| Lecturer Profile |
Prof. LI Kuan-Ching finished his Ph.D. degree and post-doctoral degree respectively in Polytechnic School, University of Sao Paulo, Brazil and in University of California-Irvine, California, USA. Currently, he is a professor from the college of Computing and Informatics, Providence University, Taiwan, China. His research interest mainly include Cluster Computing, Grid Computing, High Performance Computing, Computer Networks, Parallel simulation and modeling techniques, Performance analysis and evaluation. He is also a fellow of IET and a senior member of IEEE. |
| Lecture Abstract |
The potential of Big Data is widely recognized and organizations are collecting huge amounts of data. Most of them have the same goal: to extract "value" through sophisticated analysis for findings and decisions. Rapid advancements in computer and networking technologies have been critical to the IT revolution, driving the rapid decline in the cost and fast increase in the processing power of digital technologies. Concurrently, in the ever-changing world of software development, it’s extremely important to keep up with current technologies, methodologies and trends. Map Reduce is a programming model introduced by Google for large scale data processing. Several studies have implemented Map Reduce model on Graphic Processing Unit (GPU). However, most of them are based on the single GPU and bounded by GPU memory with inefficient atomic operations. In this presentation, we present a standalone Map Reduce system to utilize multiple GPUs, handle large-scale data processing beyond GPU memory limit, and eliminate serial atomic operations. Experimental results have demonstrated MGMR's effectiveness and promising the handling of large data sets. |
