Safe Memory Reclamation Algorithms

• Safe Memory Reclamation algorithms are techniques designed to safely free memory in concurrent data structures by ensuring retired objects are not prematurely reclaimed.
• Recent paradigms such as neutralization, reactive synchronization, and hardware–software co-design address challenges like per-thread overhead, unbounded garbage, and scalability.
• Empirical results show up to 5× throughput improvements and strict bounds on unreclaimed memory, enhancing both performance and system robustness in high-concurrency environments.

Safe memory reclamation (SMR) algorithms are mechanisms for correctly reclaiming memory in shared-memory, concurrent data structures, with an emphasis on lock-free and optimistic designs in unmanaged languages. Their central goal is to ensure that memory holding removed (retired) objects is not prematurely freed while references may still be held by other threads, thereby preserving memory safety by avoiding use-after-free errors. SMR algorithms confront the unique difficulties of concurrency, such as delayed or crashed threads, the overhead of coordination between threads, scalability bottlenecks, and the challenge of bounding the number of unreclaimed objects. Recent research has led to the development of new paradigms—neutralization, reactive synchronization, and hardware–software co-design—that aim to reconcile competing requirements for performance, robustness, memory efficiency, adaptability, and ease of use (Singh, 2 Sep 2025).

1. Fundamental Challenges in Safe Memory Reclamation

Safe memory reclamation for non-blocking data structures faces several intertwined challenges:

• Per-Thread Overhead: Traditional reservation-based schemes (e.g., hazard pointers) often require each thread to publish reservations and execute memory fences on every pointer access. This leads to non-negligible latency, particularly detrimental in read-intensive workloads.
• Unbounded Garbage Accumulation: Techniques that rely on batching—especially classic epoch-based reclamation (EBR)—can lead to situations where a single stalled or crashed thread indefinitely delays reclamation, causing the memory footprint from unreclaimed objects to grow without bound.
• Ease of Programmer Integration: Some schemes impose intrusive requirements, such as dividing operations into separate read and write phases, managing complex per-object metadata, or requiring custom compiler/runtime support.
• Applicability: Not all data structure patterns are naturally compatible with a given SMR algorithm; for example, "access-aware" data structures that restart traversals from a fixed entry point are better suited for particular approaches.
• Scalability and Balanced Overhead: The challenge is to maintain throughput and modest memory consumption as thread counts scale, and to avoid asymmetric burden where only a subset of threads performs most of the reclamation work.

Addressing these, new algorithms seek both low per-operation overhead and explicit bounds on unreclaimed memory, while minimizing the burden on implementers (Singh, 2 Sep 2025).

2. Paradigms and Algorithmic Approaches

Recent SMR research introduces three principal paradigms:

A. Neutralization Paradigm

• Overview: Threads that need to reclaim memory send POSIX signals to all other threads. When signaled, threads either discard any pointers that might be dangerous (e.g., from traversals in progress) or publish their reservations of accessed nodes.
• Algorithms:
  • NBR (Neutralization-Based Reclamation): Threads in the read phase are forced—using signal-triggered control-flow transfers (e.g., sigsetjmp/siglongjmp)—to restart from safe checkpoints, ensuring that no unreclaimed node is held live by an in-flight reference.
  • NBR+: Introduces "watermarks" for triggering reclamation with fewer signals, marking a low and high level in the local limbo bags. Once the higher limit is reached, threads are forced to publish reservations or restart, reducing the number of required signals while maintaining bounded unreclaimed memory.
• Key Invariant:

$$n \notin \bigcup_k R_k \implies \text{safe to free } n$$

where $R_k$ is the set of nodes currently reserved by thread $k$.

B. Reactive Synchronization: Publish-on-Ping (POP)

• Overview: Recognizing that synchronously publishing all reservations at every access is costly, these techniques defer the publication until reclamation is imminent. During normal execution, each thread tracks its reservations locally; a reclaiming thread "pings" others (via signals) to prompt them to publish reservations, thereby bounding the number of costly memory fence operations.
• Algorithms:
  • HazardPointersPOP, HazardErasPOP: Drop-in replacements for hazard pointer and hazard era schemes with local reservation and on-demand publication.
  • EpochPOP: Hybridizes optimistic epoch-based reclamation with publish-on-ping for fallback in the presence of delays; achieves EBR-level performance in the absence of delayed threads and robust guarantee in their presence.

C. Hardware–Software Co-Design: Conditional Access

• Overview: Proposes exposing new hardware primitives—conditional access instructions—to leverage cache-coherence metadata. These instructions conditionally permit a thread to access memory only if hardware-level state deems it safe, thus removing the need for persistent software reservations or expensive batch scanning.
• Implementation: A prototype in the Graphite simulator demonstrates near-instantaneous reclamation and minimal memory overhead, as hardware directly detects whether memory is safe to reclaim and aborts or allows accesses accordingly.

