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Measuring Sustainability in Multi-Scale High-Performance Computing

Carlos J Barrios, Frédéric Le Mouël, Yves Denneulin

arXiv:2609.08688v1cs.DC

TL;DR

Diverse workloads and resources make effective resource allocation and execution challenging in Multi-Scale HPC. The paper proposes the Characteristic Ratio to assess performance, utilization, energy efficiency, and required accuracy. Its characterization describes different module choices for lower- and higher-precision tasks, while noting that workload-aware weighting can add complexity or bias.

  • Problem

    Diverse workloads and resources leave persistent challenges for resource allocation and efficient execution.

    Source: §II · p. 3

  • Method

    The Characteristic Ratio combines architectural performance, utilization, energy efficiency, workload complexity, and required accuracy to assess operating points.

    Source: E. Characteristic Ratio · p. 5, §VI · p. 10

  • Results

    Low-to-moderate-accuracy tasks can be assigned to energy-efficient GPUs or low-power cores, while high-precision tasks are reserved for CPU nodes at increased energy use.

    Source: §IV · p. 6

  • Takeaways & Limitations

    The Characteristic Ratio is presented as a physically justified way to identify operating points and inform deployment decisions in Multi-Scale hybrid HPC systems.

    Source: §VI · p. 10

    Applying weights to low-level counters may increase model complexity or introduce biases in correlations.

    Source: §V · p. 9

Abstract

from arXiv · show

The transition from traditional High Performance Computing (HPC) to the Computing Continuum emphasizes efficient resource management and sustainable practices across Multi-Scale hybrid architectures. This paper introduces a multidimensional metric framework to characterize these systems and guide deployment strategies for modern workloads. The framework combines Architectural Performance metrics (such as Throughput, Latency, Scalability), System Utilization, and key Sustainability and Accuracy indicators (such as Energy Efficiency and Power Consumption). Using a modular hybrid testbed, experiments reveal complex relationships among metrics, especially the trade-offs between accuracy and energy, and the efficiency of hybrid nodes. The guidelines help identify optimal operating points and lay the groundwork for improving orchestrators and schedulers (e.g., Kubernetes) to assign demanding applications, including AI and Quantum Computing, to suitable system modules, ensuring high performance and sustainability.

I. INTRODUCTION

Multi-Scale HPC addresses the challenge of managing diverse workloads across heterogeneous, interconnected systems. The paper defines this capability across hardware, algorithmic, and temporal/energy scales and proposes metrics to guide monitoring and sustainable deployment.

I. INTRODUCTION

  • Hybrid or heterogeneous HPC combines diverse computer architectures to handle workloads across multiple scales.The introduction distinguishes architectural heterogeneity from scaling through hybridity.Source: §I · p. 1
  • Figure 1 relates workload scale to peak performance, data capacity, and bandwidth, with workloads classified as intensive or massive.The paper describes system and software platforms as tailored to workload types and deployment needs.Source: §I · pp. 1–2, Figure 1 · p. 2
  • Multi-Scale HPC is defined as managing workloads across interconnected dimensions spanning hardware, algorithmic, and temporal/energy scales.The hardware dimension ranges from heterogeneous cores to accelerated nodes.Source: §I · p. 2
  • The proposed metric framework uses workload analysis to support monitoring and characterize sustainability and scalability in hybrid architectures.It addresses diverse workloads and aims to lay groundwork for sustainable Multi-Scale hybrid systems.Source: §I · p. 2

II. KEY CONCEPTUAL ELEMENTS AND CHARACTERISTICS

Multi-Scale HPC combines diverse architectures and resource layers to support workloads with different scales and parallelism needs. The section highlights resource-allocation challenges and the need to align system characteristics with workload profiles.

