /dev/sda5 on / type ext4 (rw,errors=remount-ro) proc on /proc type proc (rw,noexec,nosuid,nodev) none on /sys type sysfs (rw,noexec,nosuid,nodev) none on /sys/fs/fuse/connections type fusectl (rw) none on /sys/kernel/debug type debugfs (rw) none on /sys/kernel/security type securityfs (rw) none on /dev type devtmpfs (rw,mode=0755) none on /dev/pts type devpts (rw,noexec,nosuid,gid=5,mode=0620) none on /dev/shm type tmpfs (rw,nosuid,nodev) none on /var/run type tmpfs (rw,nosuid,mode=0755) none on /var/lock type tmpfs (rw,noexec,nosuid,nodev) none on /lib/init/rw type tmpfs (rw,nosuid,mode=0755) binfmt_misc on /proc/sys/fs/binfmt_misc type binfmt_misc (rw,noexec,nosuid,nodev) gvfs-fuse-daemon on /home/me/.gvfs type fuse.gvfs-fuse-daemon (rw,nosuid,nodev,user=me) /dev/sdb1 on /media/me type fuseblk (rw,nosuid,nodev,allow_other,blksize=4096,default_permissions) /dev/sdd1 on /media/Little Boy type fuseblk (rw,nosuid,nodev,allow_other,blksize=4096,default_permissions) /dev/sde1 on /media/Fat Man type fuseblk (rw,nosuid,nodev,allow_other,blksize=4096,default_permissions) # on_ac_power; echo $? 255

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  • Cannot copy MP3 files from a CD

    - by MountainX
    I purchased a set of spoken word audio CD's that have MP3 and FLAC audio files; I think they also play as regular audio CD's because I see a CDA directory and .cda files. But I'm only interested in playing the MP3 files by copying them to my phone. Dolphin file manager shows all the files on the CD. However, it will not copy any of them to my hard drive, which is what my goal is. Dolphin shows no error, but the copy progress is zero. Amarok will play the files but not easily. I only tried the flac files. To play a file, I click the file in Dolphin, then I have to cancel a job using KDE's notification system, then Amarok proceeds to copy the file to a tmp directory which takes a long time, then it finally plays. kb3 will rip the audio, but I would prefer to copy the files directly from the CD. Since Dolphin would not copy the files, I thought I would try the terminal, but I can't get that to work either. mount -t auto -o ro /dev/sr0 /mnt/temp that gives the error: wrong fs type, bad option, bad superblock, etc. I get the same error using -t iso9660 and -t udf. so I started troubleshooting: ~$ wodim --devices wodim: Overview of accessible drives (1 found) : ------------------------------------------------------------------------- 0 dev='/dev/sg1' rwrw-- : 'MATSHITA' 'DVD-RAM UJ8A0AS' ------------------------------------------------------------------------- /dev/sg1 is not a block device sudo file -s /dev/sr0 ERROR: cannot read /dev/sr0 (input/output error) sudo file -s /dev/sg1 just hangs How can I copy these files to my computer hard disk?

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  • [Oracle Identity Manager] 11g R2 Bundle Patch 09 is Available!

    - by mustafakaya
    Oracle Identity Manager Bundle Patch 09 is available now. You can download BP09 from here. Also,there is a important recommendation for BP08!  List of bugs fixed with BP09; Bug:12699224 : Trusted source reconciliation fails to create users with many reconciliation field mappings. Bug:14407437 : Provisioning through bulk request inserts null records into child tables. Bug:14493217 : Target resource reconciliation throws ORA-06512 error when the Descriptive field is mapped to a field that does not have a reconciliation field mapping. Bug:16044671 : User form customization fails if a UDF contains invalid character. Bug:16545968 : Modifying any attribute on a service account changes the account type as a primary account. Bug:16562633 : Oracle Identity Manager throws javax.el.elexceptions while viewing profile under direct report. Bug:16662834 : User not reprovisoned after user is deleted and created in the target with the same orclguid. Bug:16662905 : If an LOV field is required on an Application Instance form, no validation is enforced on the LOV field although it is required. Bug:16701873 : The Members tab of a role displays only enabled users and does not display disabled users. Bug:16862846 : When a notification is being sent, the mail ID in the Reply To field is set as the recipient's mail ID instead of the sender's mail ID. Bug:16824062 : When you use API to fetch or delete child data from an account, the child data row value is null. Therefore, child data is not returned. Bug:16912736 : There is a performance issue when the provisioned application instance details is opened for a user.

