Irregular applications such as big graph analysis, material simulations, molecular dynamics simulations, and finite element analysis have performance problems due to their use of sparse data　structures. Inspector-executor strategies are used to improve sparse computation performance through parallelization and data locality optimizations. An inspector re-schedules and reorders data at runtime, and an executor is a transformed version of the original computation that uses the newly reorganized data structures and schedules. Inspector-executor transformations are commonly written in a domain-specific or even application-specific fashion. Significant progress has been made in incorporating such inspector-executor transformations into existing compiler transformation frameworks, thus enabling their use with compile-time only transformations. However, composing inspector-executor transformations with each other is done on a case-by-case basis so as to reduce inspector overhead.

The Sparse Polyhedral Framework (SPF) is a compiler loop and array transformation model that can generally compose inspector-executor transformations with each other. In this talk, I review the history and current state of the art for inspector-executor strategies, review how the SPF enables the composition of inspector-executor transformations, and present current research to better balance the generality-performance tradeoff that is being done with the Sparse Polyhedral Framework.