Imagen de Portada

Training Time Prediction for Mixed Precision-based Distributed Training

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kang, Minchul, Shin, Changyong, Jeong, Jinwoo, Lee, Hyunho, Go, Younghun, Kim, Gyeongmin, Yang, Gyeongsik, Yoo, Chuck
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Performance
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

GPU Memory Prediction for Multimodal Model Training
por: Jeong, Jinwoo, et al.
Publicado: (2025)

LMDeploy Accelerates Mixed-Precision LLM Inference with TurboMind
por: Zhang, Li, et al.
Publicado: (2025)

RAPID-LLM: Resilience-Aware Performance analysis of Infrastructure for Distributed LLM Training and Inference
por: Karfakis, George, et al.
Publicado: (2025)

Modeling the Impact of Fiber Latency on Compute-Communication Overlap in Geo-Distributed Multi-Datacenter AI Training
por: Papavasileiou, Ioannis, et al.
Publicado: (2026)

Fast and Scalable Mixed Precision Euclidean Distance Calculations Using GPU Tensor Cores
por: Curless, Brian, et al.
Publicado: (2025)

Fault-Tolerant Hybrid-Parallel Training at Scale with Reliable and Efficient In-memory Checkpointing
por: Wang, Yuxin, et al.
Publicado: (2023)

Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations
por: Jung, Seungwoo, et al.
Publicado: (2025)

Scaling Large-scale GNN Training to Thousands of Processors on CPU-based Supercomputers
por: Zhuang, Chen, et al.
Publicado: (2024)

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

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment
por: Vatsavai, Sairam Sri, et al.
Publicado: (2025)

MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs
por: Sarkar, Aishwarya, et al.
Publicado: (2024)

Unleashing the Power of Preemptive Priority-based Scheduling for Real-Time GPU Tasks
por: Wang, Yidi, et al.
Publicado: (2024)

FalconFS: Distributed File System for Large-Scale Deep Learning Pipeline
por: Xu, Jingwei, et al.
Publicado: (2025)

A Pilot Study on Tunable Precision Emulation via Automatic BLAS Offloading
por: Liu, Hang, et al.
Publicado: (2025)

On Orchestrating Parallel Broadcasts for Distributed Ledgers
por: Sheng, Peiyao, et al.
Publicado: (2024)

An Online Probabilistic Distributed Tracing System
por: Toslali, M., et al.
Publicado: (2024)

Towards a Peer-to-Peer Data Distribution Layer for Efficient and Collaborative Resource Optimization of Distributed Dataflow Applications
por: Scheinert, Dominik, et al.
Publicado: (2023)

Kubernetes in Action: Exploring the Performance of Kubernetes Distributions in the Cloud
por: Aqasizade, Hossein, et al.
Publicado: (2024)

Ridgeline: A 2D Roofline Model for Distributed Systems
por: Checconi, Fabio, et al.
Publicado: (2022)

Cache Blocking of Distributed-Memory Parallel Matrix Power Kernels
por: Lacey, Dane C., et al.
Publicado: (2024)

SHIRO: Near-Optimal Communication Strategies for Distributed Sparse Matrix Multiplication
por: Zhuang, Chen, et al.
Publicado: (2025)

Automated Calibration of Parallel and Distributed Computing Simulators: A Case Study
por: McDonald, Jesse, et al.
Publicado: (2024)

Towards a Scalable and Efficient PGAS-based Distributed OpenMP
por: Shan, Baodi, et al.
Publicado: (2024)

QoSFlow: Ensuring Service Quality of Distributed Workflows Using Interpretable Sensitivity Models
por: Rashid, Md Hasanur, et al.
Publicado: (2026)

Fine-Grained Energy Prediction For Parallellized LLM Inference With PIE-P
por: Dutt, Anurag, et al.
Publicado: (2025)

EfiMon: A Process Analyser for Granular Power Consumption Prediction
por: León-Vega, Luis G., et al.
Publicado: (2024)

Taming Cold Starts: Proactive Serverless Scheduling with Model Predictive Control
por: Nguyen, Chanh, et al.
Publicado: (2025)

LLload: Simplifying Real-Time Job Monitoring for HPC Users
por: Byun, Chansup, et al.
Publicado: (2024)

A Multi-Port Concurrent Communication Model for handling Compute Intensive Tasks on Distributed Satellite System Constellations
por: Veeravalli, Bharadwaj
Publicado: (2026)

Vectorization of Gradient Boosting of Decision Trees Prediction in the CatBoost Library for RISC-V Processors
por: Kozinov, Evgeny, et al.
Publicado: (2024)

Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms
por: Lin, Zhongyi, et al.
Publicado: (2024)

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference
por: Jeong, Bodon, et al.
Publicado: (2026)

BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems
por: Wang, Yuxin, et al.
Publicado: (2024)

AutoSP: Unlocking Long-Context LLM Training Via Compiler-Based Sequence Parallelism
por: Gupta, Ahan, et al.
Publicado: (2026)

xMem: A CPU-Based Approach for Accurate Estimation of GPU Memory in Deep Learning Training Workloads
por: Shi, Jiabo, et al.
Publicado: (2025)

TrainMover: An Interruption-Resilient Runtime for ML Training
por: Lao, ChonLam, et al.
Publicado: (2024)

ProTrain: Efficient LLM Training via Memory-Aware Techniques
por: Yang, Hanmei, et al.
Publicado: (2024)

Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs
por: Cornelius, Melanie, et al.
Publicado: (2025)

Profiling and optimization of multi-card GPU machine learning jobs
por: Lawenda, Marcin, et al.
Publicado: (2025)

Optimal Parallel Scheduling under Concave Speedup Functions
por: Li, Chengzhang, et al.
Publicado: (2025)