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  • How John Got 15x Improvement Without Really Trying

    - by rchrd
    The following article was published on a Sun Microsystems website a number of years ago by John Feo. It is still useful and worth preserving. So I'm republishing it here.  How I Got 15x Improvement Without Really Trying John Feo, Sun Microsystems Taking ten "personal" program codes used in scientific and engineering research, the author was able to get from 2 to 15 times performance improvement easily by applying some simple general optimization techniques. Introduction Scientific research based on computer simulation depends on the simulation for advancement. The research can advance only as fast as the computational codes can execute. The codes' efficiency determines both the rate and quality of results. In the same amount of time, a faster program can generate more results and can carry out a more detailed simulation of physical phenomena than a slower program. Highly optimized programs help science advance quickly and insure that monies supporting scientific research are used as effectively as possible. Scientific computer codes divide into three broad categories: ISV, community, and personal. ISV codes are large, mature production codes developed and sold commercially. The codes improve slowly over time both in methods and capabilities, and they are well tuned for most vendor platforms. Since the codes are mature and complex, there are few opportunities to improve their performance solely through code optimization. Improvements of 10% to 15% are typical. Examples of ISV codes are DYNA3D, Gaussian, and Nastran. Community codes are non-commercial production codes used by a particular research field. Generally, they are developed and distributed by a single academic or research institution with assistance from the community. Most users just run the codes, but some develop new methods and extensions that feed back into the general release. The codes are available on most vendor platforms. Since these codes are younger than ISV codes, there are more opportunities to optimize the source code. Improvements of 50% are not unusual. Examples of community codes are AMBER, CHARM, BLAST, and FASTA. Personal codes are those written by single users or small research groups for their own use. These codes are not distributed, but may be passed from professor-to-student or student-to-student over several years. They form the primordial ocean of applications from which community and ISV codes emerge. Government research grants pay for the development of most personal codes. This paper reports on the nature and performance of this class of codes. Over the last year, I have looked at over two dozen personal codes from more than a dozen research institutions. The codes cover a variety of scientific fields, including astronomy, atmospheric sciences, bioinformatics, biology, chemistry, geology, and physics. The sources range from a few hundred lines to more than ten thousand lines, and are written in Fortran, Fortran 90, C, and C++. For the most part, the codes are modular, documented, and written in a clear, straightforward manner. They do not use complex language features, advanced data structures, programming tricks, or libraries. I had little trouble understanding what the codes did or how data structures were used. Most came with a makefile. Surprisingly, only one of the applications is parallel. All developers have access to parallel machines, so availability is not an issue. Several tried to parallelize their applications, but stopped after encountering difficulties. Lack of education and a perception that parallelism is difficult prevented most from trying. I parallelized several of the codes using OpenMP, and did not judge any of the codes as difficult to parallelize. Even more surprising than the lack of parallelism is the inefficiency of the codes. I was able to get large improvements in performance in a matter of a few days applying simple optimization techniques. Table 1 lists ten representative codes [names and affiliation are omitted to preserve anonymity]. Improvements on one processor range from 2x to 15.5x with a simple average of 4.75x. I did not use sophisticated performance tools or drill deep into the program's execution character as one would do when tuning ISV or community codes. Using only a profiler and source line timers, I identified inefficient