A Parallel GEM5-Based Simulation Infrastructure for Multicluster SoC Performance Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid adoption of heterogeneous multicluster architectures in modern Systems-on-Chip (SoCs) has increased the need for scalable and accurate simulation tools. GEM5 continues to be widely used across academia and industry for microarchitectural exploration, yet its single-threaded event loop limits simulation throughput when evaluating SoCs composed of many interacting CPU clusters, GPUs, NPUs, and memory subsystems. To overcome this bottleneck, we propose PGSI (Parallel GEM5-based Simulation Infrastructure), a parallel simulation framework designed to extend GEM5 while preserving cycle-accurate fidelity. PGSI introduces cluster-level parallelism, a deterministic global synchronization barrier, a lock-free shared-memory emulation layer, and a cycle-accurate Network-on-Chip (NoC) timing model. Across PARSEC, SPEC CPU2017, MobileNet inference, and Android micro-services, PGSI achieves an average 3.4× speed-up over baseline GEM5 while maintaining <2% deviation in IPC, memory latency, and end-to-end execution time. PGSI demonstrates that cycle-accurate simulation of large heterogeneous SoCs can be parallelized effectively without rollback or hardware-assisted execution, providing a practical foundation for future architectural research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it