Moved latest talk to the top

data/talks.yaml

@@ -4,6 +4,16 @@
 # references. Nominally we will use /talks.
 
 cards:
+  - meeting: ParCo Conference 2017
+    location: Bologna, Italy
+    date: September 2017
+    title: "An Efficient SIMD Implementation of Pseudo-Verlet Lists for Neighbour Interactions in Particle-Based Codes"
+    author: James S. Willis
+    abstract: "In particle-based simulations, neighbour finding (i.e finding pairs of particles to interact within a given range) is the most time consuming part of the computation. One of the best such algorithms, which can be used for both Molecular Dynamics (MD) and Smoothed Particle Hydrodynamics (SPH) simulations is the pseudo-Verlet list algorithm. This algorithm, however, does not vectorize trivially, and hence makes it difficult to exploit SIMD-parallel architectures. In this paper, we present several novel modifications as well as a vectorization strategy for the algorithm which lead to overall speed-ups over the scalar version of the algorithm of 2.24x for the AVX instruction set (SIMD width of 8), 2.43x for AVX2, and 4.07x for AVX-512 (SIMD width of 16)."
+    links:
+      - href: "ParCo_2017_Bologna_Talk.pdf"
+        name: Slides
+
   - meeting: UK National Astronomy Meeting
     location: Hull, UK 
     date: July 2017 
@@ -75,13 +85,3 @@ cards:
     links:
       - href: "Ascona_2013.pdf"
         name: Slides
-
-  - meeting: ParCo Conference 2017
-    location: Bologna, Italy
-    date: September 2017
-    title: "An Efficient SIMD Implementation of Pseudo-Verlet Lists for Neighbour Interactions in Particle-Based Codes"
-    author: James S. Willis
-    abstract: "In particle-based simulations, neighbour finding (i.e finding pairs of particles to interact within a given range) is the most time consuming part of the computation. One of the best such algorithms, which can be used for both Molecular Dynamics (MD) and Smoothed Particle Hydrodynamics (SPH) simulations is the pseudo-Verlet list algorithm. This algorithm, however, does not vectorize trivially, and hence makes it difficult to exploit SIMD-parallel architectures. In this paper, we present several novel modifications as well as a vectorization strategy for the algorithm which lead to overall speed-ups over the scalar version of the algorithm of 2.24x for the AVX instruction set (SIMD width of 8), 2.43x for AVX2, and 4.07x for AVX-512 (SIMD width of 16)."
-    links:
-      - href: "ParCo_2017_Bologna_Talk.pdf"
-        name: Slides