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Co-authored-by: nilbuild <4921183+nilbuild@users.noreply.github.com>
9 lines
864 B
Markdown
9 lines
864 B
Markdown
# Parallel Computing
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Parallel computing splits a computational task across multiple processor cores at once, rather than running everything sequentially, cutting the total time for tasks that can be broken into independent pieces. R provides tools like the parallel package's `parLapply()` for distributing work across a cluster of R processes. Not every task benefits from this, since the overhead of splitting work and combining results can outweigh the benefit for small or fast tasks.
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Visit the following resources to learn more:
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- [@article@Parallel Computation](https://bookdown.org/rdpeng/rprogdatascience/parallel-computation.html)
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- [@article@Quick Intro to Parallel Computing in R](https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html)
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- [@video@Parallel Computing in R](https://www.youtube.com/watch?v=_5hXQPTW-wU) |