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  <div class="vevent h-event"> <h1 class="summary p-name">Galois Tech Talk: Faster Persistent Data Structures Through Hashing</h1> <div class='date'><time class="dtstart dt-start" title="2011-02-15T10:30:00" datetime="2011-02-15T10:30:00">Tuesday, February 15, 2011 from 10:30</time>–<time class="dtend dt-end" title="2011-02-15T11:30:00" datetime="2011-02-15T11:30:00">11:30am</time></div> <div class="location vcard p-location h-card"> <a class="url" href='http://calagator.org/venues/202390439'><span class='fn org p-name'>Galois, Inc</span></a> <div class="adr p-adr h-adr"> <div class="street-address p-street-address">421 SW 6th Ave. Suite 300</div> <span class="locality p-locality">Portland</span> , <span class="region p-region">OR</span> <span class="postal-code p-postal-code">97204</span> <div class="country-name p-country-name">US</div> (<a href='https://maps.google.com/maps?q=421%20SW%206th%20Ave.%20Suite%20300,%20Portland%20OR%2097204%20US'>map</a>) </div> </div> <div class="description p-description"> <p>Presented by Johan Tibell.</p> <p>The most commonly used map (dictionary) data type in Haskell is implemented using a size balanced tree. While size balanced trees provide good asymptotic performance, their real world performance is not stellar, especially when used with keys which are expensive to compare, such as strings.</p> <p>In this talk we will look at two different map implementations that use hashing to achieve better real world performance. The implementations have different performance characteristics: one provides very fast look-ups while the other trades better insert performance for somewhat slower look-ups. I will describe the design of these data structures and show some early benchmark results.</p> </div> <h3>Links</h3> <ul> <li><a class="url u-url" href="http://corp.galois.com/blog/2011/2/8/tech-talk-faster-persistent-data-structures-through-hashing.html">Website</a></li> </ul> </div>