I. Karlin

发表

Fast Multi-parameter Performance Modeling

Torsten Hoefler, Martin Schulz, Felix Wolf, 2016, 2016 IEEE International Conference on Cluster Computing (CLUSTER).

Preparation and optimization of a diverse workload for a large-scale heterogeneous system

Bronis R. de Supinski, John A. Gunnels, Yoonho Park, 2019, SC.

A Holistic View of Memory Utilization on HPC Systems: Current and Future Trends

I. Peng, M. Gokhale, T. Gamblin, 2021, MEMSYS.

LULESH 2.0 Updates and Changes

Ian Karlin, J. R. Neely, Jeff Keasler, 2013 .

Scalable Composition and Analysis Techniques for Massive Scientific Workflows

S. A. Jacobs, Jonathan E. Allen, B. V. Essen, 2022, 2022 IEEE 18th International Conference on e-Science (e-Science).

Performance Characteristics of HYDRA - a Multi-Physics simulation code from Lawrence Livermore National Laboratory

Ian Karlin, Steven H. Langer, Marty M. Marinak, 2014 .

Is Disaggregation possible for HPC Cognitive Simulation?

Brian Van Essen, Ian Karlin, Michael R. Wyatt, 2021, 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC).

Exploring Traditional and Emerging Parallel Programming Models Using a Proxy Application

Martin Schulz, Bradford L. Chamberlain, Felix Wang, 2013, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing.

Characterizing Parallel Scientific Applications on Commodity Clusters: An Empirical Study of a Tapered Fat-Tree

Matthew L. Leininger, Abhinav Bhatele, Ian Karlin, 2016, SC16: International Conference for High Performance Computing, Networking, Storage and Analysis.

LULESH Programming Model and Performance Ports Overview

Ian Karlin, I. Karlin, 2012 .

Highly Scalable Linear Solvers on Thousands of Processors

Allen C. Robinson, Stefan P. Domino, Jonathan Joseph Hu, 2009 .

Program optimizations: The interplay between power, performance, and energy

Ryan E. Grant, Ian Karlin, Matthew G. F. Dosanjh, 2016, Parallel Comput..

Poster: Memory and Parallelism Exploration Using the LULESH Proxy Application

Ian Karlin, Edgar A. León, Esthela Gallardo, 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

Abstract: Memory and Parallelism Exploration Using the LULESH Proxy Application

J. Keasler, I. Karlin, E. León, 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

Efficient exascale discretizations: High-order finite element methods

Jack Dongarra, Elia Merzari, Stanimire Tomov, 2021, Int. J. High Perform. Comput. Appl..

Automating the generation of composed linear algebra kernels

Elizabeth R. Jessup, Jeremy G. Siek, Ian Karlin, 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

An evaluation of the CORAL interconnects

Matthew L. Leininger, Brian E. Smith, Scott Atchley, 2019, SC.

High-Performance Tensor Contractions for GPUs

Jack J. Dongarra, Stanimire Tomov, Azzam Haidar, 2016, ICCS.

The Design, Deployment, and Evaluation of the CORAL Pre-Exascale Systems

Bronis R. de Supinski, Matthew L. Leininger, Al Geist, 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis.

Performance Characteristics of HYDRA - A Multi-physics Simulation Code from LLNL

Ian Karlin, Steve H. Langer, Michael M. Marinak, 2014, VECPAR.

DataRaceBench: A Benchmark Suite for Systematic Evaluation of Data Race Detection Tools

Markus Schordan, Chunhua Liao, Pei-Hung Lin, 2017, SC17: International Conference for High Performance Computing, Networking, Storage and Analysis.

Understanding memory effects in the automated generation of optimized matrix algebra kernels

Elizabeth R. Jessup, Jeremy G. Siek, Ian Karlin, 2010, ICCS.

Tuning the LULESH Mini-app for Current and Future Hardware

Ian Karlin, James R. McGraw, Jeff Keasler, 2013 .

A Semantics-Driven Approach to Improving DataRaceBench's OpenMP Standard Coverage

Markus Schordan, Chunhua Liao, Pei-Hung Lin, 2018, IWOMP.

Modeling the memory and performance impacts of loop fusion

Elizabeth R. Jessup, Ian Karlin, Erik Silkensen, 2012, J. Comput. Sci..

Runtime prediction of fused linear algebra in a compiler framework

Elizabeth R. Jessup, Ian Karlin, E. Jessup, 2011 .

Reimagining Codesign for Advanced Scientific Computing: Unlocking Transformational Opportunities for Future Computing Systems for Science

J. Shalf, A. Hoisie, S. Pakin, 2021 .

Optimizing Explicit Hydrodynamics for Power, Energy, and Performance

Ryan E. Grant, Ian Karlin, Edgar A. León, 2015, 2015 IEEE International Conference on Cluster Computing.

Characterizing the Impact of Program Optimizations on Power and Energy for Explicit Hydrodynamics

Ian Karlin, Edgar A. León, I. Karlin, 2014, 2014 IEEE International Parallel & Distributed Processing Symposium Workshops.

Memory and Parallelism Tuning Exploration using the LULESH Proxy Application

Ian Karlin, Bert Still, Jeff Keasler, 2012 .

Memory Analysis and Tuning of Composed Linear Algebra Kernels

Ian Karlin, I. Karlin, 2008 .

Parallel memory prediction for fused linear algebra kernels

Elizabeth R. Jessup, Jeremy G. Siek, Ian Karlin, 2011, PERV.

Build to order linear algebra kernels

Elizabeth R. Jessup, Jeremy G. Siek, Ian Karlin, 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

Predicting the Performance Impact of Different Fat-Tree Configurations

Misbah Mubarak, Matthew L. Leininger, Todd Gamblin, 2017, SC17: International Conference for High Performance Computing, Networking, Storage and Analysis.