github.com/jussi-kalliokoski/par
Package par provides utilities for parallelizing computations. Most implementations are built on parallelization via partitioning, i.e. data is divided into partitions, the partitions are mapped to intermediate representations in parallel, then the intermediate representations are combined (reduced) in parallel (where possible) to produce the desired result. This approach to parallelization provides a few key benefits: As with every performance-oriented tool, measure before applying. Most of the provided functionality is only beneficial if the datasets are large enough or the computations are expensive.
Readme
A go library to perform slice operations (map, filter, reduce, etc) in parallel.
FAQs
Package par provides utilities for parallelizing computations. Most implementations are built on parallelization via partitioning, i.e. data is divided into partitions, the partitions are mapped to intermediate representations in parallel, then the intermediate representations are combined (reduced) in parallel (where possible) to produce the desired result. This approach to parallelization provides a few key benefits: As with every performance-oriented tool, measure before applying. Most of the provided functionality is only beneficial if the datasets are large enough or the computations are expensive.
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