Abstract

Solver-aided tools have automated the verification and synthesis of practical programs in many domains, from high-performance computing to executable biology. These tools work by reducing verification and synthesis tasks to satisfiability queries, which involves compiling programs to logical constraints. Developing an effective symbolic compiler is challenging, however, and until recently, it took years of expert work to create a solver-aided tool for a new domain.

In this talk, I will present Rosette, a programming language for rapid creation of solver-aided tools. To build a new tool, you write an interpreter for the tool's input language, and Rosette lifts this interpreter into a symbolic compiler. This is made possible by Rosette's symbolic virtual machine, which can translate both a language implementation and programs in that language to efficient constraints. Since its first public release in 2014, Rosette has enabled a wide range of programmers, from professional developers to high school students, to create dozens of new verification and synthesis tools. Example applications include verifying radiation therapy software in current clinical use, synthesizing GPU kernels, and verifying and synthesizing just-in-time compilers that are part of the Linux operating system. This talk will provide a brief introduction to Rosette and describe recent applications.

Biography

Emina Torlak is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working on new languages and tools for computer-aided design, verification, and synthesis of software. She received her Bachelors (2003), Masters (2004), and Ph.D. (2009) degrees from MIT, and subsequently worked at IBM Research, LogicBlox, and as a research scientist at U.C. Berkeley. Emina is the creator of the Kodkod solver, which has been used in over 70 academic and industrial tools for software engineering. Her work on the Rosette system integrates solvers programming languages, enabling programmers to create their own solver-aided tools for all kinds of systems, from radiotherapy machines to automated algebra tutors. Emina is a recipient of the NSF CAREER Award (2017), Sloan Research Fellowship (2016), and the AITO Dahl-Nygaard Junior Prize (2016).