SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning
Conference
·
OSTI ID:1889629
While cycle-accurate simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigation. This work describes a concerted effort, where machine learning (ML) is used to accelerate microarchitecture simulation. First, an ML-based instruction latency prediction framework that accounts for both static instruction properties and dynamic processor states is constructed. Then, a GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor, and its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator. Leveraging modern GPUs, the ML-based simulator outperforms traditional CPU-based simulators significantly.
- Research Organization:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)
- DOE Contract Number:
- SC0012704
- OSTI ID:
- 1889629
- Report Number(s):
- BNL-223414-2022-CPPJ
- Country of Publication:
- United States
- Language:
- English
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