Fixing some transformation problems

Defining domain-specific abstractions for generator systems leads to a quandary between choosing abstractions that exhibit powerful programming amplification through the combinatorial opportunities provided by composition, and choosing abstractions that can be easily transformed into high-performance code. Most generators opt for abstraction to improve programming productivity, which usually compromises target program performance. Transformation-based generators widen the quandary through deep factorization of operators and operands to amplify expressive power, but this explodes the search space. My hypothesis is that existing architectures are inadequate to achieve simultaneously high levels of abstraction, high-performance target programs and small solution search spaces. To explore architectural variations to address this quandary, I have implemented a generator in Common LISP designed specifically to address these problems. It is called the Anticipatory Optimization Generator (AOG) because it allows programmers to anticipate optimization opportunities and to prepare an abstract, distributed plan that attempts to achieve them.