sections of code and improved their performance by inspection. The changes were at a high level. I am sure there is another factor of 2 or 3 in each code, and more if the codes are parallelized. The study’s results show that personal scientific codes are running many times slower than they should and that the problem is pervasive. Computational scientists are not sloppy programmers; however, few are trained in the art of computer programming or code optimization. I found that most have a working knowledge of some programming language and standard software engineering practices; but they do not know, or think about, how to make their programs run faster. They simply do not know the standard techniques used to make codes run faster. In fact, they do not even perceive that such techniques exist. The case studies described in this paper show that applying simple, well known techniques can significantly increase the performance of personal codes. It is important that the scientific community and the Government agencies that support scientific research find ways to better educate academic scientific programmers. The inefficiency of their codes is so bad that it is retarding both the quality and progress of scientific research. # cacheperformance redundantoperations loopstructures performanceimprovement 1 x x 15.5 2 x 2.8 3 x x 2.5 4 x 2.1 5 x x 2.0 6 x 5.0 7 x 5.8 8 x 6.3 9 2.2 10 x x 3.3 Table 1 — Area of improvement and performance gains of 10 codes The remainder of the paper is organized as follows: sections 2, 3, and 4 discuss the three most common sources of inefficiencies in the codes studied. These are cache performance, redundant operations, and loop structures. Each section includes several examples. The last section summaries the work and suggests a possible solution to the issues raised. Optimizing cache performance Commodity microprocessor systems use caches to increase memory bandwidth and reduce memory latencies. Typical latencies from processor to L1, L2, local, and remote memory are 3, 10, 50, and 200 cycles, respectively. Moreover, bandwidth falls off dramatically as memory distances increase. Programs that do not use cache effectively run many times slower than programs that do. When optimizing for cache, the biggest performance gains are achieved by accessing data in cache order and reusing data to amortize the overhead of cache misses. Secondary considerations are prefetching, associativity, and replacement; however, the understanding and analysis required to optimize for the latter are probably beyond the capabilities of the non-expert. Much can be gained simply by accessing data in the correct order and maximizing data reuse. 6 out of the 10 codes studied here benefited from such high level optimizations. Array Accesses The most important cache optimization is the most basic: accessing Fortran array elements in column order and C array elements in row order. Four of the ten codes—1, 2, 4, and 10—got it wrong. Compilers will restructure nested loops to optimize cache performance, but may not do so if the loop structure is too complex, or the loop body includes conditionals, complex addressing, or function calls. In code 1, the compiler failed to invert a key loop because of complex addressing do I = 0, 1010, delta_x IM = I - delta_x IP = I + delta_x do J = 5, 995, delta_x JM = J - delta_x JP = J + delta_x T1 = CA1(IP, J) + CA1(I, JP) T2 = CA1(IM, J) + CA1(I, JM) S1 = T1 + T2 - 4 * CA1(I, J) CA(I, J) = CA1(I, J) + D * S1 end do end do In code 2, the culprit is conditionals do I = 1, N do J = 1, N If (IFLAG(I,J) .EQ. 0) then T1 = Value(I, J-1) T2 = Value(I-1, J) T3 = Value(I, J) T4 = Value(I+1, J) T5 = Value(I, J+1) Value(I,J) = 0.25 * (T1 + T2 + T5 + T4) Delta = ABS(T3 - Value(I,J)) If (Delta .GT. MaxDelta) MaxDelta = Delta endif enddo enddo I fixed both programs by inverting the loops by hand. Code 10 has three-dimensional arrays and triply nested loops. The structure of the most computationally intensive loops is too complex to invert automatically or by hand. The only practical solution is to transpose the arrays so that the dimension accessed by the innermost loop is in cache order. The arrays can be transposed at construction or prior to entering a computationally intensive section of code. The former requires all array references to be modified, while the latter is cost effective only if the cost of the transpose is amortized over many accesses. I used the