Auxiliary Cache (Aux Cache) in Modern Systems

• Auxiliary cache is a supplementary cache subsystem engineered to boost efficiency, reduce redundant accesses, and complement primary caches in complex architectures.
• It employs mechanisms like MRU buffers, dynamic partitioning, and selective copy-back to optimize energy, performance, and storage utilization.
• Auxiliary caches are applied in multi-socket servers, reinforcement learning, and coded caching networks, yielding improvements in power consumption, throughput, and system scalability.

An auxiliary cache (commonly abbreviated as “aux cache”—Editor’s term) refers to any cache subsystem introduced to complement or extend the capabilities of a primary cache, typically to improve storage efficiency, data retrieval speed, scalability, power consumption, or other performance metrics in computer architectures and distributed storage systems. Rather than simply replicating the functionality of the main cache, auxiliary caches are engineered to exploit additional hardware, system-level, or application-specific structures—such as helper nodes, recently used address buffers, or workload-adaptive partitions—and are widely deployed across processors, multi-socket servers, reinforcement learning settings, and coded caching networks.

1. Primary Roles and Mechanisms of Auxiliary Cache

Auxiliary caches are designed to assist rather than supplant conventional cache hierarchies. Their functions are highly context-dependent and include:

• Reducing redundant or unnecessary cache accesses and tag/way lookups (e.g., by memoizing recent addresses (0710.4703)).
• Providing isolated storage or prefetching mechanisms for collaborative or sharing-intensive workloads (via shared caches, private caches, or both (Peter et al., 2022)).
• Leveraging selective copy-back strategies for clean cache lines to lower-level caches in non-inclusive or exclusive architectures (Wang et al., 2021).
• Supporting dynamic partitioning of the shared last-level cache (LLC) based on real-time or predicted workload phases, thus facilitating multi-tenancy and performance isolation (Chatterjee et al., 2021).
• Acting as a compact and computationally efficient return cache for deep reinforcement learning, storing aggregated returns as opposed to raw transitions (Daley et al., 2021).

In each of these regimes, auxiliary caches operate under tightly constrained area, energy, and performance budgets, and their technical characteristics are determined by the nature of the main cache and the surrounding hardware or software systems.

2. System Architectures and Caching Topologies

Auxiliary caches are embedded within diverse system architectures:

• Helper-based Shared Cache Networks: In coded caching frameworks, auxiliary caches (helper caches) reside at intermediate network nodes, serving multiple users who also hold private caches (Peter et al., 2022). Each file $W_n$ maintained on the server is partitioned into portions for helper caches ($W_n^{(s)}$) and user caches ($W_n^{(p)}$), enabling multicasting opportunities and synergistic coded delivery. The system topology is characterized by a server, $K$ users, and $\Lambda$ helper nodes; the user–helper associations are captured by an association profile $\mathcal{L}$.
• MRU Address Buffers: In processor design, a Memory Address Buffer (MAB) is introduced as an auxiliary structure to store most recently used (MRU) tags and set indices, reducing redundant tag and way accesses in set-associative caches (0710.4703). The MAB is small (2×8 to 2×32 entries) and maintained using a least recently used (LRU) policy.
• Cache Partitioners in Multitenant Servers: The Com-CAS system integrates compiler-guided analysis and ML models to forecast dynamic cache demands at phase granularity, with a backend scheduler leveraging Intel Cache Allocation Technology (CAT) to apportion LLC ways among co-executing applications (Chatterjee et al., 2021).
• Deep Reinforcement Learning: The Virtual Replay Cache provides an auxiliary mapping from state–action to cached return values, substituting the standard replay buffer with a structure capable of storing and rapidly updating cumulative, discounted returns (Daley et al., 2021).
• Copy-back Predictors in Exclusive/Non-Inclusive Hierarchies: A per-line reuse-distance counter guides whether a clean cache line is copied back to an auxiliary (lower-level) cache or discarded—selectively mitigating cache pollution in STT-MRAM-based LLCs (Wang et al., 2021).

3. Key Algorithms, Data Structures, and Mathematical Frameworks

Auxiliary caches typically rely on specialized algorithms and data structures:

• Weighted Rate Optimization in Coded Caching: The delivery rate $R(M_s, M_p)$ in the hybrid shared/private cache architecture is a weighted sum of contributions from both types of caches:

$$R(M_s, M_p) = f \cdot R_s(t_s) + (1-f) \cdot R_p(t_p)$$

where $t_s = \frac{\Lambda M_s}{fN}$ and $t_p = \frac{K M_p}{(1-f)N}$ are the normalized cache splits, and $f$ optimizes the overall rate (Peter et al., 2022).

• Auxiliary MRU Buffers: The MAB contains tags and set-indices for rapid parallel access, incurs minimal area (7.5%) and energy overhead, and is updated using a LRU policy (0710.4703).
• Reuse-Distance-Based Copy-back: The decision to copy back a cache line is made by comparing its 'private' reuse-distance counter (rd) against the average RD for its set; only lines with short rd are candidates for copy-back:
  • Additional bits encode prefetch status and hit frequency, resulting in a total hardware overhead of ~1.3% (Wang et al., 2021).
• Phase-aware Cache Allocation: Application phase timing is predicted via linear regression:

$$T_{(c)} = \mathbf{U}^T \mathbf{C} = c_0 + c_1 u_1 + \cdots + c_n u_n$$

where $u_i$ is the scaled upper bound of the $i$-th loop, and the cache partition fraction is:

$$f_t^{\text{cache}}(K) = \frac{{}^K m_{\text{phase}}}{\sum_{i=1}^N {}^I m_{\text{phase}}}$$

(Chatterjee et al., 2021).

