Advanced System Optimizer has utilities for optimization, speedup, cleanup, memory management, etc. However, very few parallel algorithms achieve optimal speedup. For some oracular problems, quantum walks provide an exponential speedup over any classical algorithm. Pipelining is an important principle for speedup in computer architecture. A key measure for performance is scalability, captured by the speedup of the implementation. However, quantum computers offer polynomial speedup for some problems. The speedup of such optimized routines varies by array element size, architecture, and implementation. Parallel execution results in a speedup of 4 over sequential execution. Amplitude amplification is often combined with quantum walks to achieve the same quadratic speedup. However, even quadratic speedup is considerable when formula 2 is large. CHs rely on shortcuts to achieve this speedup. Another application is a quadratic speedup in the training of perceptron. Suppose one would like to determine the memory-bounded speedup of matrix multiplication. Blum's speedup theorem shows that there exist artificially constructed problems with speedup. For maximum parallel speedup, more physical processors are used.