A Parallel GEM5-Based Simulation Infrastructure for Multicluster SoC Performance Evaluation
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".