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Openai/69256df6-133c-8005-ae25-fcf7c8af0f22
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==== 大規模分散学習では、勾配同期やモデル並列で GPU/TPU間のテンソル通信 が必要です。XLAはこの部分も間接的に最適化します： ==== # All-Reduce / Collective Ops のスケジューリング - TPUでは tf.distribute.Strategy や tf.tpu.CrossReplicaSum と連携 - XLAは「いつ通信するか、どのテンソルをまとめて送るか」を決定 - 通信と計算を重ね合わせ（overlap）できるようにスケジューリング # パイプライン並列・モデル並列の最適化 - TPUメッシュやGPUクラスタのネットワークトポロジに応じて、 テンソルの送信順序やチャンク分割を自動生成 - これにより通信待ちでGPU/TPUが空転する時間を削減 # デバイス配置最適化 - 同じ計算ノード内でテンソルをできるだけ局所化 - 離れたノード間通信を最小化
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