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[36] Mitigating the Compiler Optimization Phase-Ordering Problem Using Machine Learning.
Sameer Kulkarni and John Cavazos. OOPSLA 2012 [PDF]
[35] Auto-tuning a High-Level Language Targeted to GPU Codes.
Scott Grauer-Gray, Lifan Xu, Robert Searles Sudhee Ayalasomayajula, John Cavazos.
INPAR 2012 [PDF]
[34] Using Graph-Based Program Characterization for Predictive Modeling.
Eunjung Park, John Cavazos, Marco A. Alvarez.
CGO 2012 [PDF] [SLIDES]
[33] A Transactional Memory with Automatic Performance Tuning.
Qingping Wang, Sameer Kulkarni, John Cavazos, and Michael Spear.
HiPEAC 2012 [PDF][](hipeac-2012.pdf)
[32] An Evaluation of Different Modeling Techniques for Iterative Compilation.
Eunjung Park, Sameer Kulkarni, and John Cavazos.
CASES 2011 [PDF] [SLIDES]
[31] GPGPU Accelerated Cardiac Arrhythmia Simulations.
Wei Wang, H. Howie Huang, Matthew Kay, and John Cavazos.
EMBC 2011 [PDF] [SLIDES]
[30] A Machine-Learning Approach to Adaptive Transactional Memory.
Qingping Wang, Sameer Kulkarni, John Cavazos, and Michael Spear.
TRANSACT 2011 [PDF] [SLIDES]
[29] Predictive Modeling for a Polyhedral Optimization Space.
Eunjung Park, Louis-Noel Pouchet, John Cavazos, Albert Cohen, and P. Sadayappan.
CGO 2011 [PDF] [SLIDES]
[28] Faster File Matching Using GPUs.
Deephan Mohan and John Cavazos.
SAAHPC 2010 [PDF] [SLIDES]
[27] Optimizing and Auto-tuning Belief Propagation on the GPU.
Scott Grauer-Gray and John Cavazos.
LCPC 2010 [PDF] [SLIDES]
[26] Split Register Allocation: Linear Complexity Without the Performance Penalty.
Boubacar Diouf, Albert Cohen, Fabrice Rastello, and John Cavazos.
HiPEAC 2010 [PDF]
[25] MPI-aware compiler optimizations for improving communication-computation overlap.
Anthony Danalis, Lori Pollock, Martin Swany, and John Cavazos.
ICS 2009 [PDF] [SLIDES]
[24] RUGRAT: Runtime Test Case Generation using Dynamic Compilers.
Ben Breech, Lori Pollock, and John Cavazos. Acceptance: 29/116 (25%)
ISSRE 2008 [PDF] [SLIDES]
[23] Intelligent Compilers.
John Cavazos.
IWAPT 2008 [](iwapt-2008.ps.gz)[gzip’d PS] [PDF] [SLIDES]
[22] Gravel: A communication library for fast MPI.
Anthony Danalis, Aaron Brown, Lori Pollock, Martin Swany, and John Cavazos.
EuroPVM/MPI 2008 [gzip’d PS] [PDF]
[21] Iterative Optimization in the Polyhedral Model: Part II, Multidimensional Time.
Louis-Noel Pouchet, Cedric Bastoul, Albert Cohen, and John Cavazos. Acceptance: 34/184 (18%)
PLDI 2008 [gzip’d PS] [PDF]
[20] Implementing an Open64-based Tool for Improving the Performance of MPI Programs.
Anthony Danalis, Lori Pollock, Martin Swany, and John Cavazos.
Open64 Workshop 2008 [gzip’d PS] [PDF]
[19] Instruction Cache Energy Saving Through Compiler Way-Placement.
Tim Jones, Sandro Bartolini, Bruno De Bus, John Cavazos, and Michael O’Boyle. Acceptance: 198/839 (24%) DATE 2008 [gzip’d PS] [PDF]
[18] A Note on the Performance Distribution of Affine Schedules.
Louis-Noel Pouchet, Cedric Bastoul, John Cavazos, and Albert Cohen.
SMART 2008 [gzip’d PS] [PDF]
[17] Intelligent Selection of Application-Specific Garbage Collectors.
Jeremy Singer, Gavin Brown, Ian Watson, and John Cavazos. Acceptance: 14/34 (41%)
ISMM 2007 [gzip’d PS] [PDF]
[16] Using Predictive Modeling for Cross-Program Design Space Exploration in Multicore Systems.
