| 2007 |
| 14 | EE | Erez Perelman,
Jeremy Lau,
Harish Patil,
Aamer Jaleel,
Greg Hamerly,
Brad Calder:
Cross Binary Simulation Points.
ISPASS 2007: 179-189 |
| 2006 |
| 13 | EE | Greg Hamerly,
Erez Perelman,
Brad Calder:
Comparing multinomial and k-means clustering for SimPoint.
ISPASS 2006: 131-142 |
| 12 | EE | Yu Feng,
Greg Hamerly:
PG-means: learning the number of clusters in data.
NIPS 2006: 393-400 |
| 11 | EE | Greg Hamerly,
Erez Perelman,
Jeremy Lau,
Brad Calder,
Timothy Sherwood:
Using Machine Learning to Guide Architecture Simulation.
Journal of Machine Learning Research 7: 343-378 (2006) |
| 2005 |
| 10 | EE | Jeremy Lau,
Erez Perelman,
Greg Hamerly,
Timothy Sherwood,
Brad Calder:
Motivation for Variable Length Intervals and Hierarchical Phase Behavior.
ISPASS 2005: 135-146 |
| 9 | EE | Jeremy Lau,
Jack Sampson,
Erez Perelman,
Greg Hamerly,
Brad Calder:
The Strong correlation Between Code Signatures and Performance.
ISPASS 2005: 236-247 |
| 2004 |
| 8 | EE | Greg Hamerly,
Erez Perelman,
Brad Calder:
How to use SimPoint to pick simulation points.
SIGMETRICS Performance Evaluation Review 31(4): 25-30 (2004) |
| 2003 |
| 7 | EE | Erez Perelman,
Greg Hamerly,
Brad Calder:
Picking Statistically Valid and Early Simulation Points.
IEEE PACT 2003: 244- |
| 6 | EE | Greg Hamerly,
Charles Elkan:
Learning the k in k-means.
NIPS 2003 |
| 5 | EE | Erez Perelman,
Greg Hamerly,
Michael Van Biesbrouck,
Timothy Sherwood,
Brad Calder:
Using SimPoint for accurate and efficient simulation.
SIGMETRICS 2003: 318-319 |
| 4 | EE | Timothy Sherwood,
Erez Perelman,
Greg Hamerly,
Suleyman Sair,
Brad Calder:
Discovering and Exploiting Program Phases.
IEEE Micro 23(6): 84-93 (2003) |
| 2002 |
| 3 | EE | Timothy Sherwood,
Erez Perelman,
Greg Hamerly,
Brad Calder:
Automatically characterizing large scale program behavior.
ASPLOS 2002: 45-57 |
| 2 | EE | Greg Hamerly,
Charles Elkan:
Alternatives to the k-means algorithm that find better clusterings.
CIKM 2002: 600-607 |
| 2001 |
| 1 | | Greg Hamerly,
Charles Elkan:
Bayesian approaches to failure prediction for disk drives.
ICML 2001: 202-209 |