Bagua！并行通讯库.pdf

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Bagua！并行通讯库.pdf

1、A Modular but users get “greedy” with new tasks that take weeks on a beefy machine. (Think about BERT) At Kuaishou: Recommendation system (300+M daily users behaviors) Multimedia understanding (dozens of thousands years daily accumulated watching time ) Video preprocessing (200 videos are uploaded p

2、er sec) EC2 Instance: g2.8xlarge 4x GRID K520, 4.89 TFLOPS $2.60 / hour EC2 Instance: p4d.24xlarge 8x A100, 2.5 PFLOPS $ 32.77/h Existing Systems and Our Vision Question: How to accommodate the ever-growing demand of ML training over ever-growing scale of data? o Could be algorithmic solutions: bett

3、er models, algorithms, optimizer. o Could be system solutions: SCALE, SCALE, SCALE We focus on scaling with data parallelism each worker holds a partition of data, and they jointly train a single ML model. BytePS Amazing Systems Amazing Techniques Decentralized Training Communication Quantization Communication Sparsification Asynchronous Training GAP: Current Amazing Systems Dont Support Recently

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