Iteration Aware Prefetching For Unstructured Grids

10/2 Department Seminar 

Oyindamola Akande - Doctoral Student in The Department of Computer and Information Science at the University of Mississippi

3:00 p.m. Wednesday, Oct. 2 2013 
235 Weir Hall 

Iteration Aware Prefetching For Unstructured Grids

ABSTRACT: Due to the increasing quality of instruments and availability of computational resources, the size of spatial scientific datasets has been steadily increasing. However, much of the research on efficient  storage and access to spatial datasets has focused on large multidimensional arrays. In contrast, unstructured datasets consisting of collections of simplices (e.g. triangles or tetrahedra) present special challenges that have received less attention. Data values found at the vertices of the simplices may be dispersed throughout a datafile, producing especially poor disk locality.
In this paper, we address this important problem of poor locality in two major ways. First, we reorganize the unstructured dataset to improve locality in both the dataset space and in the data file on disk using a specialized chunking approach that maintains the spatial neighborhood relationships inherent in the unstructured data. This reorganization produces significant gains in performance by reducing the number of accesses made to the data file. Second, we extend our previous work and describe a prefetching method that takes advantage of prior knowledge of the user's access pattern. Applying this prefetching method to unstructured data produces further performance gains over and above the gains seen from reorganization alone.
 

BIO: Oyindamola Akande is a Doctoral Student in The Department of Computer Science here at the University of Mississippi, working with Dr. Rhodes. He has also earned a Masters Degree at UM, under the supervision of Dr. Lawhead. Before coming to Oxford, Mr. Akande received a BS in Computer Science from Bowen University, Iwo, Nigeria.