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The key issue in the design of a parallel reduction architecture is how to balance the number of processors and the number of shared data points. One approach for balancing these two parameters is to use a tree of processors. The aim is to make the distance between the roots of two trees (i.e. the distance between the processors connected to the two root processors) small enough for efficient communication. Typically the distance between the roots of two trees is bounded by the number of processors available. Therefore, to increase the number of processors, it is necessary to increase the number of nodes in the tree.
Many tree structures are known in the prior art. One such tree structure is a reduction tree or reduction fan out tree. One drawback of the reduction tree is that it does not significantly increase the total number of data points while at the same time a large number of data points are stored at every node.
Other approaches to balancing the number of processors and the number of shared data points are based on shared memory. The aim of such an approach is to increase the total number of data points that are communicated by distributing the shared data points among the different processors. An example of shared memory is a data structure known as a lists. A lists is a linked list of items or a linked list of sorted items. In a lists, every element of the list has a next pointer to the next item in the list, and an associated data item. However, a lists structure does not store the data items in an indexed way. Another drawback is that the lists structure does not require the elements to be stored together in the shared memory, which may induce extra latency in the access of the lists elements.
Thus, there exists a need for a system and method for

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