Search Space Pruning Constraints Visualization

The field of software optimization, among others, is interested in finding an optimal solution in a large search space. These search spaces are often large, complex, non-linear and even non-continuous at times. The size of the search space makes a brute force solution intractable. As a result, one or more search space pruning constraints are often used to reduce the number of candidate configurations that must be evaluated in order to solve the optimization problem. If more than one pruning constraint is employed, it can be challenging to understand how the pruning constraints interact and overlap. This work presents a visualization technique based on a radial, space-filling technique that allows the user to gain a better understanding of how the pruning constraints remove candidates from the search space. The technique is then demonstrated using a search space pruning data set derived from the optimization of a matrix multiplication code for NVIDIA CUDA accelerators.