second approach to optimize code 10. Code 5 has four-dimensional arrays and loops are nested four deep. For all of the reasons cited above the compiler is not able to restructure three key loops. Assume C arrays and let the four dimensions of the arrays be i, j, k, and l. In the original code, the index structure of the three loops is L1: for i L2: for i L3: for i for l for l for j for k for j for k for j for k for l So only L3 accesses array elements in cache order. L1 is a very complex loop—much too complex to invert. I brought the loop into cache alignment by transposing the second and fourth dimensions of the arrays. Since the code uses a macro to compute all array indexes, I effected the transpose at construction and changed the macro appropriately. The dimensions of the new arrays are now: i, l, k, and j. L3 is a simple loop and easily inverted. L2 has a loop-carried scalar dependence in k. By promoting the scalar name that carries the dependence to an array, I was able to invert the third and fourth subloops aligning the loop with cache. Code 5 is by far the most difficult of the four codes to optimize for array accesses; but the knowledge required to fix the problems is no more than that required for the other codes. I would judge this code at the limits of, but not beyond, the capabilities of appropriately trained computational scientists. Array Strides When a cache miss occurs, a line (64 bytes) rather than just one word is loaded into the cache. If data is accessed stride 1, than the cost of the miss is amortized over 8 words. Any stride other than one reduces the cost savings. Two of the ten codes studied suffered from non-unit strides. The codes represent two important classes of "strided" codes. Code 1 employs a multi-grid algorithm to reduce time to convergence. The grids are every tenth, fifth, second, and unit element. Since time to convergence is inversely proportional to the distance between elements, coarse grids converge quickly providing good starting values for finer grids. The better starting values further reduce the time to convergence. The downside is that grids of every nth element, n > 1, introduce non-unit strides into the computation. In the original code, much of the savings of the multi-grid algorithm were lost due to this problem. I eliminated the problem by compressing (copying) coarse grids into continuous memory, and rewriting the computation as a function of the compressed grid. On convergence, I copied the final values of the compressed grid back to the original grid. The savings gained from unit stride access of the compressed grid more than paid for the cost of copying. Using compressed grids, the loop from code 1 included in the previous section becomes do j = 1, GZ do i = 1, GZ T1 = CA(i+0, j-1) + CA(i-1, j+0) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) S1 = T1 + T4 - 4 * CA1(i+0, j+0) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 enddo enddo where CA and CA1 are compressed arrays of size GZ. Code 7 traverses a list of objects selecting objects for later processing. The labels of the selected objects are stored in an array. The selection step has unit stride, but the processing steps have irregular stride. A fix is to save the parameters of the selected objects in temporary arrays as they are selected, and pass the temporary arrays to the processing functions. The fix is practical if the same parameters are used in selection as in processing, or if processing comprises a series of distinct steps which use overlapping subsets of the parameters. Both conditions are true for code 7, so I achieved significant improvement by copying parameters to temporary arrays during selection. Data reuse In the previous sections, we optimized for spatial locality. It is also important to optimize for temporal locality. Once read, a datum should be used as much as possible before it is forced from cache. Loop fusion and loop unrolling are two techniques that increase temporal locality. Unfortunately, both techniques increase register pressure—as loop bodies become larger, the number of registers required to hold temporary values grows. Once register spilling occurs, any gains evaporate quickly. For multiprocessors with small register sets or small caches, the sweet spot can be very small. In the ten codes presented here, I found no opportunities for loop fusion and only two opportunities for loop unrolling (codes 1 and 3). In code 