Topic: Programming Languages and Models

The advent of accelerated processing units presents new challenges and opportunities for teams responsible for network interconnects and math libraries.

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Compilers translate human-programmable source code into machine-readable code. Building a compiler is especially challenging in the exascale era.

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LLNL’s archives recount the contributions of women who developed code during the Lab's early decades.

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LLNL’s archives provide a glimpse into the career and contributions of a computing pioneer.

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As Computing’s sixth Fernbach Fellow, postdoctoral researcher Chen Wang will work on a new I/O programming paradigm and improve HPC storage consistency models under the mentorship of Kathryn Mohror.

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The Advanced Technology Development and Mitigation program within the Exascale Computing Project shows that the best way to support the mission is through open collaboration and a sustainable software infrastructure.

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LLNL participates in the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 through June 3.

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The Exascale Computing Project (ECP) 2022 Community Birds-of-a-Feather Days will take place May 10–12 via Zoom. The event provides an opportunity for the HPC community to engage with ECP teams to discuss our latest development efforts.

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Each new season brings another hackathon, and Computing’s 2021 summer event took place on August 12–13.

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Our researchers will be well represented at the virtual SIAM Conference on Computational Science and Engineering (CSE21) on March 1–5. SIAM is the Society for Industrial and Applied Mathematics with an international community of more than 14,500 individual members.

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Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.

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Highlights include CASC director Jeff Hittinger's vision for the center as well as recent work with PruneJuice DataRaceBench, Caliper, and SUNDIALS.

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MPI_T is an interface for tools introduced in the 3.0 version of MPI. The interface provides mechanisms for tools to access and set performance and control variables that are exposed by an MPI implementation.

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