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Introduction
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Parallelization is essential for achieving good performance on modern computer architectures.
There are many different forms of parallelization. The optimal approach depends on both the type of software problem and the type of hardware available.
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Introduction to Distributed-Memory Parallelization
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Distributed-memory parallelization is the primary mechanism for acheiving parallelization between nodes.
Distributed-memory parallelization tends to have larger memory requirements than other parallelization techniques.
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MPI Hands-On - mpi4py
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Where possible, use collective communication operations instead of point-to-point communication for improved efficiency and simplicity.
Intelligent design choices can help you reduce the memory footprint required by MPI-parallelized codes
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MPI Hands-On - C++
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Where possible, use collective communication operations instead of point-to-point communication for improved efficiency and simplicity.
Intelligent design choices can help you reduce the memory footprint required by MPI-parallelized codes
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Introduction to Shared-Memory Parallelization
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Shared-memory parallelization enables lower memory requirements than distributed-memory parallelization.
Subtle bugs that are difficult to identify and fix are common when using shared-memory parallelization.
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OpenMP Hands-On
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