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# Interference-aware multiplexing for deep learning in GPU clusters: A middleware approach

## Meta Info

Presented in [SC 2023](https://doi.org/10.1145/3581784.3607060).

## Understanding the paper

### Opportunities in co-locating DL training tasks

* Tune training configurations (e.g., batch size) across all co-located tasks
* Choose appropriate tasks to multiplex on a GPU device

### Challenges

* Trade-off between *mitigating interference* and *accelerating training progress* to achieve optimal training time
* Vast search space of task configurations
* Coupling between *adjusting task configurations* and *designing task placement policies*


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