Cover Image

BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ruschel, Raphael, Iftekhar, A. S. M., Manjunath, B. S., You, Suya
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction
by: Zhang, Shuai, et al.
Published: (2024)

Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks
by: Waleffe, Roger, et al.
Published: (2025)

FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
by: Chen, Yuanyuan, et al.
Published: (2022)

Fully Distributed Online Training of Graph Neural Networks in Networked Systems
by: Olshevskyi, Rostyslav, et al.
Published: (2024)

ReInc: Scaling Training of Dynamic Graph Neural Networks
by: Guan, Mingyu, et al.
Published: (2025)

Distributed Convolutional Neural Network Training on Mobile and Edge Clusters
by: Rama, Pranav, et al.
Published: (2024)

HelixPipe: Efficient Distributed Training of Long Sequence Transformers with Attention Parallel Pipeline Parallelism
by: Zhang, Geng, et al.
Published: (2025)

An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
by: Merkel, Nikolai, et al.
Published: (2023)

Grappa: Gradient-Only Communication for Scalable Graph Neural Network Training
by: Xu, Chongyang, et al.
Published: (2026)

TensAIR: Real-Time Training of Neural Networks from Data-streams
by: Tosi, Mauro D. L., et al.
Published: (2022)

SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks
by: Huang, Xin, et al.
Published: (2024)

MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing
by: Nouaji, Rahma, et al.
Published: (2025)

GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
by: Polisetty, Sandeep, et al.
Published: (2023)

Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
by: Wang, Shuai, et al.
Published: (2025)

Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch
by: Bajaj, Saurabh, et al.
Published: (2024)

GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU
by: Yu, Zhongming, et al.
Published: (2024)

Enhancing Physics-Informed Neural Networks with a Hybrid Parallel Kolmogorov-Arnold and MLP Architecture
by: Xu, Zuyu, et al.
Published: (2025)

A Parallel Alternative for Energy-Efficient Neural Network Training and Inferencing
by: Seal, Sudip K., et al.
Published: (2025)

Distributed Matrix-Based Sampling for Graph Neural Network Training
by: Tripathy, Alok, et al.
Published: (2023)

PaSE: Parallelization Strategies for Efficient DNN Training
by: Elango, Venmugil
Published: (2024)

A Scalable Digital Twin Framework for Energy Optimization in Data Centers
by: Gonçalves, Raphael Hendrigo de Souza, et al.
Published: (2026)

Understanding Silent Data Corruption in LLM Training
by: Ma, Jeffrey, et al.
Published: (2025)

Communication and Computation Efficient Split Federated Learning in O-RAN
by: Gu, Shunxian, et al.
Published: (2025)

Efficient Parallelization Layouts for Large-Scale Distributed Model Training
by: Hagemann, Johannes, et al.
Published: (2023)

DHP: Efficient Scaling of MLLM Training with Dynamic Hybrid Parallelism
by: Niu, Yifan, et al.
Published: (2026)

Empowering Distributed Training with Sparsity-driven Data Synchronization
by: Wang, Zhuang, et al.
Published: (2023)

Cooperative Minibatching in Graph Neural Networks
by: Balin, Muhammed Fatih, et al.
Published: (2023)

Graph Neural Networks Gone Hogwild
by: Solodova, Olga, et al.
Published: (2024)

Federated Learning on Stochastic Neural Networks
by: Tang, Jingqiao, et al.
Published: (2025)

SparDL: Distributed Deep Learning Training with Efficient Sparse Communication
by: Zhao, Minjun, et al.
Published: (2023)

Efficient Long-context Language Model Training by Core Attention Disaggregation
by: Zhuang, Yonghao, et al.
Published: (2025)

MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline
by: Sheng, Guangming, et al.
Published: (2024)

Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules
by: Pan, Xinglin, et al.
Published: (2024)

Hybrid Dual-Batch and Cyclic Progressive Learning for Efficient Distributed Training
by: Lu, Kuan-Wei, et al.
Published: (2025)

Designing Large Foundation Models for Efficient Training and Inference: A Survey
by: Liu, Dong, et al.
Published: (2024)

ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism
by: Liu, Zedong, et al.
Published: (2025)

TensorSocket: Shared Data Loading for Deep Learning Training
by: Robroek, Ties, et al.
Published: (2024)

AsyncHZP: Hierarchical ZeRO Parallelism with Asynchronous Scheduling for Scalable LLM Training
by: Bai, Huawei, et al.
Published: (2025)

REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments
by: Desai, Humaid Ahmed, et al.
Published: (2023)

Communication-Efficient Distributed Training for Collaborative Flat Optima Recovery in Deep Learning
by: Dimlioglu, Tolga, et al.
Published: (2025)