Splitting memory resources in high performance computing between local nodes and a larger shared remote pool can help better support diverse applications.
Topic: Data Movement and Memory
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LLNL participates in the ISC High Performance Conference (ISC23) on May 21–25.
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An LLNL Distinguished Member of Technical Staff, Gokhale is considered an expert in her field, and continues to enjoy the fast pace of innovation and change in computing.
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LLNL is participating in the 34th annual Supercomputing Conference (SC22), which will be held both virtually and in Dallas on November 13–18, 2022.
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Data Compression | Data Movement and Memory | Data Science | Debugging and Correctness | Emerging Architectures | Events | Exascale | HPC Architectures | HPC Systems and Software | Hybrid/Heterogeneous | Outreach | Performance, Portability, and Productivity | Programming Languages and Models | Quantum Computing | Resource and Workflow Management | Software Build and Installation
Winning the best paper award at PacificVis 2022, a research team has developed a resolution-precision-adaptive representation technique that reduces mesh sizes, thereby reducing the memory and storage footprints of large scientific datasets.
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The MAPP incorporates multiple software packages into one integrated code so that multiphysics simulation codes can perform at scale on present and future supercomputers.
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A Livermore-developed programming approach helps software to run on different platforms without major disruption to the source code.
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The latest issue of LLNL's Science & Technology Review magazine showcases Computing in the cover story alongside a commentary by Bruce Hendrickson.
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Collaborations | Computational Math | Data Movement and Memory | Discrete Mathematics | Emerging Architectures | Exascale | HPC Architectures | HPC Systems and Software | Hybrid/Heterogeneous | Open-Source Software | Performance, Portability, and Productivity | Software Applications | Software Engineering | Software Libraries
Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.
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AI/ML | Biology/Biomedicine | Computational Math | Computational Science | Critical Infrastructure | Cyber Security | Data Movement and Memory | Data Science | Deep Learning | Discrete Mathematics | Emerging Architectures | Events | HPC Systems and Software | Open-Source Software | Performance, Portability, and Productivity | Resource and Workflow Management
Researchers develop innovative data representations and algorithms to provide faster, more efficient ways to preserve information encoded in data.
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Umpire is a resource management library that allows the discovery, provision, and management of memory on next-generation architectures.
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Highlights include debris and shrapnel modeling at NIF, scalable algorithms for complex engineering systems, magnetic fusion simulation, and data placement optimization on GPUs.
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