Minimal-Copy Memory Management

• Minimal-copy memory management is a set of techniques that reduce redundant data movement during allocation and reclamation, enhancing scalability and throughput.
• Approaches leverage hardware protection, lock-free and wait-free protocols, and compiler-aided methods to enforce zero-copy semantics in concurrent and heterogeneous systems.
• These methods also address fragmentation and GPU memory challenges by using randomized allocation, virtual tensor mapping, and lazy deep copy techniques to optimize resource use.

Minimal-copy memory management denotes a collection of techniques focused on minimizing or entirely eliminating unnecessary object copying during dynamic memory usage and reclamation, especially in contexts with high concurrency or latency sensitivity. The goal is to reduce both runtime overhead and fragmentation while maintaining correctness, scalability, and high throughput, often by leveraging hardware protection, optimized data structures, and fine-grained tracking mechanisms. Below follows a precise, multi-perspective coverage of minimal-copy memory management as evidenced by recent research.

1. Foundational Principles and Zero-Copy Enforcement

Minimal-copy techniques are deeply intertwined with zero-copy operations, wherein direct buffer utilization by hardware is achieved without intermediate copies. In high-performance networking (e.g., Infiniband), zero-copy transmission schemes allow network hardware to read/write application buffers at memory bus speeds, thereby attaining optimal throughput (Power, 2013). However, these approaches introduce risks of accidental, concurrent write activity on buffers being sent, potentially leading to data corruption. Traditional APIs force programmers to track buffer states through manual flags, synchronization, or blocking operations, which can severely degrade non-blocking semantics and introduce complex control flow.

An innovative solution leverages the mprotect system call to enforce hardware-level read-only protection on buffers under active zero-copy send. Upon initiating a send, the buffer region (aligned to page boundaries) is protected; any write triggers a segmentation fault handled by a custom fault handler. The handler waits for completion of the network operation (e.g., via MPI::Request::Test), unprotects the buffer, and proceeds. This approach moves exclusivity enforcement from software to hardware, simplifying the programming model and minimizing extraneous copying, as buffers are always used in-place until the operation is complete.

2. Concurrency, Lock-Freedom, and Wait-Free Reclamation

Minimizing copies in concurrent environments—a major challenge for shared-memory data structures—is driven by lock-free and wait-free memory reclamation schemes that guarantee progress (often under adversarial thread scheduling), suppressing the need for global scans or data movement.

Stamp-it (Pöter et al., 2017) introduces a mechanism where threads associate stamps with critical region entry via atomic fetch-and-add, tracking threads in a lock-free, doubly linked list. Memory reclamation occurs by timestamping retired nodes and determining which are reclaimable using the minimum active stamp, all in amortized constant time. Explicit scanning of thread states is eliminated, reducing the need for defensive copying or coarse-grain object evacuation unless strictly necessary. Compared to hazard pointers, quiescent state, and epoch-based reclamation—which require linear scans or epoch advancement—Stamp-it provides competitive performance across platforms with up to 512 threads, maintaining low memory pressure and outperforming heavy-copying schemes.

Crystalline (Nikolaev et al., 2021) further refines these principles by providing wait-freedom (in Crystalline-W) and balanced workload distribution. Using dynamic batching and a bounded, index-driven API, reclamation tasks are spread across threads; memory is reclaimed asynchronously—any thread may reclaim memory retired by any other—with minimal per-object overhead. Wide CAS instructions and per-pointer tagging prevent ABA problems, while all loops are strictly bounded. Experimental results demonstrate throughput up to 2× faster than hazard pointers or WFE, scaling predictably even with oversubscription. The copy-minimizing design avoids unbounded accumulation of retired objects and per-access reference counting.

3. Compiler-Aided, Automatic Minimal-Copy Reclamation

FreeAccess (Cohen, 2018) proposes a lock-free, general-purpose reclamation scheme applied automatically via an LLVM plug-in, supporting arbitrary lock-free data structures. By dividing execution into alternating write-only and read-only periods, and publishing local roots only on period boundaries, the scheme minimizes pointer copying and synchronization overhead. Reclamation is performed via a lock-free mark-sweep algorithm, gathering local and global roots. The compiler inserts routines for root gathering, checkpointing, and restart, eliminating the need to redesign data structures or intersperse manual retire calls. Benchmarks show a mere 0.1–1.4% overhead in long-running linked list examples and clear performance superiority to reference counting and hazard-pointer alternatives. This suggests automated, minimal-copy management is feasible even in systems with complex pointer state.

4. Fragmentation-Aware Minimal Copying and Compaction

Dealing with fragmentation, especially in unmanaged languages where object relocation is unsafe, often requires copy-based compaction. Mesh (Powers et al., 2019) eliminates fragmentation for C/C++ applications by “meshing” together pages with non-overlapping occupied offsets. The critical innovation is randomized allocation (shuffle vectors), which probabilistically ensures that two spans (pages) seldom have allocations at the same offset, allowing them to be consolidated to a single physical page via remapping—without moving objects or updating their addresses. The SplitMesher algorithm finds meshable pairs; with high probability, it can release nearly half the spans in use, breaking classical Robson bounds. Empirical evaluation notes 16% lower memory usage for Firefox and up to 39% for Redis, with runtime overhead typically under 1%.

