tgamblin.github.io

About Me – tgamblin.github.io

Lawrence Livermore National Laboratory. I am a computer scientist in the Center for Applied Scientific Computing. At Lawrence Livermore National Laboratory. My research focuses on scalable tools and algorithms for measuring, analyzing, visualizing, and understanding the massively parallel simulations that run on supercomputers. October 11, 2014. I am looking for postdocs and grad students to work on a project using machine learning to model data-dependent and adaptive programs. Using the Minimal Mistakes.

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About Me – tgamblin.github.io | tgamblin.github.io Reviews

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Lawrence Livermore National Laboratory. I am a computer scientist in the Center for Applied Scientific Computing. At Lawrence Livermore National Laboratory. My research focuses on scalable tools and algorithms for measuring, analyzing, visualizing, and understanding the massively parallel simulations that run on supercomputers. October 11, 2014. I am looking for postdocs and grad students to work on a project using machine learning to model data-dependent and adaptive programs. Using the Minimal Mistakes.

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About Me – tgamblin.github.io | tgamblin.github.io Reviews

https://tgamblin.github.io

Lawrence Livermore National Laboratory. I am a computer scientist in the Center for Applied Scientific Computing. At Lawrence Livermore National Laboratory. My research focuses on scalable tools and algorithms for measuring, analyzing, visualizing, and understanding the massively parallel simulations that run on supercomputers. October 11, 2014. I am looking for postdocs and grad students to work on a project using machine learning to model data-dependent and adaptive programs. Using the Minimal Mistakes.

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tgamblin.github.io

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Job Opportunities – tgamblin.github.io

http://tgamblin.github.io/job-opportunities

Lawrence Livermore National Laboratory. I am looking for postdoctoral researchers and graduate students to work on a new project using machine learning to model the performance of parallel applications, with a focus on data-dependent and adaptive algorithms. This job is part of a much larger set of efforts at LLNL on performance analysis, tools, debugging, modeling, visualization, and other aspects of HPC. If this sounds interesting to you, email me! Lawrence Livermore National Laboratory.

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Software Projects – tgamblin.github.io

http://tgamblin.github.io/code

Photo Credit: LLNL Sequoia. Lawrence Livermore National Laboratory. Tools and libraries I’ve released as open source. A flexible package manager designed to support multiple versions, configurations, platforms, and compilers. Provides a simple expression langauge to simplify the task of configuring HPC software. Cram runs many small MPI jobs within a single, large MPI job by virtualizing its MPI operations. Cram has been used to run 1.5 million concurrent MPI jobs on LLNL’s Sequoia.

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All Posts – tgamblin.github.io

http://tgamblin.github.io/posts

Lawrence Livermore National Laboratory. October 11, 2014. I am looking for postdocs and grad students to work on a project using machine learning to model data-dependent and adaptive programs. 2016 Todd Gamblin. Powered by Jekyll. Using the Minimal Mistakes.

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Curriculum Vitae – tgamblin.github.io

http://tgamblin.github.io/cv

Lawrence Livermore National Laboratory. Todd is a computer scientist in the Center for Applied Scientific Computing. At Lawrence Livermore National Laboratory. His research focuses on scalable tools for measuring, analyzing, and visualizing performance data from massively parallel applications. Todd is also involved with many production projects at LLNL. He works with Livermore Computing. Todd received his Ph.D. in computer science from the University of North Carolina at Chapel Hill.

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Publications – tgamblin.github.io

http://tgamblin.github.io/pubs

Lawrence Livermore National Laboratory. Conference and Journal Papers. Katherine E. Isaacs, Todd Gamblin. Abhinav Bhatele, Martin Schulz, Bernd Hamann, and Peer-Timo Bremer. Ordering Traces Logically to Identify Lateness in Message Passing Programs. IEEE Transactions on Parallel and Distributed Systems (TPDS). Austin, Texas, November 15-20 2015. LLNL-CONF-669890. Katherine E. Isaacs, Abhinav Bhatele, Jonathan Lifflander, David Böhme, Todd Gamblin. Austin, Texas, November 15-20 2015. LLNL-CONF-670046.

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Lawrence Livermore National Laboratory. I am a computer scientist in the Center for Applied Scientific Computing. At Lawrence Livermore National Laboratory. My research focuses on scalable tools and algorithms for measuring, analyzing, visualizing, and understanding the massively parallel simulations that run on supercomputers. October 11, 2014. I am looking for postdocs and grad students to work on a project using machine learning to model data-dependent and adaptive programs. Using the Minimal Mistakes.

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