:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Fan, Yangxin, Wieser, Raymond, Bruckman, Laura, French, Roger, Wu, Yinghui
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Accesso online:	https://arxiv.org/abs/2402.08470
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction
di: Kralj, Ivan, et al.
Pubblicazione: (2024)

Collaborative Split Federated Learning with Parallel Training and Aggregation
di: Papageorgiou, Yiannis, et al.
Pubblicazione: (2025)

STAlloc: Enhancing Memory Efficiency in Large-Scale Model Training with Spatio-Temporal Planning
di: Huang, Zixiao, et al.
Pubblicazione: (2025)

Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization
di: Wu, Ruilong, et al.
Pubblicazione: (2025)

Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI
di: Jin, Jianli, et al.
Pubblicazione: (2025)

A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid
di: Böckling, Martin, et al.
Pubblicazione: (2024)

Training Through Failure: Effects of Data Consistency in Parallel Machine Learning Training
di: Cao, Ray, et al.
Pubblicazione: (2024)

Placement Semantics for Distributed Deep Learning: A Systematic Framework for Analyzing Parallelism Strategies
di: Mehta, Deep Pankajbhai
Pubblicazione: (2026)

GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
di: Jeon, Byungsoo, et al.
Pubblicazione: (2024)

Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems
di: Yang, Haowei, et al.
Pubblicazione: (2025)

AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
di: Gong, Zhaoting, et al.
Pubblicazione: (2026)

Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
di: Lin, Jun-Liang, et al.
Pubblicazione: (2026)

FreeRide: Harvesting Bubbles in Pipeline Parallelism
di: Zhang, Jiashu, et al.
Pubblicazione: (2024)

iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems
di: Hu, Yi-Xiang, et al.
Pubblicazione: (2026)

Can Large Language Models Write Parallel Code?
di: Nichols, Daniel, et al.
Pubblicazione: (2024)

TimelyFreeze: Adaptive Parameter Freezing Mechanism for Pipeline Parallelism
di: Cho, Seonghye, et al.
Pubblicazione: (2026)

Opara: Exploiting Operator Parallelism for Expediting DNN Inference on GPUs
di: Chen, Aodong, et al.
Pubblicazione: (2023)

KORAL: Knowledge Graph Guided LLM Reasoning for SSD Operational Analysis
di: Akewar, Mayur, et al.
Pubblicazione: (2026)

SimpleFSDP: Simpler Fully Sharded Data Parallel with torch.compile
di: Zhang, Ruisi, et al.
Pubblicazione: (2024)

HPC-Coder: Modeling Parallel Programs using Large Language Models
di: Nichols, Daniel, et al.
Pubblicazione: (2023)

Para-B&B: Load-Balanced Deterministic Parallelization of Solving MIP
di: Zhang, Jinyu, et al.
Pubblicazione: (2026)

Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
di: Wu, Xiabao, et al.
Pubblicazione: (2025)

xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism
di: Fang, Jiarui, et al.
Pubblicazione: (2024)

AdaPtis: Reducing Pipeline Bubbles with Adaptive Pipeline Parallelism on Heterogeneous Models
di: Guo, Jihu, et al.
Pubblicazione: (2025)

Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
di: Pan, Xinglin, et al.
Pubblicazione: (2025)

Using Sequential Runtime Distributions for the Parallel Speedup Prediction of SAT Local Search
di: Arbelaez, Alejandro, et al.
Pubblicazione: (2024)

Astra: Efficient and Money-saving Automatic Parallel Strategies Search on Heterogeneous GPUs
di: Wang, Peiran, et al.
Pubblicazione: (2025)

SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference
di: Xie, Jincheng, et al.
Pubblicazione: (2026)

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training
di: Zhao, Juntao, et al.
Pubblicazione: (2025)

Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems
di: Mokadem, Riad, et al.
Pubblicazione: (2024)

PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale
di: Luqman, Alka, et al.
Pubblicazione: (2024)

HybridEP: Scaling Expert Parallelism to Cross-Datacenter Scenario via Hybrid Expert/Data Transmission
di: Yang, Weihao, et al.
Pubblicazione: (2025)

Accelerating Long-Tail Generation in Synchronous RLHF Training via Adaptive Tensor Parallelism
di: Zhao, Long, et al.
Pubblicazione: (2026)

SpecBranch: Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch Parallelism
di: Shen, Yuhao, et al.
Pubblicazione: (2025)

Helix Parallelism: Rethinking Sharding Strategies for Interactive Multi-Million-Token LLM Decoding
di: Bhatia, Nidhi, et al.
Pubblicazione: (2025)

TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training
di: Liu, Man, et al.
Pubblicazione: (2026)

DWDP: Distributed Weight Data Parallelism for High-Performance LLM Inference on NVL72
di: Li, Wanqian, et al.
Pubblicazione: (2026)

Parallel Split Learning with Global Sampling
di: Kohankhaki, Mohammad, et al.
Pubblicazione: (2024)

Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey
di: Liang, Feng, et al.
Pubblicazione: (2024)

A Parallel CPU-GPU Framework for Batching Heuristic Operations in Depth-First Heuristic Search
di: Futuhi, Ehsan, et al.
Pubblicazione: (2025)