7.2.2 Reducing False Sharing

Careful analysis of those parallel loops that play a major part in the execution of an application can reveal performance scalability problems caused by false sharing. In general, false sharing can be reduced by

• making use of private data as much as possible;

• utilizing the compiler’s optimization features to eliminate memory loads and stores.

In specific cases, the impact of false sharing may be less visible when dealing with larger problem sizes, as there might be less sharing.

Techniques for tackling false sharing are very much dependent on the particular application. In some cases, a change in the way the data is allocated can reduce false sharing. In other cases, changing the mapping of iterations to threads, giving each thread more work per chunk (by changing the chunksize value) can also lead to a reduction in false sharing.