1, unrolling the outer and inner loop one iteration increases the number of result values computed by the loop body from 1 to 4, do J = 1, GZ-2, 2 do I = 1, GZ-2, 2 T1 = CA1(i+0, j-1) + CA1(i-1, j+0) T2 = CA1(i+1, j-1) + CA1(i+0, j+0) T3 = CA1(i+0, j+0) + CA1(i-1, j+1) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) T5 = CA1(i+2, j+0) + CA1(i+1, j+1) T6 = CA1(i+1, j+1) + CA1(i+0, j+2) T7 = CA1(i+2, j+1) + CA1(i+1, j+2) S1 = T1 + T4 - 4 * CA1(i+0, j+0) S2 = T2 + T5 - 4 * CA1(i+1, j+0) S3 = T3 + T6 - 4 * CA1(i+0, j+1) S4 = T4 + T7 - 4 * CA1(i+1, j+1) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 CA(i+1, j+0) = CA1(i+1, j+0) + DD * S2 CA(i+0, j+1) = CA1(i+0, j+1) + DD * S3 CA(i+1, j+1) = CA1(i+1, j+1) + DD * S4 enddo enddo The loop body executes 12 reads, whereas as the rolled loop shown in the previous section executes 20 reads to compute the same four values. In code 3, two loops are unrolled 8 times and one loop is unrolled 4 times. Here is the before for (k = 0; k < NK[u]; k++) { sum = 0.0; for (y = 0; y < NY; y++) { sum += W[y][u][k] * delta[y]; } backprop[i++]=sum; } and after code for (k = 0; k < KK - 8; k+=8) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (y = 0; y < NY; y++) { sum0 += W[y][0][k+0] * delta[y]; sum1 += W[y][0][k+1] * delta[y]; sum2 += W[y][0][k+2] * delta[y]; sum3 += W[y][0][k+3] * delta[y]; sum4 += W[y][0][k+4] * delta[y]; sum5 += W[y][0][k+5] * delta[y]; sum6 += W[y][0][k+6] * delta[y]; sum7 += W[y][0][k+7] * delta[y]; } backprop[k+0] = sum0; backprop[k+1] = sum1; backprop[k+2] = sum2; backprop[k+3] = sum3; backprop[k+4] = sum4; backprop[k+5] = sum5; backprop[k+6] = sum6; backprop[k+7] = sum7; } for one of the loops unrolled 8 times. Optimizing for temporal locality is the most difficult optimization considered in this paper. The concepts are not difficult, but the sweet spot is small. Identifying where the program can benefit from loop unrolling or loop fusion is not trivial. Moreover, it takes some effort to get it right. Still, educating scientific programmers about temporal locality and teaching them how to optimize for it will pay dividends. Reducing instruction count Execution time is a function of instruction count. Reduce the count and you usually reduce the time. The best solution is to use a more efficient algorithm; that is, an algorithm whose order of complexity is smaller, that converges quicker, or is more accurate. Optimizing source code without changing the algorithm yields smaller, but still significant, gains. This paper considers only the latter because the intent is to study how much better codes can run if written by programmers schooled in basic code optimization techniques. The ten codes studied benefited from three types of "instruction reducing" optimizations. The two most prevalent were hoisting invariant memory and data operations out of inner loops. The third was eliminating unnecessary data copying. The nature of these inefficiencies is language dependent. Memory operations The semantics of C make it difficult for the compiler to determine all the invariant memory operations in a loop. The problem is particularly acute for loops in functions since the compiler may not know the values of the function's parameters at every call site when compiling the function. Most compilers support pragmas to help resolve ambiguities; however, these pragmas are not comprehensive and there is no standard syntax. To guarantee that invariant memory operations are not executed repetitively, the user has little choice but to hoist the operations by hand. The problem is not as severe in Fortran programs because in the absence of equivalence statements, it is a violation of the language's semantics for two names to share memory. Codes 3 and 5 are C programs. In both cases, the compiler did not hoist all invariant memory operations from inner loops. Consider the following loop from code 3 for (y = 0; y < NY; y++) { i = 0; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += delta[y] * I1[i++]; } } } Since dW[y][u] can point to the same memory space as delta for one or more values of y and u, assignment to dW[y][u][k] may change the value of delta[y]. In reality, dW and delta do not overlap in memory, so I rewrote the loop as for (y = 0; y < NY; y++) { i = 0; Dy = delta[y]; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += Dy * I1[i++]; } } } Failure to hoist invariant memory operations may be due to complex address calculations. If