Practical Data-Oriented Design

TL;DR:

• A CPU has several kinds of memory. CPUs cache data in faster and slower memory as the data is used in computation.
• Reading from a slower cache tends to be more expensive than doing several math operations, even multiplication.
• Reducing memory usage can vastly speed up an application
• When possible, 8- or 16-bit integers instead of 32- or 64-bit pointers
• Using a struct of same-sized arrays instead of an array of structs avoids padding used to align each struct.
• store different kinds of objects in different Vecs or arrays and discard the discriminating information from the struct
• store sparse data separate from common data, e.g. in hashmaps