Data Interface + Algorithms = Efficient Programs: Separating Logic from Representation to Improve Performance

Finding the right algorithm–data structure combination is easy, but finding the right data structure for a set of algorithms is much less trivial. Moreover, using the same data representation throughout the whole program might be sub-optimal. Depending on several factors, often only known at runtime, some programs benefit from changing the data representation during execution. In this position paper we introduce the idea of Just-In-Time data structures, a combination of a data interface and a set of concrete data representations with different performance characteristics. These Just-In- Time data structures can dynamically swap their internal data representation when the cost of swapping is payed back many times in the remainder of the computation. To make Just-In-Time data structures work, research is needed at three fronts: 1. We need to better understand the synergy between different data representations and algorithms; 2. We need a structured approach to handle the transitions between data representations; 3. We need descriptive programming constructs to express which representation fits a program fragment best. Combined, this research will result in a structured programming approach where separating data interface from data representation, not only improves understandability and maintainability, but also improves performance through automated transitions of data representation.