Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion

2025-03-29

AI Summary

To address the prohibitively slow speed of cycle-accurate simulators (e.g., gem5) in microarchitectural design space exploration, this paper proposes Concorde—a CPU performance modeling framework that synergistically integrates component-wise analytical modeling with lightweight machine learning. Its key innovation lies in the first use of interpretable, analytically derived performance distributions—characterizing caches, pipelines, and branch predictors—as input features to drive distribution-aware representation learning and efficient regression for program-level CPI prediction. Compared to gem5, Concorde achieves >10⁵× speedup with only ~2% mean absolute CPI error. It enables 150 million design evaluations within one hour and supports fine-grained, cross-program and cross-microarchitecture performance attribution. By unifying analytical insight with data-driven generalization, Concorde overcomes the longstanding accuracy-efficiency trade-off inherent in both traditional simulation and purely empirical modeling approaches.

Technology Category

• Machine Learning: Evaluation and Analysis
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Cycle-level simulators such as gem5 are widely used in microarchitecture design, but they are prohibitively slow for large-scale design space explorations. We present Concorde, a new methodology for learning fast and accurate performance models of microarchitectures. Unlike existing simulators and learning approaches that emulate each instruction, Concorde predicts the behavior of a program based on compact performance distributions that capture the impact of different microarchitectural components. It derives these performance distributions using simple analytical models that estimate bounds on performance induced by each microarchitectural component, providing a simple yet rich representation of a program's performance characteristics across a large space of microarchitectural parameters. Experiments show that Concorde is more than five orders of magnitude faster than a reference cycle-level simulator, with about 2% average Cycles-Per-Instruction (CPI) prediction error across a range of SPEC, open-source, and proprietary benchmarks. This enables rapid design-space exploration and performance sensitivity analyses that are currently infeasible, e.g., in about an hour, we conducted a first-of-its-kind fine-grained performance attribution to different microarchitectural components across a diverse set of programs, requiring nearly 150 million CPI evaluations.

Problem

• Develops fast accurate CPU performance modeling methodology
• Replaces slow cycle-level simulators for design exploration
• Predicts program behavior using compact performance distributions

Innovation

• Combines analytical models with ML for performance prediction
• Uses compact performance distributions for microarchitectural analysis
• Enables rapid design-space exploration with high accuracy