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2020 – today
- 2024
[i4]Tsuyoshi Ichimura, Kohei Fujita, Muneo Hori, Lalith Maddegedara, Jack C. Wells, Alan Gray, Ian Karlin, John Linford:
Heterogeneous computing in a strongly-connected CPU-GPU environment: fast multiple time-evolution equation-based modeling accelerated using data-driven approach. CoRR abs/2409.20380 (2024)- 2022
- [c32]Dong H. Ahn, Xiaohua Zhang
, Jeffrey Mast, Stephen Herbein, Francesco Di Natale, Dan Kirshner, Sam Ade Jacobs, Ian Karlin, Daniel J. Milroy, Bronis R. de Supinski, Brian Van Essen, Jonathan E. Allen, Felice C. Lightstone:
Scalable Composition and Analysis Techniques for Massive Scientific Workflows. e-Science 2022: 32-43 - [i3]Brian S. Ryujin, Arturo Vargas, Ian Karlin, Shawn A. Dawson, Kenneth Weiss, Adam Bertsch, Michael Scott McKinley, Michael R. Collette, Simon D. Hammond, Kevin T. Pedretti, Robert N. Rieben:
Understanding Power and Energy Utilization in Large Scale Production Physics Simulation Codes. CoRR abs/2201.01278 (2022) - 2021
- [j6]Sam Ade Jacobs, Tim Moon, Kevin McLoughlin, Derek Jones, David Hysom, Dong H. Ahn, John C. Gyllenhaal, Pythagoras Watson, Felice C. Lightstone, Jonathan E. Allen, Ian Karlin, Brian Van Essen:
Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models. Int. J. High Perform. Comput. Appl. 35(5) (2021) - [j5]Tzanio V. Kolev, Paul F. Fischer, Misun Min, Jack J. Dongarra, Jed Brown, Veselin Dobrev, Tim Warburton, Stanimire Tomov, Mark S. Shephard, Ahmad Abdelfattah, Valeria Barra, Natalie Beams, Jean-Sylvain Camier, Noel Chalmers, Yohann Dudouit, Ali Karakus, Ian Karlin, Stefan Kerkemeier, Yu-Hsiang Lan, David S. Medina, Elia Merzari, Aleksandr Obabko, Will Pazner, Thilina Rathnayake, Cameron W. Smith, Lukas Spies, Kasia Swirydowicz, Jeremy L. Thompson, Ananias Tomboulides, Vladimir Z. Tomov:
Efficient exascale discretizations: High-order finite element methods. Int. J. High Perform. Comput. Appl. 35(6): 527-552 (2021) - [c31]Tapasya Patki, Adam Bertsch, Ian Karlin, Dong H. Ahn, Brian Van Essen, Barry Rountree, Bronis R. de Supinski, Nathan Besaw:
Monitoring Large Scale Supercomputers: A Case Study with the Lassen Supercomputer. CLUSTER 2021: 468-480 - [c30]Edgar A. León, Marc Joos, Nathan Hanford, Adrien Cotte, Tony Delforge, François Diakhaté, Vincent Ducrot, Ian Karlin, Marc Pérache:
On-the-Fly, Robust Translation of MPI Libraries. CLUSTER 2021: 504-515 - [c29]Ivy Peng, Ian Karlin, Maya B. Gokhale, Kathleen Shoga, Matthew P. LeGendre, Todd Gamblin:
A Holistic View of Memory Utilization on HPC Systems: Current and Future Trends. MEMSYS 2021: 14:1-14:11 - [c28]Michael R. Wyatt II, Valen Yamamoto, Zoë Tosi, Ian Karlin, Brian Van Essen:
