Coherence-Aware Task Graph Modeling for Realistic Application

2025-09-10

AI Summary

In multicore systems, cache coherence and task execution are deeply intertwined, yet existing task-graph modeling approaches either rely on predefined structures or target specific schedulers, commonly neglecting coherence interactions—leading to a mismatch between design assumptions and runtime behavior. This work introduces CoTAM, the first framework to explicitly model how cache coherence affects task dependencies. CoTAM decouples coherence effects via runtime behavioral analysis and employs a data-driven learning mechanism to dynamically infer weighted task dependencies, thereby generating coherence-aware, general-purpose task graphs. Experimental results demonstrate that CoTAM significantly outperforms implicit modeling methods, improving task-graph accuracy and adaptability under dynamic workloads, and effectively bridging the semantic gap between system-level design abstractions and actual runtime execution.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

As multicore systems continue to scale, cache coherence has emerged as a critical determinant of system performance, with coherence behavior and task execution closely intertwined, reshaping inter-task dependencies. Task graph modeling provides a structured way to capture such dependencies and serves as the foundation for many system-level design strategies. However, these strategies typically rely on predefined task graphs, while many real-world applications lack explicit graphs and exhibit dynamic, data-dependent behavior, limiting the effectiveness of static approaches. To address this, several task graph modeling methods for realistic workloads have been developed. Yet, they either rely on implicit techniques that use application-specific features without producing explicit graphs, or they generate graphs tailored to fixed scheduling models, which limits generality. More importantly, they often overlook coherence interactions, creating a gap between design assumptions and actual runtime behavior. To overcome these limitations, we propose CoTAM, a Coherence-Aware Task Graph Modeling framework for realistic workloads that constructs a unified task graph reflecting runtime behavior. CoTAM analyzes the impact of coherence by decoupling its effects from overall execution, quantifies its influence through a learned weighting scheme, and infers inter-task dependencies for coherence-aware graph generation. Extensive experiments show that CoTAM outperforms implicit methods, bridging the gap between dynamic workload behavior and existing designs while demonstrating the importance of incorporating cache coherence into task graph modeling for accurate and generalizable system-level analysis.

Problem

• Modeling task dependencies in dynamic applications lacking explicit graphs
• Addressing limitations of static task graph methods for realistic workloads
• Incorporating cache coherence effects into task graph generation

Innovation

• Coherence-aware task graph modeling framework
• Decouples coherence effects from execution
• Learned weighting scheme quantifies coherence influence