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searchIndex["rayon"] = {"doc":"","items":[[3,"ThreadPool","rayon","",null,null],[5,"initialize","","Initializes the Rayon threadpool. You don't normally need to do\nthis, as it happens automatically, but it is handy for\nbenchmarking purposes since it avoids initialization overhead in\nthe actual operations.",null,{"inputs":[],"output":null}],[5,"dump_stats","","This is a debugging API not really intended for end users. It will\ndump some performance statistics out using `println`.",null,{"inputs":[],"output":null}],[5,"join","","",null,null],[11,"new","","",0,{"inputs":[],"output":{"name":"threadpool"}}],[11,"install","","Executes `op` within the threadpool. Any attempts to `join`\nwhich occur there will then operate within that threadpool.",0,null],[11,"drop","","",0,null],[0,"par_iter","","The `ParallelIterator` module makes it easy to write parallel\nprograms using an iterator-style interface. To get access to all\nthe methods you want, the easiest is to write `use\nrayon::par_iter::*;` at the top of your module, which will import\nthe various traits and methods you need.",null,null],[0,"collect","rayon::par_iter","",null,null],[5,"collect_into","rayon::par_iter::collect","",null,{"inputs":[{"name":"par_iter"},{"name":"vec"}],"output":null}],[0,"enumerate","rayon::par_iter","",null,null],[3,"Enumerate","rayon::par_iter::enumerate","",null,null],[3,"EnumerateState","","",null,null],[3,"EnumerateShared","","",null,null],[11,"new","","",1,{"inputs":[{"name":"m"}],"output":{"name":"enumerate"}}],[11,"state","","",1,null],[11,"len","","",2,null],[11,"split_at","","",2,null],[11,"next","","",2,null],[0,"len","rayon::par_iter","",null,null],[3,"ParallelLen","rayon::par_iter::len","",null,null],[12,"maximal_len","","Maximal number of elements that we will write",3,null],[12,"cost","","An estimate of the "cost" of this operation. This is a kind of\nabstract concept you can use to influence how fine-grained the\nthreads are.",3,null],[12,"sparse","","If true, all elements will be written. If false, some may not.\nFor example, `sparse` will be false if there is a filter.\nWhen doing a collect, sparse iterators require a compression\nstep.",3,null],[17,"THRESHOLD","","",null,null],[11,"clone","","",3,null],[11,"left_cost","","",3,null],[11,"right_cost","","",3,null],[0,"for_each","rayon::par_iter","",null,null],[5,"for_each","rayon::par_iter::for_each","",null,{"inputs":[{"name":"par_iter"},{"name":"op"}],"output":null}],[0,"reduce","rayon::par_iter","",null,null],[3,"SumOp","rayon::par_iter::reduce","",null,null],[3,"MulOp","","",null,null],[3,"MinOp","","",null,null],[3,"MaxOp","","",null,null],[3,"ReduceWithOp","","",null,null],[5,"reduce","","",null,{"inputs":[{"name":"par_iter"},{"name":"reduce_op"}],"output":{"name":"t"}}],[17,"SUM","","",null,null],[17,"MUL","","",null,null],[17,"MIN","","",null,null],[17,"MAX","","",null,null],[8,"ReduceOp","","Specifies a "reduce operator". This is the combination of a start\nvalue and a reduce function. The reduce function takes two items\nand computes a reduced version. The start value `S` is a kind of\n"zero" or "identity" value that may be intermingled as needed;\nidealy, `reduce(S, X)` for any item `X` yields `X`.",null,null],[10,"start_value","","",4,null],[10,"reduce","","",4,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",5,null],[11,"reduce","","",5,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",6,null],[11,"reduce","","",6,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",7,null],[11,"reduce","","",7,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"start_value","","",8,null],[11,"reduce","","",8,null],[11,"new","","",9,{"inputs":[{"name":"op"}],"output":{"name":"reducewithop"}}],[11,"start_value","","",9,null],[11,"reduce","","",9,null],[0,"slice","rayon::par_iter","",null,null],[3,"SliceIter","rayon::par_iter::slice","",null,null],[11,"state","","",10,null],[11,"len","","",10,null],[11,"split_at","","",10,null],[11,"next","","",10,null],[0,"slice_mut","rayon::par_iter","",null,null],[3,"SliceIterMut","rayon::par_iter::slice_mut","",null,null],[11,"state","","",11,null],[11,"len","","",11,null],[11,"split_at","","",11,null],[11,"next","","",11,null],[0,"state","rayon::par_iter","",null,null],[8,"ParallelIteratorState","rayon::par_iter::state","The trait for types representing the internal *state* during\nparallelization. This basically represents a group of tasks\nto be done.",null,null],[16,"Item","","",12,null],[16,"Shared","","",12,null],[10,"len","","Returns an estimate of how much work is to be done.",12,null],[10,"split_at","","Split this state into two other states, ideally of roughly\nequal size.",12,null],[10,"next","","Extract the next item from this iterator state. Once this\nmethod is called, sequential iteration has begun, and the\nother methods will no longer be