<rss version="2.0"><channel><title>RSS feed for InstantSpot site EdomGroup Blog</title><link>http://edomgroup.instantspot.com</link><description>ColdFusion, Java, Computer Science, and the musings of inspired developers...</description><language>en-us</language><copyright>This work is Copyright &#xA9; 2008 by EdomGroup Blog</copyright><generator>RSSVille ColdFusion FeedMaker, version 1.0</generator><pubDate>Thu, 21 Aug 2008 14:51:44 GMT</pubDate><item><title>Font rendering difference in Firefox 2.0 RC2 on Linux</title><link>http://edomgroup.instantspot.com/blog/2006/10/10/Font-rendering-difference-in-Firefox-20-RC2-on-Linux</link><description>&lt;p&gt;
&lt;img src=&quot;/userfiles/032907/112/firefox20rc3_linux_diff.png&quot; alt=&quot;Font rendering difference&quot; /&gt; &lt;br /&gt;
&lt;/p&gt;
&lt;p&gt;
I was reading an entry on   &lt;a href=&quot;http://www.slashdot.org/&quot;&gt;slashdot&lt;/a&gt;  about the new   &lt;a href=&quot;http://www.mozilla.com/en-US/firefox/2.0/releasenotes/&quot;&gt;  Firefox 2.0 RC2&lt;/a&gt;
release and decided to give it a try. The new aesthetic changes are
nice and I definitely like the new add-on&amp;#39;s manager which manages
themes and extensions. While I&amp;#39;m typing this I realize another added
feature is a spell check for form text area fields. One thing that I
couldn&amp;#39;t figure out, though, was why slashdot looked different on
firefox 2.0 than on firefox 1.5, so I finally took a screenshot of the
two and held them side-by-side. The image above shows the results. On
the left is version 1.5 and on the right is version 2.0 rc3. I checked
the settings of the fonts and the only thing in 2.0 rc3 that is
different is there is no font resolution (DPI) setting. The name of the
fonts used are the same and at the same sizes. I&amp;#39;m not sure if this
font difference is the same under Windows since my primary system is a
Linux box.
&lt;/p&gt;
</description><pubDate>Tue, 10 Oct 2006 12:00:45 GMT</pubDate><guid>http://edomgroup.instantspot.com/blog/2006/10/10/Font-rendering-difference-in-Firefox-20-RC2-on-Linux</guid><category>Linux</category></item><item><title>Java performance analysis - a real life example</title><link>http://edomgroup.instantspot.com/blog/2006/06/01/Java-performance-analysis--a-real-life-example</link><description>For my thesis I&amp;#39;m writing a Java program that parses two text files
(representing satellite images) and performs some analysis on them. The
two files are 128 MB and each was taking ~33 seconds to parse. The
analysis takes only a fraction of that time, then the results are
written back to an output file. 
&lt;p&gt;
Thinking this was very slow, since the files should be pretty much
sequential access off the disk and the read-ahead-buffer would have
cached the next block, I decided to dig a little. I rewrote the parsing
algorithm in C since it&amp;#39;s easier to manage the low-level IO. I used the
file stream functions (fopen,fread,fclose) provided by stdio. When I ran the program it took ~10 seconds.    
&lt;/p&gt;
&lt;p&gt;
This was insane. Java does have more overhead due to its garbage
collection, HotSpot optimizer, and object memory management but it
should not be a magnitude of 3x slower. After a few minutes looking at
the Java implementation I realized I had forgotten to instantiate a BufferedReader. I immediately jumped to the documentation and there it was, each call to the read method of the FileReader blocks until data is available. If no BufferedReader
is supplied this means a context switch for each call. No wonder Java
was taking so long. Adding the buffering I reran the algorithm and wow,
13 seconds. Not to bad for a &amp;ldquo;boated&amp;rdquo; language. 
&lt;/p&gt;
&lt;p&gt;
Figuring I had more places in my program which could be optimized I
began looking. The first place I went was to one of the main methods,
the one which did the analysis on each image. The logic was pretty
simple, find the next pattern in the given image, see if it exists in a
lookup table, if so update its statistics or create a new key and add
it as a new entry. The data structure I was using for the lookup table
was a simple HashMap
object. When writing that section of the program I was still relatively
new to the Java language and just went with what I&amp;#39;d used in other
languages, an associative array. 
&lt;/p&gt;
&lt;p&gt;
I did a little analysis with some System.out.println
statements and found that on average there was a 14,000 : 15 ratio
between reads and writes on the lookup table. Knowing this ratio was
pretty stable (I wasn&amp;#39;t going to get anywhere near 50% writes) I
decided a better data structure would be something with read time of
O(n) or less, instead of O(h(k)) where h(k) was the hash function on
the key. This meant an array would be best, but I didn&amp;#39;t want the
hassle of managing the size of the array as new patterns where found.
Java had to have something for this. After a little searching I found java.util.concurrent.CopyOnWriteArrayList. This was exactly what I needed. After a little tweaking the new code ran in 1/3 the time.    
&lt;/p&gt;
&lt;p&gt;
After this I wrote some small timed programs to examine the running time of the HashMap data structure and the CopyOnWriteArrayList. What I found was as the mutations  approached 25% the CopyOnWriteArrayList&amp;#39;s
performance slowed at an exponential rate. Finally, I couldn&amp;#39;t evaluate
the performance because the tests were taking too long. On the other
hand, the HashMap data structure had a fairly constant performance time irrelevant on the read/write ratio. 
&lt;/p&gt;
</description><pubDate>Fri, 02 Jun 2006 03:43:25 GMT</pubDate><guid>http://edomgroup.instantspot.com/blog/2006/06/01/Java-performance-analysis--a-real-life-example</guid><category>Java</category></item></channel></rss>