Asante Toney

Lindsey Kuper: Abstractions for Expressive, Efficient Parallel and Distributed Computing

Lindsey Kuper, Ron, Laura, Kunle Olokutun, Asante Toney, Neil Conway, Peter Alvaro, Carl, Nikki Vazou

The presentation outlines a unified framework for deterministic parallel computing that utilizes Lattice Variables (LVARs) and Conflict-Free Replicated Data Types to ensure correct distributed executions while enabling high-performance acceleration in Julia and Python through non-invasive domain-specific languages. Complementing these runtime systems, the speaker details a formal verification approach for safety-critical neural networks that employs lazy ReLU splitting within SMT solvers to manage the exponential complexity of non-linear activations. Future research aims to democratize solver development by integrating lattice theories directly into SMT architectures and verifying LVAR constraints through advanced type systems.