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ArchSim: Computer Architecture Simulation as a Service

Sabila Al Jannat, Wenhan Lyu, Le Khanh Trinh Mai, Huizhi Zhao 2026-07-16
Computer × Architecture Agentic Microarchitecture Simulation

ArchSim addresses the problem that computer architecture simulation studies are difficult to scale and reproduce due to implicit encoding of configuration, execution, and analysis in scripts. The method introduces declarative hardware topology graphs that auto-generate simulation code, stateless runners for job orchestration, and structured artifact storage for systematic result exploration. Experimental evidence from a 96-configuration GPU simulation matrix shows a median kernel time error of 0.18% relative to hand-written MGPUSim configurations across 95.8% of configurations, with only 1.6 seconds of overhead per simulation. This matters because ArchSim enables scalable, reproducible, and automated simulation studies without custom tooling, significantly lowering the barrier for comprehensive architecture exploration.

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Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers?

Nishant Aggarwal, Ayushi Dubal, Sreeraj Kannakarankodi, Ian McDougall 2026-07-16
Computer × Architecture Agentic Microarchitecture Simulation LLM

The problem is that existing LLM evaluations focus on summarization rather than deep technical comprehension, which requires structured critique identifying core mechanisms, buried assumptions, and cross-paper contributions. The method introduces Gauntlet, an open-source pipeline using five independent expert-persona reviewers and an adversarial synthesis stage to analyze computer architecture papers. On 20 ISCA 2025 and HPCA 2026 papers, evaluators preferred Gauntlet over human analysis in 15 of 20 comparisons, with significant advantage on Critical Rigor and only vanishing on Calibration, while humans won on trust and usefulness rather than depth. This matters because Gauntlet demonstrates that multi-agent LLM pipelines can outperform humans in deep technical critique, and the released analyses, scores, and rubric provide a community resource for advancing automated paper comprehension.

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