tech.parallel.for — techascent/tech.parallel 2.11

as-iterable clj

(as-iterable item)

doiter clj macro

(doiter varname iterable & body)

Execute body for every item in the iterable. Expecting side effects, returns nil.

dotimes-batched clj macro

(dotimes-batched [idx-var n-elems batch-size] & body)

Uses dotimes under the covers but provides support for a maximum batch size. Use this in production code to limit dotimes so you have safepoint access for really large iterations

dotimes-safepoint clj macro

(dotimes-safepoint [idx-var n-elems] & body)

dotimes that allows safepoint access for parallelized loops that could get quite large.

indexed-map-reduce clj

(indexed-map-reduce num-iters indexed-map-fn)

(indexed-map-reduce num-iters indexed-map-fn reduce-fn)

Given a function that takes exactly 2 arguments, a start-index and a length, call this function exactly N times where N is ForkJoinPool/getCommonPoolParallelism. Indexes will be split as evenly as possible among the invocations. Uses ForkJoinPool/commonPool for parallelism. The entire list of results (outputs of indexed-map-fn) are passed to reduce-fn; reduce-fn is called once. When called with 2 arguments the reduction function is dorun

indexed-pmap clj

(indexed-pmap indexed-pmap-fn num-iters)

(indexed-pmap indexed-pmap-fn num-iters concat-fn)

Legacy function; really just indexed-map-reduce where the default reducer is #(apply concat %).

launch-parallel-for clj

(launch-parallel-for num-iters indexed-map-fn)

(launch-parallel-for num-iters indexed-map-fn reduce-fn)

Legacy name. See indexed-map-reduce

parallel-for clj macro

(parallel-for idx-var num-iters & body)

Like clojure.core.matrix.macros c-for except this expects index that run from 0 -> num-iters. Idx is named idx-var and body will be called for each idx in parallel. Uses forkjoinpool's common pool for parallelism. Assumed to be side effecting; returns nil.

serial-for clj macro

(serial-for idx-var num-iters & body)

Utility to quickly switch between parallel/serial loop execution