Scalable Deep Learning-Based Microarchitecture Simulation on GPUs

Author:

Pandey Santosh1,Li Lingda2,Flynn Thomas2,Hoisie Adolfy2,Liu Hang1

Affiliation:

1. Stevens Institute of Technology,Hoboken,USA

2. Brookhaven National Laboratory,Upton,USA

Funder

Brookhaven National Laboratory

Publisher

IEEE

Reference48 articles.

1. Dadiannao: A Machine-Learning Supercomputer;chen;the 47th Annual IEEE/ACM International Symposium on Microarchitecture,2014

2. An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

3. Dynamic branch prediction with perceptrons

4. Evaluation of the Riken Post-k Processor Simulator;kodama;ArXiv Preprint,2019

5. Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation Using Deep Neural Networks;mendis;International Conference on Machine Learning (ICML),2019

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1. TAO: Re-Thinking DL-based Microarchitecture Simulation;ACM SIGMETRICS Performance Evaluation Review;2024-06-11

2. TAO: Re-Thinking DL-based Microarchitecture Simulation;Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems;2024-06-10

3. TAO: Re-Thinking DL-based Microarchitecture Simulation;Proceedings of the ACM on Measurement and Analysis of Computing Systems;2024-05-21

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