User profiles for Ian Karlin

Ian Karlin

Lawrence Livermore National Laboratory
Verified email at colorado.edu
Cited by 2327

[PDF][PDF] Lulesh 2.0 updates and changes

I Karlin, J Keasler, JR Neely - 2013 - osti.gov
The Livermore Unstructured Lagrange Explicit Shock Hydrodynamics (LULESH) proxy
application [1] is being developed as part of the NNSA Advanced Scientific Computing (ASC) …

Exploring traditional and emerging parallel programming models using a proxy application

I Karlin, A Bhatele, J Keasler… - 2013 IEEE 27th …, 2013 - ieeexplore.ieee.org
Parallel machines are becoming more complex with increasing core counts and more
heterogeneous architectures. However, the commonly used parallel programming models, C/C++ …

The design, deployment, and evaluation of the CORAL pre-exascale systems

…, B Hanson, B Hartner, I Karlin… - … Conference for High …, 2018 - ieeexplore.ieee.org
CORAL, the Collaboration of Oak Ridge, Argonne and Livermore, is fielding two similar IBM
systems, Summit and Sierra, with NVIDIA GPUs that will replace the existing Titan and …

Lulesh programming model and performance ports overview

I Karlin, A Bhatele, B Chamberlain, J Cohen, Z Devito… - 2012 - osti.gov
… was done by Ian Karlin and Jim McGraw. Jeff Keasler and Ian Karlin created the pure C
version of the code. Ian Karlin is responsible for the transactional memory and critical section …

DataRaceBench: a benchmark suite for systematic evaluation of data race detection tools

…, PH Lin, J Asplund, M Schordan, I Karlin - Proceedings of the …, 2017 - dl.acm.org
Data races in multi-threaded parallel applications are notoriously damaging while extremely
difficult to detect. Many tools have been developed to help programmers find data races. …

High-performance tensor contractions for GPUs

…, J Dongarra, C Earl, J Falcou, A Haidar, I Karlin… - Procedia Computer …, 2016 - Elsevier
We present a computational framework for high-performance tensor contractions on GPUs.
High-performance is difficult to obtain using existing libraries, especially for many …

Efficient exascale discretizations: High-order finite element methods

…, Y Dudouit, A Karakus, I Karlin… - … Journal of High …, 2021 - journals.sagepub.com
Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms
used in many large-scale applications. These architectures favor algorithms that expose …

Fast multi-parameter performance modeling

…, CW Earl, T Hoefler, I Karlin… - 2016 IEEE …, 2016 - ieeexplore.ieee.org
Tuning large applications requires a clever exploration of the design and configuration
space. Especially on supercomputers, this space is so large that its exhaustive traversal via …

Predicting the performance impact of different fat-tree configurations

…, A Bhatele, LH Howell, D Böhme, I Karlin… - Proceedings of the …, 2017 - dl.acm.org
The fat-tree topology is one of the most commonly used network topologies in HPC systems.
Vendors support several options that can be configured when deploying fat-tree networks …

Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models

…, FC Lightstone, JE Allen, I Karlin… - … Journal of High …, 2021 - journals.sagepub.com
We improved the quality and reduced the time to produce machine learned models for use
in small molecule antiviral design. Our globally asynchronous multi-level parallel training …