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git-svn-id: svn://ultimatepp.org/upp/trunk@10558 f0d560ea-af0d-0410-9eb7-867de7ffcac7
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cxl 2016-12-19 15:00:48 +00:00
parent 515af8ed37
commit 3bcdb56944
3 changed files with 41 additions and 12 deletions
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19
tutorial/CoreTutorial/CoPartition.cpp
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@ -4,6 +4,10 @@ void CoPartitionTutorial()
{
/// .CoPartition
/// There is some overhead associated with CoWork worker threads. That is why e.g.
/// performing a simple operation on the array spawning worker thread for each element is
/// not a good idea performance wise:
Vector<int> data;
for(int i = 0; i < 10000; i++)
data.Add(i);
@ -16,7 +20,12 @@ void CoPartitionTutorial()
co.Finish();
DUMP(sum);
///
/// Above code computes the sum of all elements in the `Vector`, using CoWorker job for
/// each element. While producing the correct reason, it is likely to run much slower than
/// single-threaded version.
/// The solution to the problem is to split the array into small number of larger subranges
/// that are processed in parallel. This is what `CoPartition` template algorithm does:
sum = 0;
CoPartition(data, [&sum](const auto& subrange) {
@ -28,7 +37,8 @@ void CoPartitionTutorial()
});
DUMP(sum);
///
/// Note that CoPartition is still internally used, so `CoWork::FinLock` is available.
/// Instead of working on subranges, it is also possible to use iterators:
sum = 0;
CoPartition(data.begin(), data.end(), [&sum] (auto l, auto h) {
@ -40,7 +50,8 @@ void CoPartitionTutorial()
});
DUMP(sum);
///
/// There is no requirement on the type of iterators, so it is even possible to use just
/// indices:
sum = 0;
CoPartition(0, data.GetCount(), [&sum, &data] (int l, int h) {
@ -53,4 +64,4 @@ void CoPartitionTutorial()
DUMP(sum);
///
}
}