Is Disaggregation possible for HPC Cognitive Simulation? MLHPC@SC 2021: 94-105 - [i2]Tzanio V. Kolev, Paul F. Fischer, Misun Min, Jack J. Dongarra, Jed Brown, Veselin Dobrev, Tim Warburton, Stanimire Tomov, Mark S. Shephard, Ahmad Abdelfattah, Valeria Barra, Natalie Beams, Jean-Sylvain Camier, Noel Chalmers, Yohann Dudouit, Ali Karakus, Ian Karlin, Stefan Kerkemeier, Yu-Hsiang Lan, David S. Medina, Elia Merzari, Aleksandr Obabko, Will Pazner, Thilina Rathnayake, Cameron W. Smith, Lukas Spies, Kasia Swirydowicz, Jeremy L. Thompson, Ananias Tomboulides, Vladimir Z. Tomov:
Efficient Exascale Discretizations: High-Order Finite Element Methods. CoRR abs/2109.04996 (2021) - [i1]Michael R. Wyatt II, Valen Yamamoto, Zoë Tosi, Ian Karlin, Brian Van Essen:
Is Disaggregation possible for HPC Cognitive Simulation? CoRR abs/2112.05216 (2021) - 2020
- [j4]Johann Dahm, David F. Richards, Aaron Black, Adam Bertsch, Leopold Grinberg, Ian Karlin, Sara Kokkila Schumacher, Edgar A. León, Rob Neely, Ramesh Pankajakshan, Olga Pearce:
Sierra Center of Excellence: Lessons learned. IBM J. Res. Dev. 64(3/4): 2:1-2:14 (2020) - [c27]Nathan Hanford, Ramesh Pankajakshan, Edgar A. León, Ian Karlin:
Challenges of GPU-aware Communication in MPI. ExaMPI@SC 2020: 1-10 - [c26]Edgar A. León, Trent D'Hooge, Nathan Hanford, Ian Karlin, Ramesh Pankajakshan, Jim Foraker, Chris Chambreau, Matthew L. Leininger:
TOSS-2020: a commodity software stack for HPC. SC 2020: 40
2010 – 2019
- 2019
- [c25]Pei-Hung Lin, Chunhua Liao, Markus Schordan, Ian Karlin:
Exploring Regression of Data Race Detection Tools Using DataRaceBench. CORRECTNESS@SC 2019: 11-18 - [c24]Ian Karlin, Yoonho Park, Bronis R. de Supinski, Peng Wang, Bert Still, David Beckingsale, Robert Blake, Tong Chen, Guojing Cong, Carlos H. A. Costa, Johann Dahm, Giacomo Domeniconi, Thomas Epperly, Aaron Fisher, Sara Kokkila Schumacher, Steven H. Langer, Hai Le, Eun Kyung Lee, Naoya Maruyama, Xinyu Que, David F. Richards, Björn Sjögreen, Jonathan Wong, Carol S. Woodward, Ulrike Meier Yang, Xiaohua Zhang, Bob Anderson, David Appelhans, Levi Barnes, Peter D. Barnes Jr., Sorin Bastea, David Böhme, Jamie A. Bramwell, James M. Brase, José R. Brunheroto, Barry Chen, Charway R. Cooper, Tony Degroot, Robert D. Falgout, Todd Gamblin, David J. Gardner, James N. Glosli, John A. Gunnels, Max P. Katz, Tzanio V. Kolev, I-Feng W. Kuo, Matthew P. LeGendre, Ruipeng Li, Pei-Hung Lin, Shelby Lockhart, Kathleen McCandless, Claudia Misale, Jaime H. Moreno, Rob Neely, Jarom Nelson, Rao Nimmakayala, Kathryn M. O'Brien, Kevin O'Brien, Ramesh Pankajakshan, Roger Pearce, Slaven Peles, Phil Regier, Steven C. Rennich, Martin Schulz, Howard Scott, James C. Sexton, Kathleen Shoga, Shiv Sundram, Guillaume Thomas-Collignon, Brian Van Essen, Alexey Voronin, Bob Walkup, Lu Wang, Chris Ward, Hui-Fang Wen, Daniel A. White, Christopher Young, Cyril Zeller, Edward Zywicz:
Preparation and optimization of a diverse workload for a large-scale heterogeneous system. SC 2019: 32:1-32:17 - [c23]Christopher Zimmer, Scott Atchley, Ramesh Pankajakshan, Brian E. Smith, Ian Karlin, Matthew L. Leininger, Adam Bertsch, Brian S. Ryujin, Jason Burmark, André Walker-Loud, Michael A. Clark, Olga Pearce:
An evaluation of the CORAL interconnects. SC 2019: 39:1-39:18 - [c22]Philip Taffet, Sanil Rao, Edgar A. León, Ian Karlin:
Testing the Limits of Tapered Fat Tree Networks. PMBS@SC 2019: 47-52 - 2018
- [c21]Pei-Hung Lin, Chunhua Liao, Markus Schordan, Ian Karlin:
Runtime and Memory Evaluation of Data Race Detection Tools. ISoLA (2) 2018: 179-196 - [c20]Chunhua Liao, Pei-Hung Lin, Markus Schordan, Ian Karlin:
A Semantics-Driven Approach to Improving DataRaceBench's OpenMP Standard Coverage. IWOMP 2018: 189-202 - [c19]Fangke Ye, Markus Schordan, Chunhua Liao, Pei-Hung Lin, Ian Karlin, Vivek Sarkar:
Using Polyhedral Analysis to Verify OpenMP Applications are Data Race Free. CORRECTNESS@SC 2018: 42-50 - [c18]Sudharshan S. Vazhkudai, Bronis R. de Supinski, Arthur S. Bland, Al Geist, James C. Sexton, Jim Kahle, Christopher Zimmer, Scott Atchley, Sarp Oral, Don E. Maxwell, Verónica G. Vergara Larrea, Adam Bertsch, Robin Goldstone, Wayne Joubert, Chris Chambreau, David Appelhans, Robert Blackmore, Ben Casses, George Chochia, Gene Davison, Matthew A. Ezell, Tom Gooding, Elsa Gonsiorowski, Leopold Grinberg, Bill Hanson, Bill Hartner, Ian Karlin, Matthew L. Leininger, Dustin Leverman, Chris Marroquin, Adam Moody, Martin Ohmacht, Ramesh Pankajakshan, Fernando Pizzano, James H. Rogers, Bryan S. Rosenburg, Drew Schmidt, Mallikarjun Shankar, Feiyi Wang, Py Watson, Bob Walkup, Lance D. Weems, Junqi Yin:
The design, deployment, and evaluation of the CORAL pre-exascale systems. SC 2018: 52:1-52:12 - 2017
- [c17]Chunhua Liao, Pei-Hung Lin, Joshua Asplund, Markus Schordan, Ian Karlin:
DataRaceBench: a benchmark suite for systematic evaluation of data race detection tools. SC 2017: 11 - [c16]Nikhil Jain, Abhinav Bhatele, Louis H. Howell, David Böhme, Ian Karlin, Edgar A. León, Misbah Mubarak, Noah Wolfe, Todd Gamblin, Matthew L. Leininger:
Predicting the performance impact of different fat-tree configurations. SC 2017: 50 - 2016
- [j3]Edgar A. León, Ian Karlin, Ryan E. Grant, Matthew G. F. Dosanjh:
Program optimizations: The interplay between power, performance, and energy. Parallel Comput. 58: 56-75 (2016) - [c15]Alexandru Calotoiu, David Beckingsale, Christopher W. Earl, Torsten Hoefler, Ian Karlin, Martin Schulz, Felix Wolf:
Fast Multi-parameter Performance Modeling. CLUSTER 2016: 172-181 - [c14]Ahmad Abdelfattah, Marc Baboulin, Veselin Dobrev, Jack J. Dongarra, Christopher W. Earl, Joel Falcou, Azzam Haidar, Ian Karlin, Tzanio V. Kolev, Ian Masliah, Stanimire Tomov:
High-Performance Tensor Contractions for GPUs. ICCS 2016: 108-118 - [c13]Edgar A. León, Ian Karlin, Adam Moody:
