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.

翻译：虚拟垃圾回收器（VGC）提出了一种基于区域的内存管理架构，旨在提升Python运行时环境的执行可预测性与内存行为。该设计探索了一种双层模型，包含负责管理运行时对象生命周期的活动VGC，以及作为编译时优化层用于静态分配规划的被动VGC。VGC并非依赖传统的堆遍历或分代启发式方法，而是引入了内存分区与基于检查点的状态评估机制，以减少分配扰动并约束垃圾回收范围。通过实验评估执行分区机制，以隔离工作负载并局部化内存压力，从而在循环密集型、递归及计算密集型工作负载下实现更具确定性的行为。本文阐述了在分区感知的运行时上下文中，VGC的架构原理、执行模型及实验观察结果。虽然双层设计的完整实现仍在进行中，但结果表明，基于区域的分配与分区执行为提升面向Python系统的可扩展性与内存可预测性提供了可行基础。