Cover Image

MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Nouaji, Rahma, Bitchebe, Stella, Macedo, Ricardo, Balmau, Oana
Format:	Preprint
Published:	2025
Subjects:	Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Accelerating MoE Model Inference with Expert Sharding
by: Balmau, Oana, et al.
Published: (2025)

Keigo: Co-designing Log-Structured Merge Key-Value Stores with a Non-Volatile, Concurrency-aware Storage Hierarchy (Extended Version)
by: Adão, Rúben, et al.
Published: (2025)

A Pragmatic Approach to Learned Indexing in RocksDB: Targeted Optimizations with Minimal System Modification
by: Vashisth, Shubham, et al.
Published: (2026)

Efficient Data Distribution Estimation for Accelerated Federated Learning
by: Wang, Yuanli, et al.
Published: (2024)

HarMoEny: Efficient Multi-GPU Inference of MoE Models
by: Doucet, Zachary, et al.
Published: (2025)

Training Machine Learning models at the Edge: A Survey
by: Khouas, Aymen Rayane, et al.
Published: (2024)

BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling
by: Ruschel, Raphael, et al.
Published: (2023)

Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression
by: Feng, Hao, et al.
Published: (2024)

INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning
by: Prime Intellect Team, et al.
Published: (2025)

HetCCL: Accelerating LLM Training with Heterogeneous GPUs
by: Kim, Heehoon, et al.
Published: (2026)

Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
by: Zhou, Yuanchang, et al.
Published: (2026)

Unlearning during Learning: An Efficient Federated Machine Unlearning Method
by: Gu, Hanlin, et al.
Published: (2024)

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

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

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

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

Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data
by: Amalanshu, Avi, et al.
Published: (2024)

TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation
by: Chai, Huichao, et al.
Published: (2026)

Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things
by: Wang, Ziheng, et al.
Published: (2024)

Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine
by: Chen, Canyi, et al.
Published: (2025)

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
by: Zhang, Jiaojiao, et al.
Published: (2025)

Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
by: Wang, Tianze, et al.
Published: (2025)

FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism
by: Wang, Yujie, et al.
Published: (2024)

Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator
by: Fujii, Kazuki, et al.
Published: (2024)

Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping
by: Jiang, Chenyu, et al.
Published: (2024)

Stress Monitoring in Healthcare: An Ensemble Machine Learning Framework Using Wearable Sensor Data
by: Sinhal, Arpana, et al.
Published: (2025)

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining
by: Jiang, Zhida, et al.
Published: (2026)

MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training
by: Yoon, Daegun, et al.
Published: (2023)

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
by: Luo, Shuqing, et al.
Published: (2025)

HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap
by: Lin, Wenxiang, et al.
Published: (2025)

Minder: Faulty Machine Detection for Large-scale Distributed Model Training
by: Deng, Yangtao, et al.
Published: (2024)

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

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

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

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

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

Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning
by: Yu, Chong, et al.
Published: (2024)

FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
by: Duan, Moming, et al.
Published: (2020)

Quasar: Quantized Self-Speculative Acceleration for Rapid Inference via Memory-Efficient Verification
by: Huang, Guang, et al.
Published: (2026)

Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
by: Guo, Kun, et al.
Published: (2025)