A bit more on the abstract and some <p> formatting
authorMatthew McPherrin <mimcpher@csclub.uwaterloo.ca>
Tue, 31 May 2011 02:05:33 +0000 (22:05 -0400)
committerMatthew McPherrin <mimcpher@csclub.uwaterloo.ca>
Tue, 31 May 2011 02:05:33 +0000 (22:05 -0400)
events.xml

index 03181a6..6935a87 100644 (file)
                focus of this talk will be nVidia's CUDA architecture.
        </short>
        <abstract>
-               GPGPU (general purpose graphics processing unit) computing is an
-               expanding area of interest, with applications in physics, chemistry,
-               applied math, finance, and other fields. nVidia has created an
-               architecture named CUDA to allow programmers to use graphics cards
-               without having to write PTX assembly or understand OpenGL. CUDA is
-               designed to allow for high-performance parallel computation controlled
-               from the CPU while granting the user fine control over the behaviour
-               and performance of the device.
-
-               In this talk, I'll discuss the basics of nVidia's CUDA architecture
-               (with most emphasis on the CUDA C extensions), the GPGPU programming
-               environment, optimizing code written for the graphics card, algorithms
-               with noteworthy performance on GPU, libraries and tools available to
-               the GPGPU programmer, and some applications to condensed matter
-               physics. No physics background required!
+               <p> This is the first of our member talks for the term, presented by
+                       CSC member and Waterloo undergraduate student Katie Hyatt
+               </p>
+               <p>
+                       GPGPU (general purpose graphics processing unit) computing is an
+                       expanding area of interest, with applications in physics, chemistry,
+                       applied math, finance, and other fields. nVidia has created an
+                       architecture named CUDA to allow programmers to use graphics cards
+                       without having to write PTX assembly or understand OpenGL. CUDA is
+                       designed to allow for high-performance parallel computation controlled
+                       from the CPU while granting the user fine control over the behaviour
+                       and performance of the device.
+               </p>
+
+               <p>
+                       In this talk, I'll discuss the basics of nVidia's CUDA architecture
+                       (with most emphasis on the CUDA C extensions), the GPGPU programming
+                       environment, optimizing code written for the graphics card, algorithms
+                       with noteworthy performance on GPU, libraries and tools available to
+                       the GPGPU programmer, and some applications to condensed matter
+                       physics. No physics background required!
+               </p>
        </abstract>
 </eventitem>
 <eventitem date="2011-06-03" time="7 PM" room="Comfy Lounge" title="Code Party 1">