Parallel calculations: Difference between revisions

=={{header|Raku}}==

(formerly Perl 6)

Takes the list of numbers and converts them to a <tt>HyperSeq</tt> that is stored in a variable and evaluated concurrently. <tt>HyperSeq</tt>s overload <tt>map</tt> and <tt>grep</tt> to convert and pick values in worker threads. The runtime will pick the number of OS-level threads and assign worker threads to them while avoiding stalling in any part of the program. A <tt>HyperSeq</tt> is lazy, so the computation of values will happen in chunks as they are requested.

 

As a relative comparison, perform the same factoring task on the same set of 100 numbers as found in the [[Parallel_calculations#SequenceL|SequenceL]] example, using varying numbers of threads. The absolute speed numbers are not very significant, they will vary greatly between systems, this is more intended as a comparison of relative throughput. On a Core i7-4770 @ 3.40GHz with 4 cores and hyper-threading under Linux, there is a distinct pattern where more threads on physical cores give reliable increases in throughput. Adding hyperthreads may (and, in this case, does seem to) give some additional marginal benefit.

 

<lang perl6>my @nums = 64921987050997300559, 70251412046988563035, 71774104902986066597,

83448083465633593921, 84209429893632345702, 87001033462961102237,

Beside <tt>HyperSeq</tt> and its (allowed to be) out-of-order equivalent <tt>RaceSeq</tt>, [[Rakudo]] supports primitive threads, locks and highlevel promises. Using channels and supplies values can be move thread-safely from one thread to another. A react-block can be used as a central hub for message passing.

 

 

In the future hyper operators, junctions and feeds will be candidates for autothreading.