Abstract: A system includes a computing platform including processing hardware and a memory storing software code including a trained machine learning (ML) model. The processing hardware executes the software code to receive entity specific data over a network from a user device, identify mapping parameters of the entity specific data, and map, using the trained ML model and the mapping parameters, the entity specific data to a statistical distribution in a multi-dimensional representation space. The software code further compares, using the trained ML model, the mapped statistical distribution to each of one or more predetermined statistical distributions in the multi-dimensional representation space, predicts, to using the trained ML model and the comparison, a matching probability for each of the one or more predetermined statistical distributions relative to the mapped statistical distribution. generates a similarity set based on the prediction, and outputs the similarity set to the user device over the network.

Abstract: Systems and methods for discovering approximations for compilers to apply through genetic programming and deterministic symbolic regression heuristics are provided. A method for discovering approximations for compilers and runtime optimization can include profiling a program to identify performance critical functions, determining appropriate candidates for approximation and developing application and architecture specific approximations through machine learning techniques, genetic programming, and deterministic heuristics. Such approximations can target any optimization goal, with a primary emphasis on parallelism, or can provide a set of Pareto-optimal tradeoffs.

Abstract: Systems and methods for discovering approximations for compilers to apply through genetic programming and deterministic symbolic regression heuristics are provided. A method for discovering approximations for compilers and runtime optimization can include profiling a program to identify performance critical functions, determining appropriate candidates for approximation and developing application and architecture specific approximations through machine learning techniques, genetic programming, and deterministic heuristics. Such approximations can target any optimization goal, with a primary emphasis on parallelism, or can provide a set of Pareto-optimal tradeoffs.