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DOPPLER framework optimizes ML workloads with dual-policy learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DOPPLER, a novel three-stage framework for optimizing device assignment in asynchronous dataflow graphs, particularly for complex machine learning workloads. This system addresses limitations of previous methods by supporting asynchronous systems and integrating both reinforcement learning and expert-designed heuristics. DOPPLER's dual-policy network, comprising selection and placement policies, has demonstrated superior performance in reducing execution time and improving training efficiency compared to existing baselines. AI

IMPACT Introduces a new method for optimizing ML workload execution on asynchronous systems, potentially improving efficiency and reducing training times.

RANK_REASON The cluster contains a research paper detailing a new framework for optimizing ML workloads. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DOPPLER framework optimizes ML workloads with dual-policy learning

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The cluster contains a research paper detailing a new framework for optimizing ML workloads. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xinyu Yao, Daniel Bourgeois, Abhinav Jain, Yuxin Tang, Jiawen Yao, Zhimin Ding, Arlei Silva, Chris Jermaine ·

    DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs

    arXiv:2505.23131v2 Announce Type: replace Abstract: We study the problem of assigning operations in a dataflow graph to devices to minimize execution time in a work-conserving system, with emphasis on complex machine learning workloads. Prior learning-based methods often struggle…

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