Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers | Paper Citation Record

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This paper cites Super: Sub-graph parallelism for transformers,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Super: Sub-graph parallelism for transformers,

Reference 1

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This paper cites Scaling distributed deep learning work- loads beyond the memory capacity with karma,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Scaling distributed deep learning work- loads beyond the memory capacity with karma,

Reference 2

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This paper cites Forge: Pre-training open foundation models for science,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Forge: Pre-training open foundation models for science,

Reference 3

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This paper cites Optimizing distributed training on frontier for large language models,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Optimizing distributed training on frontier for large language models,

Reference 4

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This paper cites Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model,

Reference 5

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This paper cites Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM

Reference 6

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This paper cites MegaScale: Scaling large language model training to more than 10,000 GPUs,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers MegaScale: Scaling large language model training to more than 10,000 GPUs,

Reference 7

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This paper cites Google cloud demonstrates the world’s largest distributed training job for large language models across 50000+ tpu v5e chips,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Google cloud demonstrates the world’s largest distributed training job for large language models across 50000+ tpu v5e chips,

Reference 8

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This paper cites AxoNN: An asynchronous, message-driven parallel framework for extreme-scale deep learning,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers AxoNN: An asynchronous, message-driven parallel framework for extreme-scale deep learning,

Reference 9

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This paper cites Exploiting sparsity in pruned neural networks to optimize large model training,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Exploiting sparsity in pruned neural networks to optimize large model training,

Reference 10

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

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Zero: Memory optimizations toward training trillion parameter models,

Reference 11

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This paper cites Pytorch fsdp: Experiences on scaling fully sharded data parallel,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Pytorch fsdp: Experiences on scaling fully sharded data parallel,

Reference 12

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This paper cites Megatron-lm: Training multi-billion parameter language models using model parallelism,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Megatron-lm: Training multi-billion parameter language models using model parallelism,

Reference 14

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This paper cites GPipe: efficient training of giant neural networks using pipeline parallelism,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers GPipe: efficient training of giant neural networks using pipeline parallelism,

Reference 15

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This paper cites Deepspeed: Extreme-scale model training for everyone,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Deepspeed: Extreme-scale model training for everyone,

Reference 16

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

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers A hybrid tensor-expert-data parallelism approach to optimize mixture-of-experts training,

Reference 17

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This paper cites GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch,

Reference 18

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This paper cites Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning

Reference 19

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Colossal-AI: a unified deep learning system for large-scale parallel training,

Reference 20

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Llama 2: Open foundation and fine-tuned chat models,

Reference 21

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This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 22

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers LBANN: livermore big artificial neural network HPC toolkit,

Reference 23

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Nvidia selene supercomputer,

Reference 24

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This paper cites Frontier: Exploring exascale,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Frontier: Exploring exascale,

Reference 25

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This paper cites A three-dimensional approach to parallel matrix multiplication,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers A three-dimensional approach to parallel matrix multiplication,

Reference 26

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This paper cites ZeRO++: Extremely efficient collective communication for large model training,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers ZeRO++: Extremely efficient collective communication for large model training,

Reference 27

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Improving the performance of collective operations in mpich,

Reference 28

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Optimization of collective reduction operations,

Reference 29

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Improving communication performance in dense linear algebra via topology aware collectives,

Reference 30

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Mapping applications with collectives over sub-communicators on torus networks,

Reference 31

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers RAHTM: Routing- algorithm aware hierarchical task mapping,

Reference 32

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This paper cites Optimizing the performance of parallel applications on a 5D torus via task mapping,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Optimizing the performance of parallel applications on a 5D torus via task mapping,

Reference 33

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Supervised learning based algorithm selection for deep neural networks,

Reference 34

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Language Models are Few-Shot Learners

Reference 35

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This paper cites Attention Is All You Need.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Attention Is All You Need

Reference 36

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Bigscience large open-science open-access multilingual language model,

Reference 37

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Language models are unsupervised multitask learners,

Reference 38

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Training Deep Nets with Sublinear Memory Cost

Reference 39

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers A Study of BFLOAT16 for Deep Learning Training

Reference 40

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Unresolved cited work

Reference 41

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This paper cites Interactive investigation of traffic congestion on fat-tree networks using TreeScope,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Interactive investigation of traffic congestion on fat-tree networks using TreeScope,

Reference 42

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This paper cites Quantifying I/O and communication traffic interference on dragonfly networks equipped with burst buffers,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Quantifying I/O and communication traffic interference on dragonfly networks equipped with burst buffers,

Reference 43

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Quantifying memorization across neural language models,

Reference 44

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This paper cites The times sues openai and microsoft over ai use of copyrighted work,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers The times sues openai and microsoft over ai use of copyrighted work,

Reference 45

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Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Extracting training data from large language models,

Reference 46

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This paper cites Pythia: A suite for analyzing large language models across training and scaling,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Pythia: A suite for analyzing large language models across training and scaling,

Reference 47

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This paper cites Tinyllama: An open-source small language model,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Tinyllama: An open-source small language model,

Reference 48

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This paper cites The llama 3 herd of models,.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers The llama 3 herd of models,

Reference 49

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This paper cites Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs.

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs

Reference 50

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