II. KEY CONCEPTUAL ELEMENTS AND CHARACTERISTICS

  • Integrated Multi-Scale systems must coordinate heterogeneous components and data transfer, while aligning scalability, performance, interoperability, and resource management.The section identifies architectural integration and distributed organization as conceptual elements of these systems.Source: §II · pp. 2–3
  • Hybrid cluster nodes combine CPUs and GPUs for complex workloads, while Massive Parallel Processing applications require multilevel parallelism to scale.The node configuration is presented as supporting increasing workload complexity and data volume.Source: §II · p. 3, Figure 2 · p. 3
  • Multilevel parallelism assigns fine-grained work to cores and coarse-grained components to processors or nodes, supporting scalability across granularities.The section also identifies instruction-level parallelism as a form of parallelism.Source: §II · p. 3, Figure 3 · p. 3
  • Resource allocation and efficient execution remain challenging because workloads and resources vary, requiring effective communication, scheduling, and optimization.Multi-Scale applications also require coordination across nodes and malleable resource use for diverse workload profiles.Source: §II · pp. 2–3
  • Multi-Scale architectures span horizontal platform types and vertical functional layers, from core computing through fog/exchange to cloud-level interaction.The framework considers hardware resources horizontally and algorithmic complexity, jobs, and scalability vertically when developing workload profiles.Source: §II · p. 3, Figure 4 · p. 3
  • The paper holds I/O performance constant to isolate the relationship between compute precision and energy.It therefore does not define I/O performance, while noting that alternative methods can characterize it.Source: §II · p. 3

III. MULTIDIMENSIONAL METRIC FRAMEWORK

The framework replaces isolated performance measures with composite operational indicators that account for energy and precision trade-offs in heterogeneous environments. It maps execution attributes into a multiscale vector whose components shape the Characteristic Ratio.

III. MULTIDIMENSIONAL METRIC FRAMEWORK

  • The framework combines architectural capabilities and environmental behavior, with energy efficiency at their intersection, to guide deployment across hybrid architectures.The metrics support holistic, monitoring-based analysis and visualization, system optimization, and future architectural design for multiscale computations.Source: §III · p. 4
  • Isolated measures such as peak FLOPS, throughput, or network latency do not capture energy and precision trade-offs in heterogeneous environments.The paper therefore formulates composite operational indicators.Source: §III · p. 4
  • The execution vector uses Energy-Efficiency Resource Conversion, Complexity Factor, and Required Accuracy, which influence the Characteristic Ratio beyond static benchmarks.EERC measures computational output per watt-hour considering power caps; CF evaluates runtime scaling, while RA accounts for error-tolerance limits.Source: §III · p. 4

A. Metric Framework Model

The framework characterizes systems by bringing together converging metric categories, then uses architectural performance measures to describe capability, speed, and scaling. This joint view supports system characterization and operating-point-guided scheduling.

A. Metric Framework Model

  • Figure 5 is a conceptual model for defining metric categories and understanding their convergence in system characterization.The framework emphasizes analyzing metric convergence rather than evaluating individual measures in isolation.Source: A. Metric Framework Model · p. 4, Figure 5 · p. 4
  • Architectural Performance, System Utilization, and Sustainability and Accuracy converge to characterize systems through capability, resource use, result integrity, and environmental cost.The convergence links energy efficiency ratios derived from performance and power data to operating-point identification and scheduling for hardware efficiency and performance.Source: A. Metric Framework Model · p. 4

B. Architectural Performance Metrics

  • Architectural performance metrics include throughput, latency, and scalability, describing workload volume, single-task completion time, and response to added resources.Scalability may be linear, with proportional performance gains, or sub-linear, with gains diminishing as resources increase.Source: B. Architectural Performance Metrics · p. 4

C. System Utilization Metrics

Utilization metrics show how efficiently system resources are used and help identify bottlenecks, while their interdependencies make joint monitoring important. Memory or network demands can affect processor utilization and system efficiency.

C. System Utilization Metrics

  • System metrics characterize resource use and can identify bottlenecks, optimize performance, and prevent underuse or overuse.The listed measures include processor utilization, memory usage, and network bandwidth.Source: C. System Utilization Metrics · p. 4
  • Resource metrics interact: memory-bandwidth limits can leave processors underutilized, while network-intensive workloads can increase processor use through packet processing.High processor utilization may coincide with elevated memory use, and network transfers can require memory for buffering and caching.Source: C. System Utilization Metrics · p. 5

D. Sustainability and Accuracy Metrics

Sustainability and accuracy metrics assess resource efficiency, energy consumption, and precision for diverse multiscale workloads. The framework emphasizes harmonizing these measures and defining quantifiable ratios for system characterization.

