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  <event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/176/NASA-LambdaRAM-project">
    <rdfs:label><![CDATA[NASA/LambdaRAM project]]></rdfs:label>
    <event:title><![CDATA[NASA/LambdaRAM project]]></event:title>
    <event:speaker><person:Alumnus rdf:about="http://ebiquity.umbc.edu/person/html/Anand/Patwardhan/"><person:name><![CDATA[Anand  Patwardhan]]></person:name><rdfs:label><![CDATA[Anand  Patwardhan]]></rdfs:label></person:Alumnus></event:speaker>
    <event:startDate rdf:datatype="&xsd;dateTime">2006-09-19T11:45:00-05:00</event:startDate>
    <event:endDate rdf:datatype="&xsd;dateTime">2006-09-19T12:30:00-05:00</event:endDate>
    <event:location><![CDATA[325b ITE]]></event:location>
    <event:abstract><![CDATA[
Supercomputing applications including weather forecasting, hurricane
modeling, and visualisation are characterized by the use of extremely
large datasets (terabytes of data). These applications typically
operate in multiple passes over the datasets, each pass producing an
intermediate result used by the next. Due to the mechanical components
involved in the conventional disks, the speed gap between processing
times and disk access times has continued to widen, disk access times
remain the bottlneck when accessing large datasets. Specialized and
expensive hardware is often required to meet the high-data rate
requirements of such applications.
<p>
The NASA/LambdaRAM project, a collaboration amongst UMBC, EVL/UIC,
NASA GSFC and Northrop Grumman seeks to leverage high-speed optical
networks (Lambdas) and the aggregated semiconductor memory of idle
server blades interconnected by such networks, to provide much faster
access times to extremely large datasets, to speedup supercomputing
applications.
<p>
I have been working on part of this project over the summer for
NASA/GSFC. I will be talking about the challenges we have faced, the
solutions, and future work. ]]></event:abstract>
    <event:tag><![CDATA[high performance computing]]></event:tag>
  </event:Event>

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