• Virtual Return Caching: The Virtual Replay Cache maintains mappings from state or state–action pairs to computed returns:

$$G_t = r_t + \gamma r_{t+1} + \gamma^2 r_{t+2} + \dots = \sum_{k=0}^\infty \gamma^k r_{t+k}$$

(Daley et al., 2021).

4. Experimental Evidence and Quantitative Performance

Empirical studies confirm the significant impact of auxiliary caches on system-level metrics:

• Energy and Power Reduction: Implementation of MAB yields up to 50% power reduction in D-caches and 40% in I-caches of a Fujitsu VLIW processor, with negligible area overhead (0710.4703).
• Improved Throughput and Hit Rates: Reuse distance-based copy-back achieves up to 12.8% higher IPC on SPEC CPU 2017 with gem5 simulation for STT-MRAM LLCs; average improvement is 2.5% (Wang et al., 2021).
• Optimized Delivery Rates: In coded caching, composite schemes reduce broadcast load compared to traditional caching, with several regimes achieving the theoretical lower bound (Peter et al., 2022).
• Performance Isolation in Multitenancy: Compiler-guided Com-CAS boosts throughput by 15% (and up to 20% over KPart), maintaining SLA constraints with maximal individual latency degradation capped at 15% (Chatterjee et al., 2021).
• Accelerated Learning in RL: Application of the Virtual Replay Cache nearly eliminates DQN(λ)-style cache memory footprints, reduces overall training time, and consistently enhances learning stability across Atari 2600 games (Daley et al., 2021).

5. Design Trade-offs, Limitations, and Comparative Analysis

Auxiliary caches introduce key trade-offs:

• Hardware Overheads vs. Efficiency: The MAB, copy-back predictors, and auxiliary buffers deliver notable energy efficiencies at modest area costs (<7.5%), but require integration and verification in hardware design (0710.4703, Wang et al., 2021).
• Flexibility Across User Associations: In coded caching, knowledge of user–helper associations at placement substantially decreases the achievable rate; skew profiles benefit from continuous optimization of split parameters $f$, $z$, and $v$ (Peter et al., 2022).
• Isolation vs. Sharing: Systems like Com-CAS isolate cache access for high-sensitivity phases but maintain locality via socket-grouping and careful CLOS assignment (Chatterjee et al., 2021).
• Computational vs. Memory Savings: VRC compresses memory footprint at the expense of additional update logic but avoids redundant computation, yielding net gains for RL agents (Daley et al., 2021).
• Adopted Bias for Remote Cache Lines: LLC optimization schemes must carefully balance retention thresholds and activation watermarks to avoid premature eviction, with hardware verification noted as a complexity cost (Durbhakula, 2019).

6. Applications, Extensions, and Future Directions

Auxiliary caches are receptively integrated in processor caches, multi-socket server environments, data center networks, and ML workloads:

• Non-volatile Memory Hierarchies: Selective copy-back is crucial for STT-MRAM and emerging non-volatile cache technologies to mitigate write latency and energy constraints (Wang et al., 2021).
• Wireline/Wireless Content Distribution: Optimized helper cache deployment reduces traffic and response time in crowded networks (Peter et al., 2022).
• Compiler-guided Scheduling: Fine-grained, ML-assisted cache apportionment is scalable to large server clusters and latency-sensitive workloads (Chatterjee et al., 2021).
• Reinforcement Learning: Auxiliary return caches may evolve towards multi-layered, distributed experience management systems as RL domains scale (Daley et al., 2021).

7. Theoretical Bounds and Analytical Formulas

Auxiliary cache schemes are subject to rigorous theoretical analysis:

• Cut-set Bounds: Lower bounds in coded caching with helpers:

$$R^*(M_s, M_p) \geq \max_{u \in \{1, \dots, \min(N, K)\}} \left( u - \frac{u M_p + \lambda_u M_s}{\lfloor N/u \rfloor} \right)$$

(Peter et al., 2022).

• Weighted Delivery Optimization: Partitioning parameters $f$, $z$, $v$ are determined by minimization of broadcast rates, subject to cache capacity constraints.
• Remote Miss Fraction (RMF): Adaptive logic for LLC biasing policies in NUMA systems:

$$\text{RMF} = \frac{N_{\text{remote misses}}}{N_{\text{total misses}}}$$

Biased retention policies activate when RMF exceeds a high watermark (e.g., $0.5$) and deactivate when below a low watermark (e.g., $0.1$) (Durbhakula, 2019).

Auxiliary caches remain an active research area, evolving to address heterogeneous hardware, complex workloads, and stringent service-level agreements. Their continued development is driven by advances in both theory—such as index coding and algorithmic analysis—and empirical observation of system-level improvements.