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Parallel DFS is an adaptation of the classic depth-first search algorithm. It involves multiple search processes running concurrently, each starting from different vertices or paths and synchronizing to ensure all nodes are visited without duplication.

MapReduce is a programming model for processing large data sets with a distributed algorithm on a cluster. The process involves two steps: 'Map' (to process and transform input data) and 'Reduce' (to aggregate the results).

Cannon's Algorithm is used for parallel matrix multiplication. It works by dividing the matrices into sub-matrices that fit into the memory of each processing node and systematically shifting the sub-matrices across the processors after each multiplication step.

Parallel Prefix Sum, also known as the cumulative sum, is an algorithm used to calculate the prefix sums of a given sequence. Each element at index i in the output array is the sum of all input array elements from 0 to i. This is often performed in a tree-like fashion for parallel efficiency.