SAPA-Bench: Adaptive Benchmarking Framework

• SAPA-Bench is a benchmarking framework that quantifies adaptive behaviors in computing systems by leveraging the SAPA paradigm.
• It evaluates system performance through parameterized workloads that trigger dynamic resource allocation, approximation, and fault resiliency, integrating ML and control theory.
• It measures key metrics including execution time, power consumption, accuracy, and convergence time to guide the design of self-optimizing computing platforms.

SAPA-Bench is a proposed benchmarking framework for adaptive computing systems informed by the Self-Aware Polymorphic Architecture (SAPA) paradigm. SAPA-Bench is conceived as a suite of benchmarks and evaluation tools intended to quantify and analyze the complex adaptive behaviors exhibited by systems that combine runtime intelligence, machine learning-based system control, and dynamic resource allocation. It synthesizes concepts from adaptive hardware, control theory, and runtime approximation, and aims to guide the design and evaluation of next-generation computing systems capable of self-optimization for variable application requirements.

1. Architectural Basis in SAPA

SAPA-Bench is grounded in architectural advances introduced by the Self-Aware Polymorphic Architecture (SAPA) model (Kinsy et al., 2018). SAPA extends the canonical three-layer system architecture (processing elements, memory subsystems, on-chip communication network) with an additional “intelligence fabric” layer—sometimes referred to as a nervous system layer—that facilitates dynamic, autonomous resource management. The critical components are:

• Self-Aware Polymorphic Execution Cores (SAPEC): Heterogeneous and reconfigurable, these cores adapt execution strategies in accordance with high-level program goals, supporting rapid hardware-level migration of workloads.
• Approximation-Aware Memory Organization Models (AMOM): Hierarchically organized self-adapting memory structures, equipped with hardware counters that learn usage patterns and autonomously reorganize to minimize latency and optimize placement.
• Resilient Adaptive Intelligent Network-on-Chip (RAIN): A data transport fabric whose routers dynamically manage congestion, reroute data under fault conditions, and optimize for load imbalance.
• Dynamic Approximation Execution Manager (DAEM) / Nervous System Layer: Collection points for runtime signals drawn from processing, memory, and network layers, integrating machine learning and control feedback for continuous resource optimization with respect to energy, latency, and resiliency.

SAPA-Bench systematically exercises these architectural capabilities through workloads that prompt dynamic resource allocation, runtime adaptation, and controlled approximation in computation.

2. Benchmark Suites and Adaptation Scenarios

The SAPA-Bench benchmark suite would be expressly constructed to trigger and evaluate the adaptive mechanisms of SAPA-inspired systems. Workloads are parameterized to represent a spectrum of operational scenarios:

• Dynamic Resource Allocation: Benchmarks analyze degree of runtime parallelism, requiring the system to activate variable numbers of cores, reassign tasks, and alter network topologies based on measured code parallelism and load characteristics.
• Approximate Computation: Workloads operate under varied correctness margins and resource budgets, simulating scenarios where output accuracy can be traded for execution time or energy efficiency. For example, in recognition tasks, SAPA-Bench might reduce confidence levels (e.g., from 98% to 85%) under tight constraints, prompting the system to approximate results.
• Fault Resiliency: Fault-injection cases and simulated hardware degradations test the system’s ability to reconfigure and continue computation with high reliability, leveraging adaptive routing and migration features.

Workloads are selected to maximize exercise of the adaptive stack, including applications from image recognition (with controllable quality levels), iterative solvers, and multi-phase streaming analyses.

3. Adaptive Performance Metrics

SAPA-Bench measures both conventional and adaptation-specific metrics:

Metric	Description	Significance
Execution Time	Wall-clock run time of application	Baseline for performance
Power Consumption	Average/peak power usage per run	Energy efficiency; core for adaptation goals
Accuracy	Output correctness (e.g., recognition confidence)	Quantifies trade-off under approximation
Adaptation Overhead	Cost (latency, power) of switching/adaptation	Measures self-optimization efficiency
Convergence Time	Time for feedback loops to stabilize configuration	Control loop effectiveness

Adaptation efficiency is often distilled in a composite metric, e.g.,

$$\Phi = \frac{\text{Accuracy}}{\text{Power} \times \text{Time}},$$

where higher values indicate superior trade-offs. These metrics are tracked at granularities matching architectural reconfiguration events.

