the cache effect resulting from the different memory hierarchies of a modern computer: in parallel computing, not only do the numbers of processors change, but so does the size of accumulated caches from different processors. With the larger accumulated cache size, more or even all of the working set can fit into caches and the memory access time reduces dramatically, which causes the extra speedup in addition to that from the actual computation. An analogous situation occurs when searching large datasets, such as the genomic data searched by BLAST implementations. There the accumulated RAM from each of the nodes in a cluster enables the dataset to move from disk into RAM thereby drastically reducing the time required by e.g. mpiBLAST to search it. Super-linear speedups can also occur when performing backtracking in parallel: an exception in one thread can cause several other threads to backtrack early, before they reach the exception themselves. Super-linear speedups can also occur in parallel implementations of branch-and-bound for optimization: the processing of one node by one processor may affect the work other processors need to do for the other nodes. See also Amdahl's law Gustafson's law Brooks's law Karp–Flatt metric Parallel slowdown Scalability References Analysis of parallel algorithms