Hardness, Structural Knowledge, and Opportunity: An Analytical Framework for Modular Performance Modeling

• URL: http://arxiv.org/abs/2509.11000v2
• Date: Fri, 19 Sep 2025 16:19:28 GMT
• Title: Hardness, Structural Knowledge, and Opportunity: An Analytical Framework for Modular Performance Modeling
• Authors: Omid Gheibi, Christian Kästner, Pooyan Jamshidi,
• Abstract summary: "Hardness" is defined as the inherent difficulty of performance modeling.<n>We show that modeling hardness is primarily driven by the number of modules and configuration options per module.<n>We demonstrate that both higher levels of structural knowledge and increased modeling hardness significantly enhance the opportunity for improvement.
• Score: 9.1773311943941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Performance-influence models are beneficial for understanding how configurations affect system performance, but their creation is challenging due to the exponential growth of configuration spaces. While gray-box approaches leverage selective "structural knowledge" (like the module execution graph of the system) to improve modeling, the relationship between this knowledge, a system's characteristics (we call them "structural aspects"), and potential model improvements is not well understood. This paper addresses this gap by formally investigating how variations in structural aspects (e.g., the number of modules and options per module) and the level of structural knowledge impact the creation of "opportunities" for improved "modular performance modeling". We introduce and quantify the concept of modeling "hardness", defined as the inherent difficulty of performance modeling. Through controlled experiments with synthetic system models, we establish an "analytical matrix" to measure these concepts. Our findings show that modeling hardness is primarily driven by the number of modules and configuration options per module. More importantly, we demonstrate that both higher levels of structural knowledge and increased modeling hardness significantly enhance the opportunity for improvement. The impact of these factors varies by performance metric; for ranking accuracy (e.g., in debugging task), structural knowledge is more dominant, while for prediction accuracy (e.g., in resource management task), hardness plays a stronger role. These results provide actionable insights for system designers, guiding them to strategically allocate time and select appropriate modeling approaches based on a system's characteristics and a given task's objectives.

Related papers

• Opening the Black Box: Preliminary Insights into Affective Modeling in Multimodal Foundation Models [38.34082435363237]
  We present a systematic mechanistic study of affective modeling in multimodal foundation models.<n>Our results consistently reveal a clear and robust pattern.<n>We identify textttgate_proj as a central architectural locus of affective modeling.
  arXiv  Detail & Related papers  (2026-01-22T12:34:20Z)
• An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models [59.13182819190547]
  Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields.<n>They face challenges such as complex design specifications and scalability issues with large datasets.<n>This paper proposes an Integrated Fusion Framework that merges the strengths of both paradigms to enhance model performance and interpretability.
  arXiv  Detail & Related papers  (2025-11-11T10:28:23Z)
• Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions [65.89403417819764]
  We quantify the impact of design choices on language model capabilities.<n>By incorporating features besides model size and number of training tokens, we can achieve a relative 3-28% increase in ability to predict downstream performance.
  arXiv  Detail & Related papers  (2025-03-05T19:46:04Z)
• Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
  Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based optimization (MBO) problems.<n>We present Cliqueformer, a transformer-based architecture that learns the black-box function's structure through functional graphical models (FGM)<n>Across various domains, including chemical and genetic design tasks, Cliqueformer demonstrates superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-10-17T00:35:47Z)
• Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities [4.389938747401259]
  This work explores the effects of fine-tuning strategies on Large Language Models (LLMs) in domains such as materials science and engineering. We find that the merging of multiple fine-tuned models can lead to the emergence of capabilities that surpass the individual contributions of the parent models.
  arXiv  Detail & Related papers  (2024-09-05T11:49:53Z)
• Configurable Foundation Models: Building LLMs from a Modular Perspective [115.63847606634268]
  A growing tendency to decompose LLMs into numerous functional modules allows for inference with part of modules and dynamic assembly of modules to tackle complex tasks. We coin the term brick to represent each functional module, designating the modularized structure as customizable foundation models. We present four brick-oriented operations: retrieval and routing, merging, updating, and growing. We find that the FFN layers follow modular patterns with functional specialization of neurons and functional neuron partitions.
  arXiv  Detail & Related papers  (2024-09-04T17:01:02Z)
• Experimental evaluation of architectural software performance design patterns in microservices [5.662788913145226]
  This research aims to quantify the effect of design patterns on system performance metrics. Real performance measurements are collected and compared with model-based predictions. Results demonstrate that, despite the difficulty to parameterize our benchmark system, model-based predictions are in line with real experimentation.
  arXiv  Detail & Related papers  (2024-08-20T12:21:40Z)
• Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition [0.8796261172196743]
  We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems. As a demonstrative application, we pursue the modeling of cathodic electrophoretic deposition (EPD), commonly known as e-coating.
  arXiv  Detail & Related papers  (2024-01-16T14:58:21Z)
• Is a Modular Architecture Enough? [80.32451720642209]
  We provide a thorough assessment of common modular architectures, through the lens of simple and known modular data distributions. We highlight the benefits of modularity and sparsity and reveal insights on the challenges faced while optimizing modular systems.
  arXiv  Detail & Related papers  (2022-06-06T16:12:06Z)
• Knowledge-Guided Dynamic Systems Modeling: A Case Study on Modeling River Water Quality [8.110949636804774]
  Modeling real-world phenomena is a focus of many science and engineering efforts, such as ecological modeling and financial forecasting. Building an accurate model for complex and dynamic systems improves understanding of underlying processes and leads to resource efficiency. At the opposite extreme, data-driven modeling learns a model directly from data, requiring extensive data and potentially generating overfitting. We focus on an intermediate approach, model revision, in which prior knowledge and data are combined to achieve the best of both worlds.
  arXiv  Detail & Related papers  (2021-03-01T06:31:38Z)
• S2RMs: Spatially Structured Recurrent Modules [105.0377129434636]
  We take a step towards exploiting dynamic structure that are capable of simultaneously exploiting both modular andtemporal structures. We find our models to be robust to the number of available views and better capable of generalization to novel tasks without additional training.
  arXiv  Detail & Related papers  (2020-07-13T17:44:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.