the compiler can not determine that the address calculation is invariant, then it can hoist neither the calculation nor the associated memory operations. As noted above, code 5 uses a macro to address four-dimensional arrays #define MAT4D(a,q,i,j,k) (double *)((a)->data + (q)*(a)->strides[0] + (i)*(a)->strides[3] + (j)*(a)->strides[2] + (k)*(a)->strides[1]) The macro is too complex for the compiler to understand and so, it does not identify any subexpressions as loop invariant. The simplest way to eliminate the address calculation from the innermost loop (over i) is to define a0 = MAT4D(a,q,0,j,k) before the loop and then replace all instances of *MAT4D(a,q,i,j,k) in the loop with a0[i] A similar problem appears in code 6, a Fortran program. The key loop in this program is do n1 = 1, nh nx1 = (n1 - 1) / nz + 1 nz1 = n1 - nz * (nx1 - 1) do n2 = 1, nh nx2 = (n2 - 1) / nz + 1 nz2 = n2 - nz * (nx2 - 1) ndx = nx2 - nx1 ndy = nz2 - nz1 gxx = grn(1,ndx,ndy) gyy = grn(2,ndx,ndy) gxy = grn(3,ndx,ndy) balance(n1,1) = balance(n1,1) + (force(n2,1) * gxx + force(n2,2) * gxy) * h1 balance(n1,2) = balance(n1,2) + (force(n2,1) * gxy + force(n2,2) * gyy)*h1 end do end do The programmer has written this loop well—there are no loop invariant operations with respect to n1 and n2. However, the loop resides within an iterative loop over time and the index calculations are independent with respect to time. Trading space for time, I precomputed the index values prior to the entering the time loop and stored the values in two arrays. I then replaced the index calculations with reads of the arrays. Data operations Ways to reduce data operations can appear in many forms. Implementing a more efficient algorithm produces the biggest gains. The closest I came to an algorithm change was in code 4. This code computes the inner product of K-vectors A(i) and B(j), 0 = i < N, 0 = j < M, for most values of i and j. Since the program computes most of the NM possible inner products, it is more efficient to compute all the inner products in one triply-nested loop rather than one at a time when needed. The savings accrue from reading A(i) once for all B(j) vectors and from loop unrolling. for (i = 0; i < N; i+=8) { for (j = 0; j < M; j++) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (k = 0; k < K; k++) { sum0 += A[i+0][k] * B[j][k]; sum1 += A[i+1][k] * B[j][k]; sum2 += A[i+2][k] * B[j][k]; sum3 += A[i+3][k] * B[j][k]; sum4 += A[i+4][k] * B[j][k]; sum5 += A[i+5][k] * B[j][k]; sum6 += A[i+6][k] * B[j][k]; sum7 += A[i+7][k] * B[j][k]; } C[i+0][j] = sum0; C[i+1][j] = sum1; C[i+2][j] = sum2; C[i+3][j] = sum3; C[i+4][j] = sum4; C[i+5][j] = sum5; C[i+6][j] = sum6; C[i+7][j] = sum7; }} This change requires knowledge of a typical run; i.e., that most inner products are computed. The reasons for the change, however, derive from basic optimization concepts. It is the type of change easily made at development time by a knowledgeable programmer. In code 5, we have the data version of the index optimization in code 6. Here a very expensive computation is a function of the loop indices and so cannot be hoisted out of the loop; however, the computation is invariant with respect to an outer iterative loop over time. We can compute its value for each iteration of the computation loop prior to entering the time loop and save the values in an array. The increase in memory required to store the values is small in comparison to the large savings in time. The main loop in Code 8 is doubly nested. The inner loop includes a series of guarded computations; some are a function of the inner loop index but not the outer loop index while others are a function of the outer loop index but not the inner loop index for (j = 0; j < N; j++) { for (i = 0; i < M; i++) { r = i * hrmax; R = A[j]; temp = (PRM[3] == 0.0) ? 