These paradigms expand the scope of possible trade-offs among overhead, memory robustness, latency, and programmer effort (Singh, 2 Sep 2025).

3. Hardware–Software Integration

Moving beyond traditional, software-only SMR, some recent work integrates closely with hardware facilities:

• Observation: Events such as cache invalidation, TLB shootdowns, or data page remapping implicitly convey that the memory is about to be reused, and their timing often closely tracks unsafe accesses (i.e., potential use-after-free bugs).
• Design: Exposing these events through meta-instructions enables reclamation algorithms to synchronize and reclaim memory without further software intervention.
• Benefit: This approach reduces not only memory-behavior overhead, but also cache-coherence traffic, and often allows nearly immediate memory reuse—yielding a memory footprint comparable to sequential data structures (Singh, 2 Sep 2025).

A summary of the hardware–software integration is shown below:

Paradigm	Mechanism	Memory Footprint
Software reservation	Explicit fencing	Bounded/unbounded
Publish-on-ping	On-demand publish	Bounded
Hardware assisted	Conditional access	Sequential-like (tight)

4. Empirical Performance Metrics

The experimental evaluation of these SMR techniques focuses on several metrics:

• Throughput: Measured in millions of operations per second (MOPS) on data structures like lazy/Harris lists and external BSTs. POP and neutralization-based strategies can deliver 1.2×–5× higher throughput than traditional hazard pointers, with gains particularly pronounced in read-intensive workloads.
• Memory Footprint: POP, NBR, and NBR+ maintain the unreclaimed node count within a fixed multiple of thread count; EBR-family algorithms may accumulate unbounded garbage if threads are stalled.
• Latency and Scalability: Publish-on-ping and hybrid approaches reduce per-access latency—by avoiding unnecessary fences—and show robust scaling across hundreds of threads and NUMA domains.
• Robustness Under Delay: Techniques with explicit on-demand publication or signaling maintain their bounds even under thread delays or failures, whereas classic EBR may accumulate memory linearly with time until the stalled thread resumes.

Benchmarks also highlight the reduction in synchronization hot spots (e.g., fewer global barriers or memory fences), contributing to better performance on modern hardware (Singh, 2 Sep 2025).

5. Applicability to Data Structure Designs

The suitability of SMR algorithms depends on data structure access patterns:

• Highly Compatible: Access-aware data structures—those with strictly separated read traversals (always from a fixed root/head) and well-delineated write phases—integrate easily with neutralization-based and publish-on-ping schemes (e.g., lazy lists, Harris lists, external BSTs where search always restarts from the root).
• Adaptable with Modifications: Data structures with local restarts, helping, or less regular traversals might require restructuring to always restart from a known entry point to use these paradigms safely.
• Less Compatible: If a data structure's control flow makes it impossible to guarantee that all accesses start from a "safe" point or to resume safely after a restart (e.g., some balanced trees with global rotations), traditional reservation-based (hazard pointer) or batch-based (EBR) schemes may still be required.

The publish-on-ping paradigm is well suited to environments supporting POSIX signals. Conditional access requires architectural support for the relevant cache-coherence exposure, making it appropriate for advanced multicore systems (Singh, 2 Sep 2025).

6. Innovations and Future Perspectives

Key technical innovations introduced by the reviewed work include:

• On-Demand Neutralization: Using asynchronous interrupts (signals) to force threads to discard or publish potentially dangerous pointers only during reclamation events, eliminating the need for frequent publishing.
• Publish-on-Ping for On-Demand Coordination: Separates the cost of pointer publishing from the main traversal path, significantly reducing expensive synchronization on non-critical paths.
• Hardware-Supported Conditional Access: Demonstrates the potential for co-designed hardware–software interfaces to enable instantaneous, sequential-style memory reclamation without classical software barriers.
• Hybrid Schemes (e.g., EpochPOP): Achieve nearly the performance of the best EBR variants while attaining robustness guarantees comparable to the strongest pointer-based techniques.

Empirical results suggest that the new techniques can deliver significant performance improvements—up to 5× in favorable scenarios—while maintaining strict bounds on unreclaimed memory even under worst-case scheduling (e.g., delayed or preempted threads) (Singh, 2 Sep 2025). The approaches are most naturally applied to C/C++ code with non-blocking data structures and are particularly well matched to modern CPU and OS ecosystems supporting fast context switches, signals, and (ideally) hardware coherence notifications.

These paradigms signal a convergence between SMR algorithm design and broader systems co-design, emphasizing adaptability and practicality for large-scale, high-concurrency environments. Further work will likely extend these methods to more general data structure classes, programming languages with more complex memory models, and broader hardware-software deployment contexts.