P2 is a scalable compiler for collection data structures. High-level abstractions insulate P2 users from data structure implementation details. By specifying a target data structure as a composition of components from a reuse library, the P2 compiler replaces abstract operations with their concrete implementations.LEAPS is a production system compiler that produces the fastest sequential executables of OPS5 rule sets. LEAPS is a hand-written, highly-tuned, performance-driven application that relies on complex data structures. Reengineering LEAPS using P2 was an acid test to evaluate P2's scalability, productivity benefits, and generated code performance.In this paper, we present some of our experimental results and experience in this reengineering exercise. We show that P2 scaled to this complex application, substantially increased productivity, and provided unexpected performance gains.

Many software libraries (e.g., the Booch C++ Components, libg++, NIHCL, COOL) provide components (classes) that implement data structures. Each component is written by hand and represents a unique combination of features (e.g. concurrency, data structure, memory allocation algorithms) that distinguishes it from other components.We argue that this way of building data structure component libraries is inherently unscalable. Libraries should not enumerate complex components with numerous features; rather, libraries should take a minimalist approach: they should provide only primitive building blocks and be accompanied by generators that can combine these blocks to yield complex custom data structures.In this paper, we describe a prototype data structure generator and the building blocks that populate its library. We also present preliminary experimental results which suggest that this approach does not compromise programmer productivity nor the run-time performance of generated data structures.

We present a model of the data structure domain that is expressed in terms of the GenVoca domain modeling concepts [7]. We show how familiar data structures can be encapsulated as realms of plug-compatible, symmetric, and reusable components, and we show how complex data structures can be formed from their composition. The target application of our research is a precompiler for specifying and generating customized data structures.