1.0 : pow(r, PRM[3]); high = temp * kcoeff * B[j] * PRM[2] * PRM[4]; low = high * PRM[6] * PRM[6] / (1.0 + pow(PRM[4] * PRM[6], 2.0)); kap = (R > PRM[6]) ? high * R * R / (1.0 + pow(PRM[4]*r, 2.0) : low * pow(R/PRM[6], PRM[5]); < rest of loop omitted > }} Note that the value of temp is invariant to j. Thus, we can hoist the computation for temp out of the loop and save its values in an array. for (i = 0; i < M; i++) { r = i * hrmax; TEMP[i] = pow(r, PRM[3]); } [N.B. – the case for PRM[3] = 0 is omitted and will be reintroduced later.] We now hoist out of the inner loop the computations invariant to i. Since the conditional guarding the value of kap is invariant to i, it behooves us to hoist the computation out of the inner loop, thereby executing the guard once rather than M times. The final version of the code is for (j = 0; j < N; j++) { R = rig[j] / 1000.; tmp1 = kcoeff * par[2] * beta[j] * par[4]; tmp2 = 1.0 + (par[4] * par[4] * par[6] * par[6]); tmp3 = 1.0 + (par[4] * par[4] * R * R); tmp4 = par[6] * par[6] / tmp2; tmp5 = R * R / tmp3; tmp6 = pow(R / par[6], par[5]); if ((par[3] == 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp5; } else if ((par[3] == 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp4 * tmp6; } else if ((par[3] != 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp5; } else if ((par[3] != 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp4 * tmp6; } for (i = 0; i < M; i++) { kap = KAP[i]; r = i * hrmax; < rest of loop omitted > } } Maybe not the prettiest piece of code, but certainly much more efficient than the original loop, Copy operations Several programs unnecessarily copy data from one data structure to another. This problem occurs in both Fortran and C programs, although it manifests itself differently in the two languages. Code 1 declares two arrays—one for old values and one for new values. At the end of each iteration, the array of new values is copied to the array of old values to reset the data structures for the next iteration. This problem occurs in Fortran programs not included in this study and in both Fortran 77 and Fortran 90 code. Introducing pointers to the arrays and swapping pointer values is an obvious way to eliminate the copying; but pointers is not a feature that many Fortran programmers know well or are comfortable using. An easy solution not involving pointers is to extend the dimension of the value array by 1 and use the last dimension to differentiate between arrays at different times. For example, if the data space is N x N, declare the array (N, N, 2). Then store the problem’s initial values in (_, _, 2) and define the scalar names new = 2 and old = 1. At the start of each iteration, swap old and new to reset the arrays. The old–new copy problem did not appear in any C program. In programs that had new and old values, the code swapped pointers to reset data structures. Where unnecessary coping did occur is in structure assignment and parameter passing. Structures in C are handled much like scalars. Assignment causes the data space of the right-hand name to be copied to the data space of the left-hand name. Similarly, when a structure is passed to a function, the data space of the actual parameter is copied to the data space of the formal parameter. If the structure is large and the assignment or function call is in an inner loop, then copying costs can grow quite large. While none of the ten programs considered here manifested this problem, it did occur in programs not included in the study. A simple fix is always to refer to structures via pointers. Optimizing loop structures Since scientific programs spend almost all their time in loops, efficient loops are the key to good performance. Conditionals, function calls, little instruction level parallelism, and large numbers of temporary values make it difficult for the compiler to generate tightly packed, highly efficient code. Conditionals and function calls introduce jumps that disrupt code flow. Users should eliminate or isolate conditionls to their own loops as much as possible. Often logical expressions can be substituted for if-then-else statements. For example, code 2 includes the following snippet MaxDelta = 0.0 do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) if (Delta > MaxDelta) MaxDelta = Delta enddo enddo if (MaxDelta .gt. 0.001) goto 200 Since the only use of MaxDelta is to control the jump to 200 and all that matters is whether or not it is greater than 0.001, I made MaxDelta a boolean and rewrote the snippet as MaxDelta = .false. do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) MaxDelta = MaxDelta .or. (Delta .gt. 0.001) enddo enddo if (MaxDelta) goto 200 thereby, eliminating the conditional expression from the inner loop. A microprocessor can execute many instructions per instruction cycle. Typically, it can execute one or more memory, floating point, integer, and jump operations. To be executed simultaneously, the operations must be independent. Thick loops tend to have more instruction level parallelism than thin loops. Moreover, they reduce memory