CBC Talk on Abstractions for Data-Centric Computing - August 4, 2015
CBC Talk on Abstractions for Data-
Total number of participants: 10
Total number of guests outside of CBC: 2
Number of different nationalities represented: 2
Total number of speakers: 1
Total number of talks: 1
Speaker: Didem Unat, Koç University
Time: Tuesday Aug 4, 11:00
Programming models play a crucial role in providing the necessary tools to express locality and minimize data movement, while also abstracting complexity from programmers. Unfortunately, existing compute-centric programming environments provide few abstractions to manage data movement and locality, and rely on a large shared cache to virtualize data movement. We propose three programming abstractions, tiles, layout and loop traversal that address data locality and increased parallelism on emerging parallel computing systems. The TiDA library implements these abstractions in the data structures through domain decomposition and provides performance portable codes. We demonstrate how TiDA provides high performance with minimal coding effort on current and future NUMA node architectures.
Didem Unat joined Koç University in Istanbul in September 2014 as a full time faculty. Previously she was at the Lawrence Berkeley National Laboratory. She is the recipient of the Luis Alvarez Fellowship in 2012 at the Berkeley Lab. Her research interest lies primarily in the area of high performance computing, parallel programming models, compiler analysis and performance modeling. She is currently working on designing and evaluating programming models for future exascale architectures, as part of Hardware Software co-design project. She received her Ph.D under Prof. Scott B. Baden's research group at University of California-San Diego. In her thesis, she developed the Mint programming model and its source-to-source compiler to facilitate GPGPU programming. She holds a B.S in computer engineering from Boğaziçi University.
Center for Biomedical Computing (CBC) aims to develop and apply novel simulation technologies to reach new understanding of complex physical processes affecting human health. We target selected medical problems where insight from mathematical modeling can contribute to changing clinical practice.