Domain-specific languages (DSLs) are able to provide intuitive high-level abstractions that are easy to work with while attaining better performance than general-purpose languages. Yet, implementing new DSLs is a burdensome task. As a result, new DSLs are usually embedded in general-purpose languages. While low-level languages like C or C++ often provide better performance as a host than high-level languages like Python, high-level languages are becoming more prevalent in many domains due to their ease and flexibility. Here, we present Codon, a domain-extensible compiler and DSL framework for high-performance DSLs with Python's syntax and semantics. Codon builds on previous work on ahead-of-time type checking and compilation of Python programs and leverages a novel intermediate representation to easily incorporate domain-specific optimizations and analyses. We showcase and evaluate several compiler extensions and DSLs for Codon targeting various domains, including bioinformatics, secure multi-party computation, block-based data compression and parallel programming, showing that Codon DSLs can provide benefits of familiar high-level languages and achieve performance typically only seen with low-level languages, thus bridging the gap between performance and usability.

Genomic data leaks are irreversible. Leaked DNA cannot be changed, stays disclosed indefinitely, and affects the owner's family members as well. The recent large-scale genomic data collections [1], [2] render the traditional privacy protection mechanisms, like the Health Insurance Portability and Accountability Act (HIPAA), inadequate for protection against the novel security attacks [3]. On the other hand, data access restrictions hinder important clinical research that requires large datasets to operate [4]. These concerns can be naturally addressed by the employment of privacy-enhancing technologies, such as a secure multiparty computation (MPC) [5]–[10]. Secure MPC enables computation on data without disclosing the data itself by dividing the data and computation between multiple computing parties in a distributed manner to prevent individual computing parties from accessing raw data. MPC systems are being increasingly adopted in fields that operate on sensitive datasets [11]–[13], such as computational genomics and biomedical research [14]–[22].