DPX Instructions \cite{bloga}

Algorithms built upon problems where optimal solutions to subproblems constitute an optimal solution to the problem itself rely on dynamic programming (DP). Application areas include healthcare (e.g., genomics), robotics (e.g., path finding), quantum computing, and data science.
A simple example comes from the Fibonacci numbers. The n-th Fibonacci number is known to be the sum of the two previous Fibonacci numbers. Finding the n-th Fibonacci number is thus solved by recursively solving sub-problems. Furthermore, subproblems of Fibonacci overlap. Other DP algorithms include Dijkstra's shortest path, Floyd-Warshall all-pairs shortest path and Smith-Waterman for sequence alignment.
DP problems benefit from the tabulation (building a solution bottom-up) and memoization (top-down) strategies. Both strategies store results of sub-problems such that recomputation is avoided. The new DPX instruction set aims to speed up dynamic programming with specialized instructions that presumably exploit the characteristics of the DP problems.