Virtual Garbage Collector (VGC): A Zone-Based Garbage Collection Architecture for Python's Parallel Runtime

TL;DR

The paper introduces Virtual Garbage Collector (VGC), a zone-based architecture for Python that aims to improve memory predictability and scalability through a dual-layer model and execution partitioning.

Contribution

It presents a novel zone-based, dual-layer garbage collection architecture with partition-aware execution for Python, enhancing predictability and scalability.

Findings

Zone-based allocation reduces memory churn.

Partitioning isolates workloads and localizes memory pressure.

Preliminary results show improved predictability in Python runtimes.

Abstract

The Virtual Garbage Collector (VGC) proposes a zone-based memory management architecture aimed at improving execution predictability and memory behavior in Python runtimes. The design explores a dual-layer model consisting of an Active VGC, responsible for managing runtime object lifecycles, and a Passive VGC, intended as a compile-time optimization layer for static allocation planning. Rather than relying on traditional heap traversal or generational heuristics, VGC introduces memory zoning and checkpoint-based state evaluation to reduce allocation churn and constrain garbage collection scope. Execution partitioning is experimentally evaluated to isolate workloads and localize memory pressure, enabling more deterministic behavior under loop-intensive, recursive, and compute-heavy workloads. This work presents the architectural principles, execution model, and experimental observations of VGC within a partition-aware runtime context. While the full realization of the dual-layer design is an ongoing effort, the results indicate that zone-based allocation and partitioned execution provide a viable foundation for improving scalability and memory predictability in Python-oriented systems.