traffice by maximizing data reuse. Loop unrolling and loop fusion are two techniques to increase the size of loop bodies. Several of the codes studied benefitted from loop unrolling, but none benefitted from loop fusion. This observation is not too surpising since it is the general tendency of programmers to write thick loops. As loops become thicker, the number of temporary values grows, increasing register pressure. If registers spill, then memory traffic increases and code flow is disrupted. A thick loop with many temporary values may execute slower than an equivalent series of thin loops. The biggest gain will be achieved if the thick loop can be split into a series of independent loops eliminating the need to write and read temporary arrays. I found such an occasion in code 10 where I split the loop do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do into two disjoint loops do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) end do end do do i = 1, n do j = 1, m C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do Conclusions Over the course of the last year, I have had the opportunity to work with over two dozen academic scientific programmers at leading research universities. Their research interests span a broad range of scientific fields. Except for two programs that relied almost exclusively on library routines (matrix multiply and fast Fourier transform), I was able to improve significantly the single processor performance of all codes. Improvements range from 2x to 15.5x with a simple average of 4.75x. Changes to the source code were at a very high level. I did not use sophisticated techniques or programming tools to discover inefficiencies or effect the changes. Only one code was parallel despite the availability of parallel systems to all developers. Clearly, we have a problem—personal scientific research codes are highly inefficient and not running parallel. The developers are unaware of simple optimization techniques to make programs run faster. They lack education in the art of code optimization and parallel programming. I do not believe we can fix the problem by publishing additional books or training manuals. To date, the developers in questions have not studied the books or manual available, and are unlikely to do so in the future. Short courses are a possible solution, but I believe they are too concentrated to be much use. The general concepts can be taught in a three or four day course, but that is not enough time for students to practice what they learn and acquire the experience to apply and extend the concepts to their codes. Practice is the key to becoming proficient at optimization. I recommend that graduate students be required to take a semester length course in optimization and parallel programming. We would never give someone access to state-of-the-art scientific equipment costing hundreds of thousands of dollars without first requiring them to demonstrate that they know how to use the equipment. Yet the criterion for time on state-of-the-art supercomputers is at most an interesting project. Requestors are never asked to demonstrate that they know how to use the system, or can use the system effectively. A semester course would teach them the required skills. Government agencies that fund academic scientific research pay for most of the computer systems supporting scientific research as well as the development of most personal scientific codes. These agencies should require graduate schools to offer a course in optimization and parallel programming as a requirement for funding. About the Author John Feo received his Ph.D. in Computer Science from The University of Texas at Austin in 1986. After graduate school, Dr. Feo worked at Lawrence Livermore National Laboratory where he was the Group Leader of the Computer Research Group and principal investigator of the Sisal Language Project. In 1997, Dr. Feo joined Tera Computer Company where he was project manager for the MTA, and oversaw the programming and evaluation of the MTA at the San Diego Supercomputer Center. In 2000, Dr. Feo joined Sun Microsystems as an HPC application specialist. He works with university research groups to optimize and parallelize scientific codes. Dr. Feo has published over two dozen research articles in the areas of parallel parallel programming, parallel programming languages, and application performance.

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