Filtered by: Microarchitecture × Simulation × Clear all

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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CLIP-3D: Closed-Loop Evaluation of Performance and Physical Constraints for 3D ICs

Shuo Ren, Libo Shen, Yaohui Han, Leilei Jin 2026-07-16
Gem5 Microarchitecture × Simulation ×

CLIP-3D addresses the problem that early-stage 3D-IC architectural exploration using simulators like gem5 fails to account for layout-driven thermal, wire, and cache effects that determine actual throughput. The method introduces a shift-left flow that lifts architectural configurations into a physical block representation via McPAT, CACTI, and HotSpot, then co-optimizes cross-tier macro assignment and in-plane placement using an analytical 3D thermal-aware floorplanner. The abstract does not disclose experimental results. This matters because it enables early-stage design selection based on realized BIPS rather than surrogate metrics, preventing throttling on silicon before sign-off.

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Microflow: Microarchitectural Causal Observability for Deep Cross-Layer Analysis and Optimization

Saber Ganjisaffar, Chengyu Song, Nael Abu-Ghazaleh 2026-07-16
Microarchitectural Microarchitecture × Simulation ×

The problem is that existing architectural simulators expose aggregate metrics or raw traces but fail to reveal complex interactions among microarchitectural events and their relationship to program execution. Microflow introduces an observability framework that elevates causality to a first-class analytical object by transforming execution traces into the Microflow Intermediate Representation (MFIR), which explicitly captures dependencies across software semantics, instructions, microarchitectural events, and hardware resources. Experimental evidence on two SPEC CPU 2017 benchmarks demonstrates that Microflow precisely attributes stalls, reveals unobservable phenomena, and enables exact critical-path decomposition, uncovering hidden misprediction costs in leela and cross-loop-iteration contention in mcf. This matters because making causality queryable provides a strong foundation for systematic performance analysis and hardware-software co-design, enabling architects to attribute performance symptoms to root causes across abstraction layers.

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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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