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Energy-efficient multi-GPU scheduling can be significantly enhanced by jointly optimizing task placement and DVFS, yielding up to 14.8% energy savings without sacrificing performance.
Achieving up to 25.5% faster multi-GPU training by overlapping computation and communication could redefine efficiency benchmarks in large-scale ML systems.
