Overlapping computation and communication in distributed GPU training slows compute kernels by up to 40% and raises power use, while remaining faster than sequential execution.
PipeDream: generalized pipeline parallelism for DNN training,
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Characterizing Compute-Communication Overlap in GPU-Accelerated Distributed Deep Learning: Performance and Power Implications
Overlapping computation and communication in distributed GPU training slows compute kernels by up to 40% and raises power use, while remaining faster than sequential execution.