3.4.3 Resource Partitioning and Replication
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3.4.3 Resource Partitioning and Replication

The most effective parallel algorithms and systems exploit resource parallelism, so much so that it is usually wise to begin parallelization by partitioning your write-intensive resources and replicating frequently accessed read-mostly resources. The resource in question is most frequently data, which might be partitioned over computer systems, mass-storage devices, NUMA nodes, CPU cores (or dies or hardware threads), pages, cache lines, instances of synchronization primitives, or critical sections of code. For example, partitioning over locking primitives is termed ``data locking'' [BK85].

Resource partitioning is frequently application dependent, for example, numerical applications frequently partition matrices by row, column, or sub-matrix, while commercial applications frequently partition write-intensive data structures and replicate read-mostly data structures. For example, a commercial application might assign the data for a given customer to a given few computer systems out of a large cluster. An application might statically partition data, or dynamically change the partitioning over time.

Resource partitioning is extremely effective, but it can be quite challenging for complex multilinked data structures.