Optimizing Sideways Composition: Fast Context-oriented Programming in ContextPyPy

Abstract

The prevalent way of code sharing in many current object systems is static and/or single inheritance; both are limiting in situations that call for multi-dimensional decomposition. Sideways composition provides a technique to reduce their limitations. Context-oriented programming (COP) notably applies sideways composition to achieve better modularity. However, most COP implementations have a substantial performance overhead. This is partly because weaving and execution of layered methods violate assumptions that common language implementations hold about lookup. Meta-tracing just-in-time (JIT) compilers have unique characteristics that can alleviate the performance overhead, as they can treat lookup differently. We show that meta-tracing JIT compilers are good at optimizing sideways composition and give initial, supporting results. Furthermore, we suggest that explicit communication with the JIT compiler in a COP implementation can improve performance further.