Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models | Paper Citation Record

Observation e36d548d-3952-47a7-a449-edec124c2c0a · outbound

This paper cites Gshard: Scaling giant models with conditional computation and automatic sharding,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Gshard: Scaling giant models with conditional computation and automatic sharding,

Reference 1

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This paper cites Glam: Efficient scaling of language models with mixture-of-experts,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Glam: Efficient scaling of language models with mixture-of-experts,

Reference 2

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This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 3

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This paper cites Deepspeed-moe: Advancing mixture-of- experts inference and training to power next-generation ai scale,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Deepspeed-moe: Advancing mixture-of- experts inference and training to power next-generation ai scale,

Reference 4

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This paper cites Tutel: Adaptive mixture-of-experts at scale,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Tutel: Adaptive mixture-of-experts at scale,

Reference 5

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This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 6

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This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,

Reference 7

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Observation 09976f2d-8bef-4494-8040-2d022e3473b2 · outbound

This paper cites Faster- moe: modeling and optimizing training of large-scale dynamic pre- trained models,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Faster- moe: modeling and optimizing training of large-scale dynamic pre- trained models,

Reference 8

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Observation c36279c3-9609-4d50-a18a-153a4be382b1 · outbound

This paper cites Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement,

Reference 9

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This paper cites Zero: Memory optimizations toward training trillion parameter models,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Zero: Memory optimizations toward training trillion parameter models,

Reference 10

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This paper cites Scaling Laws for Neural Language Models.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Scaling Laws for Neural Language Models

Reference 11

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 12

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 13

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This paper cites Xlnet: Generalized autoregressive pretraining for language understanding,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Xlnet: Generalized autoregressive pretraining for language understanding,

Reference 14

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 15

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Observation d145997b-c7d3-446b-80e6-fb8425bac0f5 · outbound

This paper cites Language models are unsupervised multitask learners,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Language models are unsupervised multitask learners,

Reference 16

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This paper cites Language mod- els are few-shot learners,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Language mod- els are few-shot learners,

Reference 17

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This paper cites Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 18

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This paper cites Mixture of A Million Experts.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Mixture of A Million Experts

Reference 19

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This paper cites Taming Sparsely Activated Transformer with Stochastic Experts.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Taming Sparsely Activated Transformer with Stochastic Experts

Reference 20

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Observation fc4f260d-5b54-4dd9-a9aa-07fe5e8455b5 · outbound

This paper cites Scaling laws for fine-grained mixture of experts,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Scaling laws for fine-grained mixture of experts,

Reference 21

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This paper cites Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 22

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This paper cites Go wider instead of deeper,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Go wider instead of deeper,

Reference 23

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This paper cites One Student Knows All Experts Know: From Sparse to Dense.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models One Student Knows All Experts Know: From Sparse to Dense

Reference 24

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This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 25

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This paper cites GPT-4 Technical Report.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models GPT-4 Technical Report

Reference 26

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This paper cites Stabilization of planar collective motion: All-to-all communication,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Stabilization of planar collective motion: All-to-all communication,

Reference 27

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Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Optimization of all-to-all communication on the blue gene/l supercomputer,

Reference 28

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This paper cites The hierarchical factor algorithm for all-to-all communication,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models The hierarchical factor algorithm for all-to-all communication,

Reference 29

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This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 30

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This paper cites HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System

Reference 31

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Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models FastMoE: A Fast Mixture-of-Expert Training System

Reference 32

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Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Moesys: A distributed and efficient mixture-of-experts training and inference system for internet services,

Reference 33

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This paper cites Accelerating distributed moe training and inference with lina,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Accelerating distributed moe training and inference with lina,

Reference 34

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This paper cites Janus: A unified distributed training framework for sparse mixture-of-experts models,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Janus: A unified distributed training framework for sparse mixture-of-experts models,

Reference 35

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This paper cites Amp: Automatically finding model parallel strategies with heterogeneity awareness,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Amp: Automatically finding model parallel strategies with heterogeneity awareness,

