PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning | Paper Citation Record

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This paper cites Lamda: Language models for dialog applications,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Lamda: Language models for dialog applications,

Reference 1

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This paper cites Software- defined network assimilation: bridging the last mile towards centralized network configuration management with nassim,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Software- defined network assimilation: bridging the last mile towards centralized network configuration management with nassim,

Reference 2

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This paper cites Netllm: Adapting large language models for networking,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Netllm: Adapting large language models for networking,

Reference 3

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This paper cites Llama: Open and efficient foundation language models,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Llama: Open and efficient foundation language models,

Reference 4

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This paper cites Language models are few-shot learners,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Language models are few-shot learners,

Reference 5

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 6

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This paper cites You only look once: Unified, real-time object detection,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning You only look once: Unified, real-time object detection,

Reference 7

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This paper cites Accelerating model training in multi-cluster environments with consumer-grade gpus,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Accelerating model training in multi-cluster environments with consumer-grade gpus,

Reference 8

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This paper cites Crux: Gpu-efficient communication scheduling for deep learning training,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Crux: Gpu-efficient communication scheduling for deep learning training,

Reference 9

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

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning MegaScale: Scaling large language model training to more than 10,000 GPUs,

Reference 10

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This paper cites Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression

Reference 11

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This paper cites Optimal and Near-Optimal Adaptive Vector Quantization.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Optimal and Near-Optimal Adaptive Vector Quantization

Reference 12

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This paper cites Terngrad: Ternary gradients to reduce communication in distributed deep learning,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Terngrad: Ternary gradients to reduce communication in distributed deep learning,

Reference 13

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This paper cites THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression

Reference 14

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This paper cites Sparse communication for distributed gradient descent,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Sparse communication for distributed gradient descent,

Reference 15

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This paper cites Deep gradient compression: Reducing the communication bandwidth for distributed training,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Deep gradient compression: Reducing the communication bandwidth for distributed training,

Reference 16

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This paper cites PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

Reference 17

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This paper cites Grace: A compressed communication framework for distributed machine learning,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Grace: A compressed communication framework for distributed machine learning,

Reference 18

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This paper cites Efficient sparse collective communication and its application to accelerate distributed deep learning,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Efficient sparse collective communication and its application to accelerate distributed deep learning,

Reference 19

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This paper cites Empowering Distributed Training with Sparsity-driven Data Synchronization.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Empowering Distributed Training with Sparsity-driven Data Synchronization

Reference 20

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This paper cites Embrace: Accelerating sparse communication for distributed training of deep neural networks,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Embrace: Accelerating sparse communication for distributed training of deep neural networks,

Reference 21

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This paper cites Hi-speed dnn training with espresso: Unleashing the full potential of gradient compression with near-optimal usage strategies,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Hi-speed dnn training with espresso: Unleashing the full potential of gradient compression with near-optimal usage strategies,

Reference 22

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This paper cites Mccs: A service-based approach to collective communication for multi- tenant cloud,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Mccs: A service-based approach to collective communication for multi- tenant cloud,

Reference 23

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This paper cites Swing: Short- cutting rings for higher bandwidth allreduce,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Swing: Short- cutting rings for higher bandwidth allreduce,

Reference 24

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This paper cites Scaling distributed machine learning with the parameter server,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Scaling distributed machine learning with the parameter server,

Reference 25

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This paper cites Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation,

Reference 26

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This paper cites Learning multiple layers of features from tiny images,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Learning multiple layers of features from tiny images,

Reference 27

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 28

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PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Deep Residual Learning for Image Recognition

Reference 29

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This paper cites Learning both weights and connections for efficient neural networks,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Learning both weights and connections for efficient neural networks,

Reference 30

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This paper cites Pruning filters for efficient convnets,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Pruning filters for efficient convnets,

Reference 31

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This paper cites Pruning convolutional neural networks for resource efficient inference,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Pruning convolutional neural networks for resource efficient inference,

Reference 32

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This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning The lottery ticket hypothesis: Finding sparse, trainable neural networks,

Reference 33

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This paper cites Earlybert: Efficient bert training via early-bird lottery tickets,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Earlybert: Efficient bert training via early-bird lottery tickets,

Reference 34

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This paper cites When to prune? a policy towards early structural pruning,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning When to prune? a policy towards early structural pruning,

Reference 35

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This paper cites Similarity of Neural Network Representations Revisited.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Similarity of Neural Network Representations Revisited

Reference 36

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This paper cites Model Sparsity Can Simplify Machine Unlearning.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Model Sparsity Can Simplify Machine Unlearning

Reference 37

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This paper cites DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training

Reference 38

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This paper cites FedMef: Towards Memory-efficient Federated Dynamic Pruning.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning FedMef: Towards Memory-efficient Federated Dynamic Pruning

Reference 39

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This paper cites ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

Reference 40

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This paper cites Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better

Reference 41

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This paper cites Distributed Pruning Towards Tiny Neural Networks in Federated Learning.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Distributed Pruning Towards Tiny Neural Networks in Federated Learning

Reference 42

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This paper cites ImageNet Large Scale Visual Recognition Challenge.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning ImageNet Large Scale Visual Recognition Challenge

Reference 43

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This paper cites Tiny imagenet visual recognition challenge,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Tiny imagenet visual recognition challenge,

Reference 44

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This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 45

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This paper cites Nvidia collective communications library (nccl),.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Nvidia collective communications library (nccl),

Reference 46

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This paper cites Available: https://api.semanticscholar.org/CorpusID: 16664790.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Available: https://api.semanticscholar.org/CorpusID: 16664790

Reference 2015

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Observation dd5850b4-e66a-4141-8a24-ed4516cc1ca6 · outbound

This paper cites TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning

Reference 2017

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This paper cites LaMDA: Language Models for Dialog Applications.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning LaMDA: Language Models for Dialog Applications

Reference 2022

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