D. Sustainability and Accuracy Metrics

  • Energy efficiency compares the energy consumed to complete a task across systems, executions, or configurations, while power consumption indicates environmental impact and operating cost.Source: D. Sustainability and Accuracy Metrics · p. 5
  • Accuracy requirements vary by workload: scientific simulations may require double precision, whereas AI and deep learning may use FP16 or lower to improve performance and reduce energy with little accuracy impact.The discussion also notes that quantum-computing precision depends on coherence and error minimization.Source: D. Sustainability and Accuracy Metrics · p. 5
  • Accuracy assessment should span instruction, core, node, and inter-node communication levels, and system characterization should consider accuracy alongside speed and efficiency.The Optimal Operation Point is defined as a function of throughput, scalability, energy efficiency, and accuracy.Source: D. Sustainability and Accuracy Metrics · p. 5
  • Metrics must be harmonized in the multilevel framework and associated with quantifiable ratios to characterize multiscale HPC performance and sustainability.Source: D. Sustainability and Accuracy Metrics · p. 5

E. Characteristic Ratio

The Characteristic Ratio (CR) combines architectural performance and energy efficiency with workload complexity and required accuracy to characterize a system.

E. Characteristic Ratio

  • CR formalizes a multidimensional structure for calculating and weighting system characteristics.It is presented as a bridge from guidelines to a framework.Source: E. Characteristic Ratio · p. 5
  • The composite Architectural Performance index derives from normalized throughput, latency, and scalability.EERC is the Energy-Efficiency Resource Conversion ratio; CF denotes algorithmic complexity, and RA the workload’s required accuracy.Source: E. Characteristic Ratio · p. 5
  • CF is a dynamic, dimensionless workload-difficulty measure, while RA penalizes workloads requiring very high accuracy.CF is assigned 1.0 for O(n) workloads and 1.5 for O(n^2) or higher interdependencies.Source: E. Characteristic Ratio · p. 5

IV. MULTI-SCALE HPC SYSTEMS CHARACTERIZATION

Characterization of the modular hybrid testbed shows that workload size and node configuration shape performance, energy use, and accuracy. The findings identify trade-offs and module transitions relevant to matching workloads with resources.

IV. MULTI-SCALE HPC SYSTEMS CHARACTERIZATION

  • The evaluation spans workloads of 5,000–30,000 MB across 2–30 nodes, with average processor utilization between 89.7% and 97.3%.The testbed includes CPU-only and CPU-GPU modules with differing processor and GPU configurations.Source: §IV · p. 6
  • In the tested workloads, high memory use is mainly attributable to the process itself.The authors describe the hybrid architecture as helping manage resource trade-offs, with lower-to-moderate accuracy tasks assigned to GPUs or low-power cores and high-precision tasks to CPUs.Source: §IV · pp. 6–7, Figure 9 · p. 7
  • Workloads of 20,000–30,000 MB overload the system at the transition from CPU-only module 1 to CPU-GPU module 2, where throughput is limited by the slowest component.Performance remains stable when shifting between CPU-GPU modules 3 and 4.Source: §IV · pp. 6–7, Figure 7 · p. 6
  • Latency is high on nodes 1–6 and low across nodes 8–30, increasing slightly as workloads grow.The authors associate high bandwidth with modules that have the most resources.Source: §IV · p. 7, Figure 8 · p. 7
  • CPU-GPU nodes average around 100 Watts per node and show superior energy efficiency, while CPU nodes typically consume more power despite reduced frequencies.The CPU-GPU node’s total power combines the consumption of its CPU and GPU modules.Source: §IV · p. 7, Figure 10 · p. 7
  • The paper introduces accuracy–energy trade-off and architectural influence analyses for evaluating hybrid HPC systems.It notes that more processing support improves efficiency while also increasing energy consumption.Source: §IV · p. 7
  • Accuracy and energy consumption both depend on workload size and node configuration; GPU nodes deliver better performance.Energy consumption rises with workload size and total node power, while the plotted accuracy is capped at 1.0.Source: §IV · p. 8, Figure 11 · p. 8, Figure 12 · p. 8
  • 25% lower energy accompanies accuracy reductions from 99.5% to 98%, with minimal quality loss.The paper uses this saturation trade-off to motivate penalizing accuracy overkill through RA in the CR.Source: §IV · p. 8, Figure 12 · p. 8

A. Extending the CR Metric to Quantum and Edge Domains

The CR formulation is extended to quantum and edge settings by mapping technology-specific parameters into its multidimensional vector. The current experimental validation, however, focuses on classical heterogeneous HPC nodes.

