Scalable Deep Learning-Based Microarchitecture Simulation on GPUs

Li, Lingda; Pandey, Santosh; Li, L; Flynn, T; Hoisie, A; Liu, H

doi:10.1109/SC41404.2022.00084

Scalable Deep Learning-Based Microarchitecture Simulation on GPUs

Conference · 13 November 2022

DOI:https://doi.org/10.1109/SC41404.2022.00084· OSTI ID:1989626

Li, Lingda; Pandey, Santosh; Li, L; Flynn, T; Hoisie, A; Liu, H

Cycle-accurate microarchitecture simulators are essential tools for designers to architect, estimate, optimize, and manufacture new processors that meet specific design expectations. However, conventional simulators based on discrete-event methods often require an exceedingly long time-to-solution for the simulation of applications and architectures at full complexity and scale. Given the excitement around wielding the machine learning (ML) hammer to tackle various architecture problems, there have been attempts to employ ML to perform architecture simulations, such as Ithemal and SimNet. However, the direct application of existing ML approaches to architecture simulation may be even slower due to overwhelming memory traffic and stringent sequential computation logic. This work proposes the first graphics processing unit (GPU)-based microarchitecture simulator that fully unleashes the potential of GPUs to accelerate state-of-the-art ML-based simulators. First, considering the application traces are loaded from central processing unit (CPU) to GPU for simulation, we introduce various designs to reduce the data movement cost between CPUs and GPUs. Second, we propose a parallel simulation paradigm that partitions the application trace into sub-traces to simulate them in parallel with rigorous error analysis and effective error correction mechanisms. Combined, this scalable GPU-based simulator outperforms by orders of magnitude the traditional CPU-based simulators and the state-of-the-art ML-based simulators, i.e., SimNet and Ithemal.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21); ~OTHER

DOE Contract Number:: SC0012704

OSTI ID:: 1989626

Report Number(s):: BNL-224538-2023-COPA

Resource Relation:: Conference: SC '22: The International Conference on High Performance Computing, Networking, Storage and Analysis, Dallas, TX, 11/13/2022 - 11/18/2022

Country of Publication:: United States

Language:: English

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