GPEmu: A GPU Emulator for Faster and Cheaper Prototyping and Evaluation of Deep Learning System Research
Summary: GPEmu: a GPU emulator enabling DL systems prototyping/evaluation without real GPUs via time emulation, memory emulation, distributed execution and sharing support. Scales to 30+ models and 6 GPU configs, reproduces 9 papers' results and speeds micro-optimization prototyping. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Meng Wang
- 2. Gus Waldspurger
- 3. Naufal Ananda
- 4. Yuyang Huang
- 5. Kemas Wiharja
- 6. John Bent
- 7. Swaminathan Sundararaman
- 8. Vijay Chidambaram
- 9. Haryadi S. Gunawi
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 411 | PyTorch Distributed: Experiences on Accelerating Data Parallel Training | 2020 | VLDB | 0.00023906921 |
| 1,504 | Analyzing and Mitigating Data Stalls in DNN Training | 2021 | VLDB | 0.00011642333 |
| 2,170 | tf.data: A Machine Learning Data Processing Framework | 2021 | VLDB | 9.3821603e-05 |
| 2,688 | Accelerating Recommendation System Training by Leveraging Popular Choices | 2022 | VLDB | 8.2991144e-05 |
| 2,902 | PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel | 2023 | VLDB | 7.93939e-05 |
| 4,180 | FastFlow: Accelerating Deep Learning Model Training with Smart Offloading of Input Data Pipeline | 2023 | VLDB | 6.3793352e-05 |
| 5,084 | In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle | 2022 | SIGMOD | 5.7091191e-05 |
| 5,552 | GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning | 2023 | SIGMOD | 5.4402488e-05 |
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