A. Extending the CR Metric to Quantum and Edge Domains

  • Experimental validation currently focuses on classical heterogeneous HPC nodes, while CR is formulated to accommodate Quantum and Edge paradigms.Their technology-specific parameters are mapped into Θ = ⟨EERC, CF, RA⟩.Source: A. Extending the CR Metric to Quantum… · p. 8
  • For quantum-classical hybrid execution, RA depends on gate fidelity and coherence time, CF incorporates circuit depth and qubit width, and EERC includes cryogenic cooling overhead per circuit.The passage specifies RAQ = f(F, T1).Source: A. Extending the CR Metric to Quantum… · p. 8
  • For edge systems, EERC is adjusted to account for network transmission overhead alongside computational power consumption.The edge formulation expands the denominator to include network communication overhead; β balances offload latency and transmission energy costs.Source: A. Extending the CR Metric to Quantum… · p. 8

V. DISCUSSION AND FURTHER WORK

The discussion frames sustainable Multi-Scale HPC analysis around workload-aware, multidimensional metrics that account for performance, energy, and accuracy. It also identifies limits in current measurements and points to configuration guidance for heterogeneous workloads.

V. DISCUSSION AND FURTHER WORK

  • CPU-and-GPU node power depends on workload and module efficiency, while integrated GPUs sharing CPU memory can reduce power and heat.The paper identifies integrated GPUs as suitable for energy-constrained settings.Source: §V · p. 9
  • Accuracy depends on floating-point support, error-correction methods, and approximation tolerance, while the discussion relies on manufacturers’ standard values and metrics.I/O performance is not explicitly defined; future CR iterations are to add an I/O Wait-Time coefficient for data-intensive bottlenecks.Source: §V · p. 9
  • A workload weighting factor can emphasize relevant hardware features, such as Memory Bandwidth for data-intensive jobs, but regression weights may add complexity or bias.The factor transforms generic hardware performance data into a workload-aware metric.Source: §V · p. 9
  • The guidelines combine workload-specific performance, scalability, resilience, carbon footprint, and energy efficiency to support developer decisions.They are intended to empower developers rather than impose control.Source: §V · p. 9
  • Configuration guidance includes setting up Kubernetes infrastructure to align with specific metrics and limitations while maintaining performance accuracy.The framework aims to characterize both Multi-HPC systems and applications.Source: §V · p. 9
  • AI and quantum-computing workloads motivate specialized modules, while growing data, storage, speed, and integration demands are identified as relevant to sustainability metrics.The examples include quantum simulators rather than real quantum computers.Source: §V · p. 9

VI. CONCLUSION

The conclusion presents the Characteristic Ratio as a multidimensional way to assess performance, utilization, energy efficiency, and required accuracy across Multi-Scale HPC. The proposed metrics support workload characterization and deployment guidance, while the paper notes limits to monitoring and the need to contextualize metrics.

VI. CONCLUSION

  • The CR excludes a utility-based Error Tolerance Cost because it is subjective, with Required Accuracy instead constraining the denominator and penalizing modules below the minimum.This keeps the metric based on physical and architectural data.Source: §VI · p. 9
  • Monitoring loses effectiveness once application features are known, including in well-known applications or pre-profiled containerized environments.The paper points instead to policies for execution and optimization within modular systems.Source: §VI · p. 9
  • Deployment guidance targets orchestrators, schedulers, and deployers, but effective characterization requires linking multiple metrics to the analysis context.The paper gives Kubernetes as an example of directing workloads to modules based on workload and energy-efficiency goals.Source: §VI · p. 10
  • The Characteristic Ratio assesses trade-offs among architectural performance, utilization, energy efficiency, and Required Accuracy to identify an optimal operating point.The results are presented as relevant to low-interaction scientific workloads and high-interaction AI applications.Source: §VI · pp. 9–10
  • The guidelines aim to balance speed with sustainable, high-quality performance while supporting resource-allocation decisions across the Computing Continuum.The paper describes the CR as a physically justified alternative to basic energy-per-operation measurements.Source: §VI · p. 10
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