Reference 36

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Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Merak: An efficient distributed dnn training framework with automated 3d parallelism for giant foundation models,

Reference 37

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This paper cites Hippie: A data-paralleled pipeline approach to improve memory-efficiency and scalability for large dnn training,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Hippie: A data-paralleled pipeline approach to improve memory-efficiency and scalability for large dnn training,

Reference 38

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Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Colossal-ai: A unified deep learning system for large-scale parallel training,

Reference 39

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Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Piper: Mul- tidimensional planner for dnn parallelization,

Reference 40

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Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Parallel intelligent computing: development and challenges,

Reference 41

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Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Zero- infinity: Breaking the gpu memory wall for extreme scale deep learning,

Reference 42

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This paper cites Zero-offload: Democratizing billion-scale model training,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Zero-offload: Democratizing billion-scale model training,

Reference 43

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This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 44

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This paper cites MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

Reference 45

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This paper cites Maximizing Parallelism in Distributed Training for Huge Neural Networks.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Maximizing Parallelism in Distributed Training for Huge Neural Networks

Reference 46

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This paper cites Autopipe: A fast pipeline parallelism approach with balanced partitioning and micro- batch slicing,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Autopipe: A fast pipeline parallelism approach with balanced partitioning and micro- batch slicing,

Reference 47

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This paper cites Hph: Hybrid parallelism on heterogeneous clusters for accelerating large-scale dnns training,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Hph: Hybrid parallelism on heterogeneous clusters for accelerating large-scale dnns training,

Reference 48

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This paper cites Sequence Parallelism: Long Sequence Training from System Perspective.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Sequence Parallelism: Long Sequence Training from System Perspective

Reference 49

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This paper cites Reducing activation recomputation in large transformer models,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Reducing activation recomputation in large transformer models,

Reference 50

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This paper cites DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Reference 51

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This paper cites Ring Attention with Blockwise Transformers for Near-Infinite Context.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 52

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This paper cites Bagualu: targeting brain scale pretrained models with over 37 million cores,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Bagualu: targeting brain scale pretrained models with over 37 million cores,

Reference 53

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Observation 90b3f15f-2ac1-461d-a45f-6634f43a9305 · outbound

This paper cites Parm: Efficient training of large sparsely-activated models with dedicated schedules,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Parm: Efficient training of large sparsely-activated models with dedicated schedules,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:10:00.593618Z

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Observation 170fcd03-b062-4ae9-8564-7850af247d9e · outbound

This paper cites A hybrid tensor-expert-data parallelism approach to optimize mixture- of-experts training,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models A hybrid tensor-expert-data parallelism approach to optimize mixture- of-experts training,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T20:10:00.367178Z

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Observation 8b1933bb-90f3-4d74-878b-0bbc965268e7 · outbound

This paper cites Mg-wfbp: Efficient data communication for distributed synchronous sgd algorithms,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Mg-wfbp: Efficient data communication for distributed synchronous sgd algorithms,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:10:00.579108Z

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Observation 330a99b6-790c-430b-ace4-199aeb2e85b3 · outbound

This paper cites PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T20:10:00.372977Z

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Observation fe168bc5-30cb-4bf0-a885-1718896f5399 · outbound

This paper cites A multidimensional communication scheduling method for hybrid parallel dnn training,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models A multidimensional communication scheduling method for hybrid parallel dnn training,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:10:00.570645Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b1c76ec9-0a12-47b8-96bd-a305aff7edc3 · outbound

This paper cites Schemoe: An extensible mixture-of-experts distributed training system with tasks scheduling,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Schemoe: An extensible mixture-of-experts distributed training system with tasks scheduling,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T20:10:00.379017Z

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Observation 7b64b832-e524-4522-b65d-dd22fe32f106 · outbound

This paper cites Pipemoe: Accelerating mixture- of-experts through adaptive pipelining,.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Pipemoe: Accelerating mixture- of-experts through adaptive pipelining,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T20:10:00.382168Z

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