2024微軟人工智能系統(tǒng)深度學(xué)習(xí)中的分布式訓(xùn)練算法

單一設(shè)備Top-5accuracyvs.computationalcomplexity(singleforwardpass)BenchmarkAnalysisofRepresentativeDeepNeuralNetworkArchitectures/pdf/1810.00736.pdf模型相關(guān)，相對(duì)固定 可變因素可變因素工作重點(diǎn)Moore定律，可變因素工作重點(diǎn)相對(duì)有限本章內(nèi)容本章內(nèi)容下章內(nèi)容 TensorFlow

Data-FlowGraph(DFG)x=tf.placeholder(tf.float32)W=x=tf.placeholder(tf.float32)W=b=tf.Variable(tf.float32)m=W*xs=m+by=tf.reduce_sum(s)grad_W,grad_b=tf.gradients(y,[W,b])update=optimizer.apply_gradients({[W,grad_W],[b,grad_b]})xWb??W??b*m*??++????ms??sΣΣ??y x=tf.placeholder(tf.float32)W=b=tf.Variable(tf.float32)

多個(gè)樣本

Data-FlowGraph(DFG)m=W*xs=m+by=tf.reduce_sum(s)grad_W,grad_b=tf.gradients(y,[W,b])update=optimizer.apply_gradients({[W,grad_W],[b,grad_b]})

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多個(gè)操作Σ y

張量計(jì)算算子間并行數(shù)據(jù)并行：多個(gè)樣本并行執(zhí)行模型并行：多個(gè)算子并行執(zhí)行組合并行：多種并行方案組合疊加https://blog.skymind.ai/distributed-deep-learning-part-1-an-introduction-to-distributed-training-of-neural-networks/ im2colconvolution im2colconvolution JosephE.GonzalezAI-SystemsDistributedTraining算子內(nèi)并行算子并行：并行單個(gè)張量計(jì)算子內(nèi)的計(jì)算（GPU多處理單元并行）https://blog.skymind.ai/distributed-deep-learning-part-1-an-introduction-to-distributed-training-of-neural-networks/AllReducex W b *

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GPU0

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Compute Communicate

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