A Combined Autotuning and Runtime Resource Management Framework for Dynamic Workload Optimization on Homogeneous Architectures
Background and Motivation
As computing platforms grow increasingly heterogeneous in their application demands—even on homogeneous hardware—they face the dual challenge of sustaining application performance and minimizing power consumption. Autotuners can optimize software-level parameters such as algorithm resolution or task granularity, while runtime managers tune architectural features like CPU frequency or core allocation. However, treating these components in isolation limits control scope and system-wide optimization potential. The motivation behind this work is to design a holistic strategy that leverages the strengths of both approaches for more resilient and energy-aware system behavior.
Integrated Two-Level Governor Architecture
The proposed framework introduces a two-level governor designed to unify the hardware and software optimization layers. The lower layer implements application autotuners to adjust software parameters based on quality and performance constraints. The upper layer integrates runtime resource management to allocate computing resources and configure architectural knobs. This hierarchical design enables coordinated decision-making across the entire stack, ensuring that application demands are met without sacrificing global efficiency. By sharing system metrics and performance feedback, both layers collaborate to maximize energy savings while maintaining performance accuracy.
Resource Distribution Policies and Power-Aware Control
Central to the framework is the advanced resource management policy that carefully distributes computing resources among simultaneous applications. The policy accounts for performance requirements, quality-of-result thresholds, and real-time workload variations. It uses frequency scaling, core allocation, and other architectural controls to enforce performance guarantees. Crucially, the policy incorporates power-aware strategies that limit unnecessary energy use, thereby reducing system overhead and improving power scalability in dynamic scenarios.
Autotuning for Performance and Quality Assurance
The application-level autotuner complements architectural control by adjusting software-level parameters such as frame resolution, iteration counts, or sampling rates. Its primary goal is to maintain output quality and satisfy performance constraints while masking application-specific details from the global controller. This decoupling allows the resource manager to operate with simplified abstractions, making the system more modular, portable, and adaptive. The autotuner' s ability to optimize algorithmic choices in real time significantly enhances the framework’s responsiveness to workload variability.
Experimental Evaluation and Results
The framework was experimentally validated on a homogeneous workstation-class architecture, demonstrating significant improvements over conventional single-layer optimization approaches. Results show that the integrated governor achieves system stability under highly dynamic workloads while substantially reducing power consumption. Notably, the combined method saves over 72% more power compared to traditional, isolated solutions. These findings highlight the framework’s ability to effectively balance performance, accuracy, and efficiency, making it viable for future high-performance and energy-aware computing systems.
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