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Creators/Authors contains: "Huang, Chien-Chin"

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  1. ; ; (, International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS))
    It is known that deeper and wider neural networks can achieve better accuracy. But it is difficult to continue the trend to increase model size due to limited GPU memory. One promising solution is to support swapping between GPU and CPU memory. However, existing work on swapping only handle certain models and do not achieve satisfactory performance. Deep learning computation is commonly expressed as a dataflow graph which can be analyzed to improve swapping. We propose SwapAdvisor, which performs joint optimization along 3 dimensions based on a given dataflow graph: operator scheduling, memory allocation, and swap decisions. SwapAdvisor explores the vast search space using a custom-designed genetic algorithm. Evaluations using a variety of large models show that SwapAdvisor can train models up to 12 times the GPU memory limit while achieving 53-99% of the throughput of a hypothetical baseline with infinite GPU memory. 
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  2. ; ; (, EuroSys '19: Proceedings of the Fourteenth EuroSys Conference 2019)
    This paper presents Tofu, a system that partitions very large DNN models across multiple GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow graph of fine-grained tensor operators used by platforms like MXNet and TensorFlow. In order to automatically partition each operator, we propose to describe the semantics of an operator in a simple language inspired by Halide. To optimally partition different operators in a dataflow graph, Tofu uses a recursive search algorithm that minimizes the total communication cost. Our experiments on an 8-GPU machine show that Tofu enables the training of very large CNN and RNN models. It also achieves 25% - 400% speedup over alternative approaches to train very large models. 
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