Dynamic Memory System Overview

• Dynamic Memory Systems are adaptive architectures that modify memory allocation and organization in real-time based on workload, user interaction, or environmental changes.
• They integrate both hardware and software strategies, employing feedback loops and scheduling algorithms to optimize resource use and performance.
• Applications range from high-performance computing and virtualization to robotics and secure enclave execution, demonstrating significant gains in speed and efficiency.

A dynamic memory system is a computational architecture or mechanism in which the allocation, organization, retrieval, and adaptation of memory contents occur at runtime as a function of workload demands, user interaction, or environmental changes. This encompasses both hardware (e.g., DRAM extension, memcomputing devices) and software (e.g., memory management algorithms, external memories for continual learning, dynamic scheduling on GPUs) layers, and is foundational for modern high-performance computing (HPC), adaptive machine reasoning, virtualization, robotics, and specialized domains like secure enclave execution.

1. Core Principles and Architectural Variants

Dynamic memory systems instantiate memory as an adaptive resource, expanding, contracting, or logically reorganizing capacity and content based on observable demand or explicit feedback.

Main Principles

• Runtime Adaptivity: Allocation and reclamation of memory regions (heap blocks, tensors, physical pages, etc.) are scheduled or triggered dynamically, often guided by measured system state, access patterns, or direct user/interpreter stimuli.
• Externalized or Decoupled State: Dynamic systems often decouple "working" memory (e.g., a modifiable external memory module, paged caches, auxiliary data structures) from the fixed core of neural or system parameters, enabling rapid adaptation without resource-intensive retraining or recompilation.
• Feedback or Prediction Loops: Many systems employ control-theoretic feedback (e.g., proportional-integral controllers in DynIMS (Xuan et al., 2016)), statistical prediction (e.g., hotness scores in DMX (Rellermeyer et al., 2019)), or continual user feedback (e.g., TeachMe's appended fact lists (Mishra et al., 2022)) to infer necessary adjustments.
• Multi-Granularity: Dynamic memory may be managed at scales from bits/bytes (e.g., buddy allocators), to application-level objects, to memory pages, blocks, or even task-semantic levels (e.g., session-long memory records in conversational agents (Wang et al., 31 May 2025)).

Representative Architectural Classes

Domain	Dynamic Memory Mechanism	Reference
QA Reasoning Systems	Append-only fact store with BM25 retrieval	(Mishra et al., 2022)
HPC In-Memory Storage	DRAM quota feedback controller for storage/compute sharing	(Xuan et al., 2016)
Memcomputing Hardware	Memcapacitive "cells" with in-place polymorphic logic	(Traversa et al., 2013)
VM Virtualization	Balloon driver, guest-pinned frames for memory overcommit	(Moniruzzaman, 2014)
Online Scene Reconstruction	Dual-memory (transient and persistent) feature banks	(Cai et al., 11 Aug 2025)
GPU Scheduling	Tensor-level swap/recompute scheduling	(Zhang et al., 2021)
LLM Serving	Fine-grained virtual/physical decoupled KV-cache mapping	(Prabhu et al., 7 May 2024)
Secure Enclaves	Enclave+OS-coordinated dynamic (de)allocation of EPC pages	(Dhanraj et al., 22 Apr 2025)

2. Mathematical Models and Control Algorithms

Dynamic memory systems span a spectrum of algorithmic formalizations, including feedback, scheduling, retrieval, and prediction.

Proportional Feedback

In HPC clusters (DynIMS (Xuan et al., 2016)), the fraction of DRAM $u_i$ allocated to in-memory storage is controlled by the error $e_i = r_i - r_o$ (where $r_i$ is measured node utilization, $r_o$ is setpoint). The update is:

$u_{i+1} = u_i - \lambda\frac{e_i}{r_o}u_i$

with bounds on $u_{i+1}$.

Retrieval-Augmented Generation

TeachMe (Mishra et al., 2022) retrieves $r$ highest-BM25-scoring fact sentences from memory $M$ for question $Q$:

$C(Q) = \arg\operatorname{top}_r \limits_{m\in M} s(Q,m),\quad s(Q,m)=\mathrm{BM25}(Q,m)$

These are prepended to the input for downstream proof search.

