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Visible to Intel only — GUID: GUID-7FB269B9-EE6B-4CE1-BA14-83600B60CDE6
Partitioner Summary
The parallel loop templates parallel_for and parallel_reduce take an optional partitioner argument, which specifies a strategy for executing the loop. The following table summarizes partitioners and their effect when used in conjunction with blocked_range.
Partitioner |
Description |
When Used with blocked_range(i,j,g) |
---|---|---|
simple_partitioner |
Chunksize bounded by grain size. |
g/2 ≤ chunksize ≤ g |
auto_partitioner (default) |
Automatic chunk size. |
g/2 ≤ chunksize |
affinity_partitioner |
Automatic chunk size, cache affinity and uniform distribution of iterations. |
g/2 ≤ chunksize |
static_partitioner |
Deterministic chunk size, cache affinity and uniform distribution of iterations without load balancing. |
max(g/3, problem_size/num_of_resources) ≤ chunksize |
An auto_partitioner is used when no partitioner is specified. In general, the auto_partitioner or affinity_partitioner should be used, because these tailor the number of chunks based on available execution resources. affinity_partitioner and static_partitioner may take advantage of Range ability to split in a given ratio (see “Advanced Topic: Other Kinds of Iteration Spaces”) for distributing iterations in nearly equal chunks between computing resources.
simple_partitioner can be useful in the following situations:
The subrange size for operator() must not exceed a limit. That might be advantageous, for example, if your operator() needs a temporary array proportional to the size of the range. With a limited subrange size, you can use an automatic variable for the array instead of having to use dynamic memory allocation.
A large subrange might use cache inefficiently. For example, suppose the processing of a subrange involves repeated sweeps over the same memory locations. Keeping the subrange below a limit might enable the repeatedly referenced memory locations to fit in cache.
You want to tune to a specific machine.