English(EN) DynaFlow: Transparent and Flexible Intra-Device Parallelism via Programmable Operator Scheduling

DynaFlow框架通过可编程算子调度增强机器学习并行性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了DynaFlow，一个旨在提高机器学习推理和训练设备内并行性的新框架。DynaFlow将逻辑模型定义与物理执行调度解耦，允许以最小的代码更改透明地集成并行策略。这种方法旨在克服现有框架需要侵入性、模型特定大修的局限性。DynaFlow在六个最先进的ML系统中展示了高达1.29倍的吞吐量提升。 AI

影响通过提高资源利用率，实现更高效的机器学习模型训练和推理。

排序理由该集群包含一篇详细介绍机器学习设备内并行新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yi Pan, Yile Gu, Jinbin Luo, Yibo Wu, Ziren Wang, Hongtao Zhang, Ziyi Xu, Shengkai Lin, Baris Kasikci, Stephanie Wang · 2026-10-02 04:00

DynaFlow：通过可编程算子调度实现透明且灵活的设备内并行

arXiv:2605.21603v1 Announce Type: cross Abstract: Intra-device parallelism addresses resource under-utilization in ML inference and training by overlapping the execution of operators with different resource usage. However, its wide adoption is hindered by a fundamental conflict w…

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报道来源 [1]

DynaFlow：通过可编程算子调度实现透明且灵活的设备内并行

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