System Noise Revisited: Enabling Application Scalability and Reproducibility with SMT. IPDPS 2016: 596-607 - [c12]Ian Karlin, Tom Scogland, Arpith C. Jacob, Samuel F. Antão, Gheorghe-Teodor Bercea, Carlo Bertolli, Bronis R. de Supinski, Erik W. Draeger, Alexandre E. Eichenberger, Jim Glosli, Holger Jones, Adam Kunen, David Poliakoff, David F. Richards:
Early Experiences Porting Three Applications to OpenMP 4.5. IWOMP 2016: 281-292 - [c11]Edgar A. León, Ian Karlin, Abhinav Bhatele, Steven H. Langer, Chris Chambreau, Louis H. Howell, Trent D'Hooge, Matthew L. Leininger:
Characterizing parallel scientific applications on commodity clusters: an empirical study of a tapered fat-tree. SC 2016: 909-920 - 2015
- [c10]Edgar A. León, Ian Karlin, Ryan E. Grant:
Optimizing Explicit Hydrodynamics for Power, Energy, and Performance. CLUSTER 2015: 11-21 - [c9]Kamal Sharma, Ian Karlin, Jeff Keasler, James R. McGraw, Vivek Sarkar:
Data Layout Optimization for Portable Performance. Euro-Par 2015: 250-262 - 2014
- [c8]Edgar A. León, Ian Karlin:
Characterizing the Impact of Program Optimizations on Power and Energy for Explicit Hydrodynamics. IPDPS Workshops 2014: 773-781 - [c7]Steven H. Langer, Ian Karlin, Michael M. Marinak:
Performance Characteristics of HYDRA - A Multi-physics Simulation Code from LLNL. VECPAR 2014: 173-181 - 2013
- [c6]Ian Karlin, Abhinav Bhatele, Jeff Keasler, Bradford L. Chamberlain, Jonathan D. Cohen, Zachary DeVito, Riyaz Haque, Dan Laney, Edward Luke, Felix Wang, David F. Richards, Martin Schulz, Charles H. Still:
Exploring Traditional and Emerging Parallel Programming Models Using a Proxy Application. IPDPS 2013: 919-932 - 2012
- [j2]Ian Karlin, Elizabeth R. Jessup, Erik Silkensen:
Modeling the memory and performance impacts of loop fusion. J. Comput. Sci. 3(3): 120-126 (2012) - [c5]Ian Karlin, Jim McGraw, Esthela Gallardo, Jeff Keasler, Edgar A. León, Bert Still:
Abstract: Memory and Parallelism Exploration Using the LULESH Proxy Application. SC Companion 2012: 1427-1428 - [c4]Ian Karlin, Jim McGraw, Esthela Gallardo, Jeff Keasler, Edgar A. León, Bert Still:
Poster: Memory and Parallelism Exploration Using the LULESH Proxy Application. SC Companion 2012: 1429 - 2011
- [j1]Ian Karlin, Elizabeth R. Jessup, Geoffrey Belter, Jeremy G. Siek:
Parallel memory prediction for fused linear algebra kernels. SIGMETRICS Perform. Evaluation Rev. 38(4): 43-49 (2011) - 2010
- [c3]Elizabeth R. Jessup, Ian Karlin, Erik Silkensen, Geoffrey Belter, Jeremy G. Siek:
Understanding memory effects in the automated generation of optimized matrix algebra kernels. ICCS 2010: 1873-1881
2000 – 2009
- 2009
- [c2]Geoffrey Belter, Elizabeth R. Jessup, Ian Karlin, Jeremy G. Siek:
Automating the generation of composed linear algebra kernels. SC 2009 - 2008
- [c1]Jeremy G. Siek, Ian Karlin, Elizabeth R. Jessup:
Build to order linear algebra kernels. IPDPS 2008: 1-8
Coauthor Index
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last updated on 2024-10-22 21:19 CEST by the dblp team
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