4. ML and Control Theory Integration

SAPA-Bench uniquely evaluates the contributions of machine learning and control theory in system runtime adaptation:

• Machine Learning Algorithms: Analyze runtime counters and event traces to infer optimal resource mappings, predict necessary configuration switches, and assess performance trade-offs. These data-driven modules guide hardware-level reconfiguration, with benchmarks assessing their quality by measuring both the optimality of decisions and convergence speed.
• Control Theory Methods: Real-time feedback controllers (e.g., proportional–integral–derivative schemes) respond to deviations in power envelopes, accuracy targets, or latency by adjusting both resource allocations and approximation degrees. SAPA-Bench targets scenarios where stability and responsiveness of these loops are critical and evaluates against forced perturbations.

Case studies included in SAPA-Bench can illustrate how system workload pattern changes dynamically provoke hardware and controller adaptation events, measuring latency and stability in response.

5. Layered Evaluation Pipeline

The SAPA-Bench framework is organized in a pipeline reflecting the layered architecture of SAPA:

1. Workload Execution: Benchmarks are run with diverse profiles—static, dynamic, and fault-injection.
2. Metric Collection: Adaptive metrics are sampled at runtime—execution time, power, accuracy, adaptation overhead.
3. ML/Control Analysis: System logs are analyzed to evaluate the feedback loop efficiency and system stability under adaptation stressors.
4. Aggregate System Evaluation: Results are synthesized to yield comprehensive performance profiles, exposing strengths and limitations of adaptive behavior.

A representative structure for this pipeline is formalized with:

$$\begin{array}{c} \textbf{SAPA-Bench Framework} \ \downarrow \ \left[\begin{array}{c} \text{Workload Profiles} \ \text{(Dynamic, Static, Fault-Injection)} \end{array}\right] \ \downarrow \ \left[\begin{array}{c} \text{Adaptive Metrics} \ (\text{Execution Time, Power, Accuracy, Adaptation Overhead}) \end{array}\right] \ \downarrow \ \left[\begin{array}{c} \text{ML and Control Analysis} \ \text{(Feedback Loop Efficiency, Stability)} \end{array}\right] \ \downarrow \ \textbf{Overall System Performance Evaluation} \end{array}$$

This layered process enables systematic analyses of adaptation effectiveness.

6. Context, Related Benchmarks, and Implementation

SAPA-Bench is positioned in the landscape of adaptive and context-aware systems benchmarking. Unlike static benchmarks such as SPEC or TPC variants, SAPA-Bench emphasizes dynamic and autonomous architectural features. Comparative analysis can be drawn with BigBench for big data systems (Poggi et al., 2020), which characterizes queries by resource consumption but does not test runtime adaptation or control-reactive configuration.

The modularity and extensibility evident in SAIBench (Li et al., 2022) can inform SAPA-Bench’s design, with task, model, and metric modules decoupled for flexible scenario description. Adapting DSLs such as SAIL from SAIBench enables rapid composition of workload profiles and metric suites.

Implementation of SAPA-Bench requires instrumentation of runtime monitors, integration of ML controllers, and a workload harness that triggers and times adaptation events. Data and configuration are parameterized to induce meaningful transitions and enable rigorous evaluation.

7. Significance and Outlook

SAPA-Bench advances benchmarking methodology by prioritizing adaptive system behaviors—dynamic resource reallocation, runtime approximation, and intelligent control. It provides a quantitative foundation for assessing trade-offs in power, latency, resiliency, and output quality, thereby facilitating the co-design of hardware/software systems poised for context-aware, energy-centric computation. In practical terms, SAPA-Bench identifies the cost–quality boundaries of adaptation and sets expectations for next-generation systems integrating autonomous resource management.

A plausible implication is that, as adaptive architectures become more prevalent, benchmarking frameworks like SAPA-Bench will be essential for fair comparative evaluation, optimization guidance, and validation of self-optimizing computing platforms against traditional and static designs.