Dynamic Scheduling

In TENSILE (Zhang et al., 2021), the GPU memory peak $MP_j$ for job $j$ is minimized over per-tensor residency variables $x_{t,\tau}\in\{0,1\}$ by:

$$\underset{\{x_{t,\tau}\}}{\text{minimize}}\ \max_\tau \sum_t x_{t,\tau}M_t \quad \text{subject to}\ \sum_t x_{t,\tau}M_t \leq C,\,\forall\tau$$

where $M_t$ is tensor size, $C$ is physical GPU memory.

Prediction-Driven Eviction

DMX (Rellermeyer et al., 2019) maintains an EWMA estimate $\hat{\Delta}_p$ of per-page inter-access time, deriving a hotness $P_\text{hot}(p)=\exp(-\hat{\Delta}_p/T)$. Pages are evicted to flash if $P_\text{hot}(p)<\theta_\text{evict}$ and the amortized migration cost $C_\text{mig}(p)$ is negative.

3. Methodologies: Design, Scheduling, and Continual Learning

Grammar-Based Evolution and Simulation

Application-specific dynamic memory managers are synthesized through Grammatical Evolution, which designs allocators by searching over grammars describing free-list structures, coalescing/splitting policies, and fit strategies (Álvarez et al., 2023, Risco-Martín et al., 7 Mar 2024, Risco-Martín et al., 22 Jun 2024, Risco-Martín et al., 28 Jun 2024). The genome encodes production-rule choices; each candidate is simulated (not recompiled) using real application traces for cost and utilization metrics.

Continual Memory-Augmented Learning

TeachMe (Mishra et al., 2022) exemplifies memory-based continual adaptation without modifying model parameters:

• User originates correction $f$ for a model error; $M\leftarrow M\cup\{f\}$.
• For subsequent QA, similar context retrieval corrects future errors.
• With 25% user feedback, performance on QA benchmarks improves to within 1% of oracle (full feedback).

Dual-Memory for Online Dynamic Environments

Mem4D (Cai et al., 11 Aug 2025) separates memory for static (Persistent Structure Memory, PSM) and dynamic (Transient Dynamics Memory, TDM) components:

• TDM: maintains high-fidelity, short-history motion context via correlation volumes and self-attention ($\mathcal{M}^D$).
• PSM: stores temporally-coarsened, long-term spatial anchors ($\mathcal{M}^S$).
• The decoder alternates queries to TDM and PSM, eliminating the “Memory Demand Dilemma” between static drift and motion blur.

Dynamic Allocation in Secure Enclaves

SGX2's EDMM (Dhanraj et al., 22 Apr 2025) enables runtime memory growth/shrinkage in enclaves. Because of mutual untrust between OS and enclave, efficient management combines:

• Page pre-allocation at launch.
• Batched EAUG/EACCEPT system calls for contiguous regions.
• Lazy free, caching unused pages to avoid expensive EPC page removal.

4. Performance Analysis, Benchmarks, and Quantitative Results

Empirical evaluations across domains consistently demonstrate large performance, utilization, and latency benefits.

Accuracy Gains

• QA system accuracy increases by up to 15% after minimal user feedback, with 1% gap to full-oracle upper bound using only 25% training example feedback (Mishra et al., 2022).

Resource Utilization and Latency

• DynIMS (Xuan et al., 2016) achieves a 5× speedup for Spark-ML workloads under DRAM pressure by dynamically resizing in-memory storage. The in-memory cache hit ratio increases from ~30% (static) to ~75% (dynamic).
• DMX (Rellermeyer et al., 2019) maintains throughput within 10% and 99th-percentile latency under 100 ms even as container density doubles, compared to default Linux+DRAM/SSD swap where latency collapses.

Energy and Throughput

• Dynamic memory tailoring via Grammatical Evolution yields up to 62% improvement in performance and 30% reduction in memory usage relative to generic allocators (Risco-Martín et al., 7 Mar 2024).
• DCRAM memcomputing (Traversa et al., 2013) achieves energy per operation in the 1–5 fJ range, supporting orders-of-magnitude speedup by performing logic in place.
• AnnaAgent (Wang et al., 31 May 2025) demonstrates statistically significant F1/BERT-score improvements and >30% better accuracy on long-term recall benchmarks for dynamic, persona-coherent LLM-based counseling agents.

5. Applications and Specialized Use-Cases

Virtualization and Overcommitment

Memory ballooning (Moniruzzaman, 2014) allows hypervisors to dynamically reclaim guest RAM by inflating drivers within VMs:

• Under ballooning, throughput remains within 10% of baseline even as limits are pushed to 2 GB/VM. In contrast, host-level swapping incurs up to 34% performance loss.
• Works by cooperative guest/host pinning, guest-driven swap, and dynamic frame reclamation.

