Apollo uses temporal-spatial multiplexing and a performance model to let multiple multimodal model modules share GPUs, delivering up to 1.31x training speedup in testbed experiments.
InProceedings of the Nineteenth European Conference on Computer Systems(2024), EuroSys ’24, Association for Computing Machinery, pp
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Mosaic: Towards Efficient Training of Multimodal Models with Spatial Resource Multiplexing
Apollo uses temporal-spatial multiplexing and a performance model to let multiple multimodal model modules share GPUs, delivering up to 1.31x training speedup in testbed experiments.