5. Precision Marking, Granularity, and In-Place Reclamation

Garbage collection and heap management have classically traded off copying against granularity and precision of reclamation. Nofl (Wingo, 21 Mar 2025) extends the Immix collector by moving from coarse 128-byte lines to fine-grained marking at 16-byte granularity. With one metadata byte per payload fragment, Nofl permits in-place, bump-pointer allocation into “holes” precisely discovered via side-table scan—avoiding unnecessary copying except in rare evacuation phases. Lazy sweeping by the mutator, complemented by multi-purpose metadata bits for write barriers and generational tracking, maintains tight heap usage. Microbenchmarks document that Nofl’s mostly-marking collector outperforms classical copying and mark-sweep collectors for allocators running with tight heaps; it preserves locality and minimizes sweep-induced copying in typical cases.

6. Population-Based Probabilistic Programming and Lazy Copy Platforms

In population-based probabilistic programming (PBPP), such as particle filtering, object copying across generations can scale as O(DNT) in dense mode. “Lazy object copy” (Murray, 2020) introduces copy-on-write semantics at the platform level: deep copies of heap graphs are deferred until a write is forced, permitting sparse representation with memory use scaling as O(DT + DN log DN). This is enabled via smart pointers, per-label hash table memos, and augmented reference counting. Theoretical models using labeled directed multigraphs formalize the lazy copy’s reach, and empirical evidence across RBPF, PCFG, VBD, MOT, and CRBD scenarios shows reduced peak memory and execution times consistent with sparse theory. For imperative, object-oriented, and functional paradigms, the facility delivers copy-on-write, lazy deep copy, and in-place write optimization, respectively.

7. Unified CPU–GPU Systems and Zero-Copy in Heterogeneous HPC

System-level minimal-copy management is increasingly relevant in heterogeneous computing. The Grace Hopper Superchip (Schieffer et al., 10 Jul 2024) supports an integrated CPU–GPU page table, enabling unified virtual address translation. Standard C allocations (via malloc) use first-touch policy for demand-mapped system allocation, with remote NVLink-C2C access at cacheline granularity. This obviates explicit data copies when transferring memory between CPU and GPU. CUDA managed memory, using cudaMallocManaged, relies on on-demand page migration; hardware prefetching and page size tuning (from 4KB to 64KB) are recommended for optimizing migration and initialization. Case studies in Qiskit and HPC workloads demonstrate that unified, system-allocated memory outperforms explicit-copy implementations in many use cases, with minimal porting burden.

8. Minimal-Copy Memory Management in LLM Serving

High-performance serving of LLMs requires elastic memory management to handle dynamic components (activations and key-value caches). eLLM (Xu et al., 18 Jun 2025) introduces a virtual tensor abstraction (“eTensor”), decoupling the virtual address space from physical GPU memory so that mapping is dynamically updated by the GPU’s Virtual Memory Manager. Physical memory chunks are reallocated between KV caches and activation tensors through “inflation” and “deflation” operations—ownership transfer without copying. CPU memory is used as an auxiliary buffer, supporting offload and prefetch with overlapping communication and computation. Lightweight scheduling with SLO-aware policies optimizes for TTFT and TPOT, supporting 3× larger batches and 2.32× higher throughput than prior systems. This design realizes minimal-copy semantics by propagating mapping relationships, rather than moving data, but requires diligent synchronization and virtual-level fragmentation management.

Table: Key Minimal-Copy Techniques by Domain

Domain	Technique/Method	Copy Avoidance Mechanism
High-performance networking (MPI/Infiniband)	mprotect, signal handlers	Hardware-enforced buffer exclusivity
Concurrent data structures (lock/wait-free)	Stamp-it, Crystalline, FreeAccess	Lock-free lists, batching, compiler-aided publish/restart
Memory compaction (C/C++ allocators)	Mesh, shuffle vectors	Page “meshing”, randomized allocation
Garbage collection (managed languages)	Nofl, Immix	Granule-level metadata, lazy sweeping
Probabilistic programming (particle filters)	Lazy object copy, hash map memos	Deferred deep copy, copy-on-write
Heterogeneous memory (CPU–GPU systems)	Integrated page tables, NVLink-C2C	Demand-mapped remote access
LLM serving (GPU memory)	Virtual tensor (eTensor), mapping	Ownership transfer, elastic CPU buffer offload

Conclusion

Minimal-copy memory management leverages hardware mechanisms, fine-grained metadata, randomized allocation, dynamic reclamation, and virtualization of address space to suppress unnecessary data movement. Techniques vary by domain—ranging from page protection in networking, compiler-aided lock-free reclamation in concurrency, randomized meshing in C/C++ allocators, fine-grained marking in garbage collection, lazy deep copy in PBPP, integrated managed memory in CPU–GPU systems, to elastic mapping for LLM serving. The convergent aim across these systems is to reclaim or share memory precisely and efficiently, minimizing programmatic and runtime overhead tied to copying, and guaranteeing progress and correctness in modern, highly concurrent and data-intensive applications.