Dynamic Scene and World Models

DynaMem (Liu et al., 7 Nov 2024) represents real-time 3D semantic occupancy maps with insertion and removal dynamically driven by sensor-derived observations. A sparse voxel memory is updated per frame, supporting feature-query and LLM-based object location, with ~70% pick-and-drop success on non-stationary targets vs. ~30% for static-memory baselines.

GPU Memory Scheduling

TENSILE (Zhang et al., 2021) employs predictive operator latency modeling and tensor-granular swap/recompute scheduling to minimize peak GPU memory. It eliminates the passive-cold-start and across-iteration scheduling gaps of earlier work, maintaining at least 25–50% savings in peak memory at 10–50% lower overhead.

LLM Serving

vAttention (Prabhu et al., 7 May 2024) decouples virtual and physical memory allowing for fine-grained, on-demand mapping of the KV-cache via virtual memory APIs. This yields up to 1.23× improvement in LLM serving throughput over PagedAttention methods, while preserving index-based tensor access and supporting out-of-the-box attention kernels.

6. Limitations, Challenges, and Future Directions

Several challenges persist in dynamic memory systems:

• Stability vs. Responsiveness: Feedback parameters (gain $\lambda$, control interval $T$) in dynamic controllers must balance fast adaptation with stability (DynIMS (Xuan et al., 2016)), else oscillations and performance degradations occur.
• Fragmentation and Metadata Overhead: Power-of-two buddy systems (e.g., ROOPL++ (Cservenka, 2018)) bound internal fragmentation at $\leq$2× but incur logarithmic worst-case allocation cost. DMX's per-page prediction structures scale at ~16 bytes/page—modest but nonzero at multi-TiB scales.
• Domain Adaptation: Continual learning via external memory (TeachMe, AnnaAgent) depends on retrieval quality; retrieval misses and knowledge coverage limitations are reported to be ~54% and ~24% of failure cases, respectively (Mishra et al., 2022).
• Security/Trusted IO Boundary: SGX2's EDMM imposes high context-switch and system-call overhead if not carefully optimized (Dhanraj et al., 22 Apr 2025); naive EDMM can increase runtime by 58%.
• Scalability: Spatio-semantic map systems (DynaMem) and feature memory banks (Mem4D) can grow to millions of elements. Efficient compression and pruning schemes are open research questions.

A plausible implication is that as systems scale and applications diversify (robotics, LLM serving, privacy-preserving computation), compositional, highly-adaptive dynamic memory mechanisms—optimized via simulation, feedback, or co-evolution—will increasingly be co-designed with hardware and runtime environments.

• "Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement" (Mishra et al., 2022)
• "DynIMS: A Dynamic Memory Controller for In-memory Storage on HPC Systems" (Xuan et al., 2016)
• "Dynamic Computing Random Access Memory" (Traversa et al., 2013)
• "Analysis of Memory Ballooning Technique for Dynamic Memory Management of Virtual Machines (VMs)" (Moniruzzaman, 2014)
• "Mem4D: Decoupling Static and Dynamic Memory for Dynamic Scene Reconstruction" (Cai et al., 11 Aug 2025)
• "TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads system" (Zhang et al., 2021)
• "AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation" (Wang et al., 31 May 2025)
• "Evolutionary Design of the Memory Subsystem" (Álvarez et al., 2023)
• "Simulation of high-performance memory allocators" (Risco-Martín et al., 22 Jun 2024)
• "A methodology to automatically optimize dynamic memory managers applying grammatical evolution" (Risco-Martín et al., 7 Mar 2024)
• "A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems" (Risco-Martín et al., 28 Jun 2024)
• "vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention" (Prabhu et al., 7 May 2024)
• "DynaMem: Online Dynamic Spatio-Semantic Memory for Open World Mobile Manipulation" (Liu et al., 7 Nov 2024)
• "Container Density Improvements with Dynamic Memory Extension using NAND Flash" (Rellermeyer et al., 2019)
• "Design and Implementation of Dynamic Memory Management in a Reversible Object-Oriented Programming Language" (Cservenka, 2018)
• "Adaptive and Efficient Dynamic Memory Management for Hardware Enclaves" (Dhanraj et al., 22 Apr 2025)