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#![allow(dead_code)] //! The `ParallelIterator` module makes it easy to write parallel //! programs using an iterator-style interface. To get access to all //! the methods you want, the easiest is to write `use //! rayon::par_iter::*;` at the top of your module, which will import //! the various traits and methods you need. //! //! The submodules of this module mostly just contain implementaton //! details of little interest to an end-user. use std::f64; use std::ops::Fn; use self::collect::collect_into; use self::enumerate::Enumerate; use self::map::Map; use self::reduce::{reduce, ReduceOp, SumOp, MulOp, MinOp, MaxOp, ReduceWithOp, SUM, MUL, MIN, MAX}; use self::state::ParallelIteratorState; use self::weight::Weight; use self::zip::ZipIter; pub mod collect; pub mod enumerate; pub mod len; pub mod for_each; pub mod reduce; pub mod slice; pub mod slice_mut; pub mod state; pub mod map; pub mod weight; pub mod zip; pub mod range; #[cfg(test)] mod test; pub trait IntoParallelIterator { type Iter: ParallelIterator<Item=Self::Item>; type Item: Send; fn into_par_iter(self) -> Self::Iter; } pub trait IntoParallelRefIterator<'r> { type Iter: ParallelIterator<Item=&'r Self::Item>; type Item: Sync + 'r; fn par_iter(&'r self) -> Self::Iter; } pub trait IntoParallelRefMutIterator<'r> { type Iter: ParallelIterator<Item=&'r mut Self::Item>; type Item: Send + 'r; fn par_iter_mut(&'r mut self) -> Self::Iter; } /// The `ParallelIterator` interface. pub trait ParallelIterator { type Item: Send; type Shared: Sync; type State: ParallelIteratorState<Shared=Self::Shared, Item=Self::Item> + Send; fn state(self) -> (Self::Shared, Self::State); /// Indicates the relative "weight" of producing each item in this /// parallel iterator. A higher weight will cause finer-grained /// parallel subtasks. 1.0 indicates something very cheap and /// uniform, like copying a value out of an array, or computing `x /// + 1`. If your tasks are either very expensive, or very /// unpredictable, you are better off with higher values. See also /// `weight_max`, which is a convenient shorthand to force the /// finest grained parallel execution posible. Tuning this value /// should not affect correctness but can improve (or hurt) /// performance. fn weight(self, scale: f64) -> Weight<Self> where Self: Sized { Weight::new(self, scale) } /// Shorthand for `self.weight(f64::INFINITY)`. This forces the /// smallest granularity of parallel execution, which makes sense /// when your parallel tasks are (potentially) very expensive to /// execute. fn weight_max(self) -> Weight<Self> where Self: Sized { self.weight(f64::INFINITY) } /// Yields an index along with each item. fn enumerate(self) -> Enumerate<Self> where Self: Sized { Enumerate::new(self) } /// Executes `OP` on each item produced by the iterator, in parallel. fn for_each<OP>(self, op: OP) where OP: Fn(Self::Item) + Sync, Self: Sized { for_each::for_each(self, &op) } /// Applies `map_op` to each item of his iterator, producing a new /// iterator with the results. fn map<MAP_OP,R>(self, map_op: MAP_OP) -> Map<Self, MAP_OP> where MAP_OP: Fn(Self::Item) -> R, Self: Sized { Map::new(self, map_op) } /// Collects the results of the iterator into the specified /// vector. The vector is always truncated before execution /// begins. If possible, reusing the vector across calls can lead /// to better performance since it reuses the same backing buffer. fn collect_into(self, target: &mut Vec<Self::Item>) where Self: Sized { collect_into(self, target); } /// Reduces the items in the iterator into one item using `op`. /// See also `sum`, `mul`, `min`, etc, which are slightly more /// efficient. Returns `None` if the iterator is empty. /// /// Note: unlike in a sequential iterator, the order in which `op` /// will be applied to reduce the result is not specified. So `op` /// should be commutative and associative or else the results will /// be non-deterministic. fn reduce_with<OP>(self, op: OP) -> Option<Self::Item> where Self: Sized, OP: Fn(Self::Item, Self::Item) -> Self::Item + Sync, { reduce(self.map(Some), &ReduceWithOp::new(op)) } /// Sums up the items in the iterator. /// /// Note that the order in items will be reduced is not specified, /// so if the `+` operator is not truly commutative and /// associative (as is the case for floating point numbers), then /// the results are not fully deterministic. fn sum(self) -> Self::Item where Self: Sized, SumOp: ReduceOp<Self::Item> { reduce(self, SUM) } /// Multiplies all the items in the iterator. /// /// Note that the order in items will be reduced is not specified, /// so if the `*` operator is not truly commutative and /// associative (as is the case for floating point numbers), then /// the results are not fully deterministic. fn mul(self) -> Self::Item where Self: Sized, MulOp: ReduceOp<Self::Item> { reduce(self, MUL) } /// Computes the minimum of all the items in the iterator. /// /// Note that the order in items will be reduced is not specified, /// so if the `Ord` impl is not truly commutative and associative /// (as is the case for floating point numbers), then the results /// are not deterministic. fn min(self) -> Self::Item where Self: Sized, MinOp: ReduceOp<Self::Item> { reduce(self, MIN) } /// Computes the maximum of all the items in the iterator. /// /// Note that the order in items will be reduced is not specified, /// so if the `Ord` impl is not truly commutative and associative /// (as is the case for floating point numbers), then the results /// are not deterministic. fn max(self) -> Self::Item where Self: Sized, MaxOp: ReduceOp<Self::Item> { reduce(self, MAX) } /// Reduces the items using the given "reduce operator". You may /// prefer `reduce_with` for a simpler interface. /// /// Note that the order in items will be reduced is not specified, /// so if the `reduce_op` impl is not truly commutative and /// associative, then the results are not deterministic. fn reduce<REDUCE_OP>(self, reduce_op: &REDUCE_OP) -> Self::Item where Self: Sized, REDUCE_OP: ReduceOp<Self::Item> { reduce(self, reduce_op) } /// Iterate over tuples `(A, B)`, where the items `A` are from /// this iterator and `B` are from the iterator given as argument. /// Like the `zip` method on ordinary iterators, if the two /// iterators are of unequal length, you only get the items they /// have in common. fn zip<ZIP_OP>(self, zip_op: ZIP_OP) -> ZipIter<Self, ZIP_OP::Iter> where Self: Sized, ZIP_OP: IntoParallelIterator { ZipIter::new(self, zip_op.into_par_iter()) } }