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Tofu

19 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Tofu is an intra-layer model parallel system that partitions very large DNN models across multiple GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow graph of fine-grained tensor operators used by platforms like MXNet and TensorFlow. To optimally partition different operators in a dataflow graph, Tofu uses a recursive search algorithm that minimizes the total communication cost.

Source: Supporting Very Large Models using Automatic Dataflow...

Papers archive 2025-07-28

19 shown of 19, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

18 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Machine Unlearning4
Text Generation2
3D Reconstruction1
Computational Efficiency1
Federated Learning1
GPU1
Image Generation1
Language Modeling1
Language Modelling1
Large Language Model1
Memorization1
Model Editing1
Recommendation Systems1
Reinforcement Learning1
Vocal Bursts Intensity Prediction1
World Knowledge1
graph partitioning1
reinforcement-learning1

Usage over time archive 2025-07-28

Papers per year tagged with Tofu: 2018 to 2025, peak 8 8 0 2018: 1 paper 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 1 paper 2021 2022: 1 paper 2022 2023: 2 papers 2023 2024: 8 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (19 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Distributed Methods

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