Salman Khan, Polychronis Xekalakis, John Cavazos, and Marcelo Cintra. Acceptance: 34/175 (19%)
PACT 2007 [gzip’d PS] [PDF]
[15] Fast Compiler Optimisation Evaluation Using Code-Feature Based Performance Prediction.
Christophe Dubach, John Cavazos, Bjorne Franke, Grigori Fursin, Michael F. P. O’Boyle, and Olivier Temam. Acceptance 28/56 (50%)
Computing Frontiers 2007 [gzip’d PS] [PDF]
[14] Rapidly Selecting Good Compiler Optimizations using Performance Counters.
John Cavazos, Grigori Fursin, Felix Agakov, Edwin Bonilla, Michael F. P. O’Boyle, and Olivier Temam. Acceptance: 26/84 (31%)
CGO 2007 [gzip’d PS] [PDF]
[13] MiDataSets: Creating The Conditions For A More Realistic Evaluation of Iterative Optimization.
Grigori Fursin, John Cavazos, Michael F.P. O’Boyle, and Olivier Temam. Acceptance: (29%)
HiPEAC 2007 [gzip’d PS] [PDF]
[12] Automatic Performance Model Construction for the Fast Software Exploration of New Hardware Designs.
John Cavazos, Christophe Dubach, Felix Agakov, Edwin Bonilla, Michael F. P. O’Boyle, Grigori Fursin, and Olivier Temam. Acceptance: 41/100 (41%)
CASES 2006 [gzip’d PS] [PDF] Finalist Best Paper Award
[11] Method-Specific Dynamic Compilation using Logistic Regression.
John Cavazos and Michael F. P. O’Boyle. Acceptance: 26/157 (16%)
OOPSLA 2006 [gzip’d PS] [PDF]
[10] Predictive Search Distributions.
Edwin Bonilla, Christopher K. I. Williams, Felix Agakov, John Cavazos, John Thomson, and Michael F. P. O’Boyle. Acceptance: 140/548 (25%)
ICML 2006 [gzip’d PS] [PDF]
[9] Hybrid Optimizations: Which Optimization Algorithm to Use?
John Cavazos, J. Eliot B. Moss, and Michael F. P. O’Boyle. Acceptance 17/72 (24%)
Compiler Construction 2006 [gzip’d PS] [PDF] [Slides]
[8] Using Machine Learning to Focus Iterative Optimization.
Felix Agakov, Edwin Bonilla, John Cavazos, Bjoern Franke, Grigori Fursin, Michael F. P. O’Boyle, John Thomson, Marc Toussaint, and Christopher K. I. Williams. Acceptance: 29/80 (36%)
CGO 2006 [gzip’d PS] [PDF] [Slides] Best Presentation Award
[7] Using Statistical Simulation for Studying Compiler-Microarchitecture Interactions.
Lieven Eeckhout and John Cavazos. Acceptance: 8/14 (57%)
INTERACT 2006 [gzip’d PS] [PDF]
[6] Automatic Tuning of Inlining Heuristics for Java JIT Compilation.
John Cavazos and Michael F. P. O’Boyle.
CPC 2006 [gzip’d PS] [PDF]
[5] Automatic Tuning of Inlining Heuristics.
John Cavazos and Michael F. P. O’Boyle. Acceptance: 63/260 (24%)
Supercomputing 2005 [gzip’d PS] [PDF] [Slides]
[4] Inducing Heuristics to Decide Whether to Schedule.
John Cavazos and J. Eliot B. Moss. Acceptance: 25/128 (20%)
PLDI 2004 [gzip’d PS] [PDF] [Slides]
[3] Learning to Schedule Straight-Line Code.
J. Eliot B. Moss, Paul E. Utgoff, John Cavazos, Doina Precup, Darko Stefanovic, Carla E. Brodley,and David T. Scheeff. Acceptance: 150/485 (31%)
NIPS 1997 [gzip’d PS] [PDF]
[2] Automatically Constructing Compiler Optimization Heuristics Using Supervised Learning.
Advisor: J. Eliot B. Moss
Doctoral dissertation September 2004 [gzip’d PS] [PDF] [Slides]
[1] Using Machine Learning to Build Flexible Instruction Schedulers.
Master’s Thesis April 1997 [gzip’d PS] [PDF]
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