Passive VGC: Compile-Time Memory Optimization

• Passive VGC is a compile-time mechanism that classifies static objects into zones based on access patterns for precise cache-line alignment.
• The approach uses predictive memory mapping and heuristic profiling to reduce fragmentation—achieving up to a 6× improvement over naïve layouts.
• Its integration within the dual-layer VGC architecture enhances cache performance and scalability in memory-intensive, parallel applications.

Passive VGC refers to the compile-time component of the Virtual Garbage Collector (VGC) architecture, designed to optimize static object memory allocation by aligning objects to cache boundaries and minimizing fragmentation. Unlike conventional runtime garbage management systems, Passive VGC employs predictive memory mapping and zone classification exclusively during compilation, separating concerns between static and dynamic memory handling. The resulting layouts enable predictable memory access patterns and significant reductions in resource overhead, yielding up to 25% total memory savings and improved cache performance in parallel and memory-intensive applications (M, 29 Dec 2025).

1. Dual-Layer Architecture and Compile-Time Role

Passive VGC is integrated into the dual-layer design of VGC, where Active VGC manages dynamic allocations at runtime using concurrent mark-and-sweep, and Passive VGC handles all statically known allocations (globals, literals, static arrays) during compilation. After front-end semantic analysis generates the AST/IR and identifies static objects, Passive VGC classifies each allocation into one of three zones—Red (R), Green (G), or Blue (B)—using access-frequency and mutability heuristics. The system then emits linker-section directives or zone tables (.data.R, .data.G, .data.B) to ensure the linker places zone objects in contiguous, cache-aligned regions. This approach provides separation between static and dynamic memory management responsibilities, facilitating predictable and scalable deployment across diverse hardware targets.

2. Predictive Memory Mapping and Zone Assignment

The predictive allocation process begins by statically estimating, for each object $o_s$, the expected read-frequency $\hat a(o_s)$, write-frequency $\hat\mu(o_s)$, and size $\hat s(o_s)$. Feature extraction is typically based on profile-guided feedback or heuristic annotation ("hot" vs. "cold" globals). Classification into R/G/B zones repurposes predicates from the Active VGC runtime:

• $E_R(o_s): a(o_s)<a_R \wedge \mu(o_s)<\mu_R$
• $E_G(o_s): a(o_s)\ge a_G \lor \mu(o_s)\ge \mu_G$
• $E_B(o_s):$ otherwise

A cost model $$C_z(o) = \alpha_z\chi(o)+\beta_z\rho(o)+\gamma_z s(o)$$ provides tie-breaks, with complexity measures replaced by compile-time proxies (initializer complexity, pointer fan-out). Static objects in each zone are placed back-to-back, with each start offset at the next cache-line boundary:

$$\mathrm{Offset}_z(o_s) = \mathrm{Base}_z + \left\lceil \frac{\sum_{o' < o_s} \hat s(o')}{L} \right\rceil \times L$$

where $L$ is the cache-line size. Zone-table entries (object, zone, offset, size) and linker directives ensure precise alignment.

3. Fragmentation Minimization, Cache-Line Alignment, and Data Structures

Passive VGC guarantees that each static object begins at a cache-line multiple (e.g., 64 bytes), bounding per-object internal fragmentation to $L-1$ bytes. Empirical measurements yield sub-2% total fragmentation in typical workloads, compared to 8–12% for naïve layouts. Data structures include:

• Zone Table (per zone): base address, capacity, next-free pointer, allocation-map entries
• Allocation Map: compile-time bitmap of cache-line slots, used to detect overlaps and overflow
• Runtime header: allows Active VGC to locate each static object for checkpointing

Such precise alignment and fragmentation control directly increases L1/L2 hit rates and diminishes total memory usage in large-scale, parallel systems.

4. Performance Analysis and Quantitative Outcomes

Passive VGC achieves substantial resource optimization:

• Fragmentation reduction: baseline alignment wastes up to 8–12%; Passive VGC frequently under 2%, a 6× improvement
• Memory usage: Up to 25% total static region savings, e.g., 10.2 MB baseline reduces to 7.8 MB with Passive VGC in benchmarked Python-embedded C-extension workloads
• Cache-miss rate: 12% lower in L1/L2 for table-intensive microbenchmarks

The process consists of (1) static object identification (O(n)), (2) feature estimation and zone assignment (O(n)), (3) intra-zone size sorting (O($n_z\log n_z$)), and (4) offset allocation (O(n)); overall complexity is $O(n\log n)$, robust for high symbol-count binaries.

5. Integration with Toolchain, Scalability, and Trade-Offs

Passive VGC is typically implemented as a compiler module pass (e.g., after IR generation, before target-layout in LLVM), emitting custom section attributes or linker scripts. For workloads with vast static object sets ($>10^{5}$), the sort and bitmap maintenance dominate, but remain scalable at $O(n\log n)$. If a zone region overflows reserved capacity, Passive VGC either expands the zone at link time or spills objects into fallback dynamic allocators.

Static heuristic quality directly impacts cache locality; misclassified objects may reduce benefit. Zero-profile builds use conservative defaults, typically assigning all statics to the Green zone. Passive VGC is most beneficial when the object graph is substantially static (as in C/C++ shared libraries or constant Python-extension structs); in languages with late binding and dynamic typing, benefits diminish unless full-program analysis or ahead-of-time tracing is deployed.

6. Limitations and Applicability

The primary limitation is dependence on reliable profile or heuristic estimates: inaccurate classification can compromise cache performance. Applicability is broad in contexts such as memory-intensive parallel systems, embedded devices, or statically-analyzed extensions, yet limited in vanilla dynamic languages without static traceability. Scalability bottlenecks are encountered only at extremely high static symbol counts.

7. Synthesis and Impact

Passive VGC provides a deterministic, low-fragmentation allocation model for static objects by leveraging zone-based, compile-time memory mapping (M, 29 Dec 2025). The scheme aligns allocations to cache boundaries, minimizes internal fragmentation, and integrates seamlessly with runtime VGC checkpointing. Empirical results show consistent improvements in memory usage and cache miss rates. Passive VGC thus enhances system performance and scalability, offering predictable memory behavior well-suited to parallel and resource-constrained environments where compile-time knowledge of the object graph is substantial.