Collecting variants in low-level hardware features across multiple GPU and CPU architectures
Topic: Power Management
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LLNL participates in the ISC High Performance Conference (ISC23) on May 21–25.
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Supercomputers broke the exascale barrier, marking a new era in processing power, but the energy consumption of such machines cannot run rampant.
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Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.
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AI/ML | Biology/Biomedicine | Computational Math | Computational Science | Data Science | Data-Driven Decisions | Debugging and Correctness | Deep Learning | Discrete Mathematics | HPC Systems and Software | Hydrodynamics | Mathematical Optimization | Open-Source Software | Performance, Portability, and Productivity | Power Management | Programming Languages and Models | Solvers | Transport
Highlights include the latest work with RAJA, the Exascale Computing Project, algebraic multigrid preconditioners, and OpenMP.
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libMSR provides a convenient interface to access Model Specific Registers and to allow tools to utilize their full functionality.
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These techniques emulate the behavior of anticipated future architectures on current machines to improve performance modeling and evaluation.
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