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source/_data/SymbioticLab.bib

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Cornserve allows model developers to describe the computation graph of generic Any-to-Any models, which consists of heterogeneous components such as multimodal encoders, autoregressive models like Large Language Models (LLMs), and multimodal generators like Diffusion Transformers (DiTs). Given this, Cornserve's planner automatically finds an optimized deployment plan for the model, including whether and how to disaggregate the model into smaller components based on model and workload characteristics. Cornserve's distributed runtime then executes the model per the plan, efficiently handling Any-to-Any model heterogeneity during online serving. Evaluations show that Cornserve can efficiently serve diverse Any-to-Any models and workloads, delivering up to 3.81x throughput improvement and up to 5.79x tail latency reduction over existing solutions.
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@article{sigdgx:cacm25,
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author = {Chen, Ang and Ratnasamy, Sylvia and Alizadeh, Mohammad and Chowdhury, Mosharaf and Guikema, Seth and Huang, Ryan and Jaganathann, Suresh and Kerkez, Branko and Lee, Edward and Low, Steven and Mao, Morley and Mathieu, Johanna and Reiter, Michael and Wang, Xinyu and Cerf, Vinton},
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title = {Resilient Infrastructures via Digital Unification},
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journal = {Communications of the ACM},
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year = {2025},
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month = {Dec},
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volume = {69},
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number = {1},
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pages = {38–40},
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publist_confkey = {CACM:69(1)},
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publist_link = {paper || https://dl.acm.org/doi/full/10.1145/3736713},
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}

source/publications/index.md

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name: ACM SIGEnergy Energy Informatics Review
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date: 2023-10-25
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url: https://dl.acm.org/toc/sigenergy-eir/2023/3/3
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CACM:
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category: Journals
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occurrences:
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- key: 'CACM:69(1)'
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name: Communications of the ACM
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date: 2025-12-04
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url: https://dl.acm.org/doi/full/10.1145/3736713
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ICML:
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category: Conferences
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occurrences:

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