<div dir="ltr"><div><div>Hi Sarah and Team BioHaskell,<br><br></div>Do you suppose the performance tradeoffs would be any different after the application is parallelized? (For example, some portion of the workflow that is more difficult to parallelize begins to dominate CPU usage -- or perhaps one data structure parallelizes better than another.) If so, would it make sense to first do the parallelization as you described in your project plan, and then return to profiling-guided optimizations?<br>
<br></div><div>Cheers,<br></div><div>Eric<br></div><div><br></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Mon, Jun 30, 2014 at 6:46 AM, Sarah Berkemer <span dir="ltr"><<a href="mailto:sarah.berkemer@gmail.com" target="_blank">sarah.berkemer@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><div><div><div>Hello,<br><br></div>here is my blogpost for this week: <a href="http://biohaskell.org/GSoC_blog/Weeks_6and7" target="_blank">http://biohaskell.org/GSoC_blog/Weeks_6and7</a><br>
</div>Last week I did some debugging and further changes in the current version of the code. I also did profiling statistics from both versions, the original and the current one with a small input. It can be seen that with just a small input, both version need almost the same time. For a large input, the original version is still faster. This week I will try to find the reason for that.<br>
<br></div>Have a nice week,<br><br></div>Sarah<br></div>
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