called.",12,null],[0,"map","rayon::par_iter","",null,null],[3,"Map","rayon::par_iter::map","",null,null],[3,"MapShared","","",null,null],[3,"MapState","","",null,null],[11,"new","","",13,{"inputs":[{"name":"m"},{"name":"map_op"}],"output":{"name":"map"}}],[11,"state","","",13,null],[11,"len","","",14,null],[11,"split_at","","",14,null],[11,"next","","",14,null],[0,"weight","rayon::par_iter","",null,null],[3,"Weight","rayon::par_iter::weight","",null,null],[3,"WeightState","","",null,null],[3,"WeightShared","","",null,null],[11,"new","","",15,{"inputs":[{"name":"m"},{"name":"f64"}],"output":{"name":"weight"}}],[11,"state","","",15,null],[11,"len","","",16,null],[11,"split_at","","",16,null],[11,"next","","",16,null],[0,"zip","rayon::par_iter","",null,null],[3,"ZipIter","rayon::par_iter::zip","",null,null],[3,"ZipShared","","",null,null],[3,"ZipState","","",null,null],[11,"new","","",17,{"inputs":[{"name":"a"},{"name":"b"}],"output":{"name":"zipiter"}}],[11,"state","","",17,null],[11,"len","","",18,null],[11,"split_at","","",18,null],[11,"next","","",18,null],[0,"range","rayon::par_iter","",null,null],[3,"RangeIter","rayon::par_iter::range","",null,null],[11,"state","","",19,null],[11,"len","","",19,null],[11,"split_at","","",19,null],[11,"next","","",19,null],[11,"state","","",19,null],[11,"len","","",19,null],[11,"split_at","","",19,null],[11,"next","","",19,null],[11,"state","","",19,null],[11,"len","","",19,null],[11,"split_at","","",19,null],[11,"next","","",19,null],[11,"state","","",19,null],[11,"len","","",19,null],[11,"split_at","","",19,null],[11,"next","","",19,null],[11,"state","","",19,null],[11,"len","","",19,null],[11,"split_at","","",19,null],[11,"next","","",19,null],[11,"state","","",19,null],[11,"len","","",19,null],[11,"split_at","","",19,null],[11,"next","","",19,null],[11,"state","","",19,null],[11,"len","","",19,null],[11,"split_at","","",19,null],[11,"next","","",19,null],[11,"state","","",19,null],[11,"len","","",19,null],[11,"split_at","","",19,null],[11,"next","","",19,null],[8,"IntoParallelIterator","rayon::par_iter","",null,null],[16,"Iter","","",20,null],[16,"Item","","",20,null],[10,"into_par_iter","","",20,null],[8,"IntoParallelRefIterator","","",null,null],[16,"Iter","","",21,null],[16,"Item","","",21,null],[10,"par_iter","","",21,null],[8,"IntoParallelRefMutIterator","","",null,null],[16,"Iter","","",22,null],[16,"Item","","",22,null],[10,"par_iter_mut","","",22,null],[8,"ParallelIterator","","The `ParallelIterator` interface.",null,null],[16,"Item","","",23,null],[16,"Shared","","",23,null],[16,"State","","",23,null],[10,"state","","",23,null],[11,"weight","","Indicates the relative "weight" of producing each item in this\nparallel iterator. A higher weight will cause finer-grained\nparallel subtasks. 1.0 indicates something very cheap and\nuniform, like copying a value out of an array, or computing `x\n+ 1`. If your tasks are either very expensive, or very\nunpredictable, you are better off with higher values. See also\n`weight_max`, which is a convenient shorthand to force the\nfinest grained parallel execution posible. Tuning this value\nshould not affect correctness but can improve (or hurt)\nperformance.",23,null],[11,"weight_max","","Shorthand for `self.weight(f64::INFINITY)`. This forces the\nsmallest granularity of parallel execution, which makes sense\nwhen your parallel tasks are (potentially) very expensive to\nexecute.",23,null],[11,"enumerate","","Yields an index along with each item.",23,null],[11,"for_each","","Executes `OP` on each item produced by the iterator, in parallel.",23,null],[11,"map","","Applies `map_op` to each item of his iterator, producing a new\niterator with the results.",23,null],[11,"collect_into","","Collects the results of the iterator into the specified\nvector. The vector is always truncated before execution\nbegins. If possible, reusing the vector across calls can lead\nto better performance since it reuses the same backing buffer.",23,null],[11,"reduce_with","","Reduces the items in the iterator into one item using `op`.\nSee also `sum`, `mul`, `min`, etc, which are slightly more\nefficient. Returns `None` if the iterator is empty.",23,null],[11,"sum","","Sums up the items in the iterator.",23,null],[11,"mul","","Multiplies all the items in the iterator.",23,null],[11,"min","","Computes the minimum of all the items in the iterator.",23,null],[11,"max","","Computes the maximum of all the items in the iterator.",23,null],[11,"reduce","","Reduces the items using the given "reduce operator". You may\nprefer `reduce_with` for a simpler interface.",23,null],[11,"zip","","Iterate over tuples `(A, B)`, where the items `A` are from\nthis iterator and `B` are from the iterator given as argument.\nLike the `zip` method on ordinary iterators, if the two\niterators are of unequal length, you only get the items they\nhave in common.",23,null]],"paths":[[3,"ThreadPool"],[3,"Enumerate"],[3,"EnumerateState"],[3,"ParallelLen"],[8,"ReduceOp"],[3,"SumOp"],[3,"MulOp"],[3,"MinOp"],[3,"MaxOp"],[3,"ReduceWithOp"],[3,"SliceIter"],[3,"SliceIterMut"],[8,"ParallelIteratorState"],[3,"Map"],[3,"MapState"],[3,"Weight"],[3,"WeightState"],[3,"ZipIter"],[3,"ZipState"],[3,"RangeIter"],[8,"IntoParallelIterator"],[8,"IntoParallelRefIterator"],[8,"IntoParallelRefMutIterator"],[8,"ParallelIterator"]]};
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