State management for distributed Python applications

We present a novel state management mechanism that can be used to capture the complete execution state of distributed Python applications. This mechanism can serve as the foundation for a variety of dependability strategies including checkpointing, replication, and migration. Python is increasingly used for rapid prototyping parallel pro grams and, in some cases, used for high-performance application development using libraries such as NumPy. Building on Stackless Python and the River parallel and distributed programming environment, we have developed mechanisms for state capture at the language level. Our approach allows for migration and checkpointing of applications in heterogeneous environments. In addition, we allow for preemptive state capture so that programmers need not introduce explicit snapshot requests. Our mechanism can be extended to support application or domain-specific state capture. To our knowledge, this is the first general checkpointing scheme for Python. We describe our system, the implementation, and give some initial performance figures.