Date:         Wed, 21 Jul 2010 16:11:43 -0500
Reply-To:     Michelle Zunnurain <michelle_zunnurain@HOTMAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Michelle Zunnurain <michelle_zunnurain@HOTMAIL.COM>
Subject:      In search of a more efficient program
Content-Type: text/plain; charset="iso-8859-1"

Andy,

 

I am going through the same thing myself.  Trying to make performance improvements to a large, complex, time consuming process.

 

I started out by setting options mprintnest to make the log easier to trace back to the program, and fullstimer to get more specific information from the log.  Also %logparse was a nice addition which I wrapped in loops to summarize my steps and put the results to a spreadsheet.  

%logparse will show you the stepname, # obs, # vars, realtime, usertime, cputime, memused, plus many more.  It can also be filtered to get just the stepname='SAS' as a summary of multiple logs.  Then run over multiple time periods, in my case by month, to see the trend.

 

Then I chose steps to target based on the longest real time.  Then I tested by trying lots 

of different coding tricks, some found online, some found here, some found in SAS

documentation and other papers.  I usually tested proc sql, vs merge vs hash join.  If the datasets were too large to work with in a timely manner, I extracted a test batch of records and used those for testing, then proceeded to a full test.  I compared the number of records and the number of variables to make sure they matched.  Then did a proc compare to make sure the dataset matched exactly.

 

If everything was successful/beneficial to this point then I moved the code into production.

The only way to accomplish what you seek is by a lot of work/testing/guessing and running a lot of test code.

 

Good luck.

 
                                          
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