Mark Murphy

Office: 573 Soda Hall
Computer Science Division
University of California, Berkeley
Berkeley, CA 94720-1776
Email: mjmurphy at cs.berkeley.edu

Publications

Image Feature Extraction for Mobile Processors , Mark Murphy, Hong Wang, Kurt Keutzer, IEEE International Symposium on Workload Characterization 2009 (IISWC 09), Austin, TX, Oct 4-6 2009

Efficient, High-Quality Image Contour Detection Bryan Catanzaro, Bor-Yiing Su, Narayanan Sundaram, Yunsup Lee, Mark Murphy, Kurt Keutzer, IEEE International Conference on Computer Vision, (ICCV09), Kyoto Japan Sept 29 - Oct 2, 2009

A Design Methodology for Domain-Oriented Power-Efficient Supercomputing Marghoob Mohiyuddin, Mark Murphy, Leonid Oliker, John Shalf, John Wawrzynek, Sam Williams, Supercomputing 2009, (SC09), Portland, OR, Nov 14-20, 2009

Stencil Computation Optimization and Autotuning on State-of-the-Art Multicore Architectures, Kaushik Datta, Mark Murphy, Vasily Volkov, Samuel Williams, Jonathan Carter, Leonid Oliker, David Patterson, John Shalf, and Katherine Yelick, Supercomputing 2008 (SC08), Austin, TX, Nov 18-20, 2008

CIGAR: Application Partitioning for a CPU/Coprocessor Architecture John Kelm, Isaac Gelado, Mark Murphy, Steven Lumetta, Nacho Navarro, and Wen-Mei Hwu, International Conference on Parallel Architectures and Compilation Techniques, 2007 (PACT07)

Classes I've Taken

Semester Course Professor Course Title
Fall 2007 EE 244 Kurt Keutzer, Sanjit Seshia Computer Aided Design of Integrated Circuits
CS 294-25 Ras Bodik et al. Current Berkeley Research in Programming Systems
Spring 2008 CS 294-27 Kurt Keutzer, Tim Mattson Architecting Parallel Software
CS 267 Horst Simon Applications of Parallel Programming
Summer-Fall 2008 Extended internship in the Microarchitecture Research Lab at Intel, under Hong Wang
Spring 2009 CS 294-43 Trevor Darrell Visual Object and Activity Recognition
CS 294-45 Kurt Keutzer, Tim Mattson Parallel Programming Patterns
Fall 2009 IEOR 262A Alper Atamturk Mathematical Programming I
CS 281A / Stat 241A Peter Bartlett Statistical Learning Theory