Symbolic Resource Bound Inference for Functional Programs

ABSTRACT. We present an approach for inferring symbolic resource bounds for purely functional programs consisting of recursive functions, algebraic data types and nonlinear arithmetic operations. In our approach, the developer specifies the desired shape of the bound as a program expression containing numerical holes which we refer to as templates. For e.g, time ≤ a ∗ size(tree) + b where a, b are unknowns, is a template that specifies a bound on the execution time. We present a scalable algorithm for computing strongest bounds for sequential and parallel execution times by solving for the unknowns in the template. We empirically evaluate our approach on several benchmarks that manipulate complex data-structures such as binomial heap, lefitist heap, red-black tree and AVL tree. Our implementation is able to infer hard, nonlinear symbolic time bounds for our benchmarks that are beyond the capability of the existing approaches.