Cover Image

GPU Cluster Scheduling for Network-Sensitive Deep Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sharma, Aakash, Bhasi, Vivek M., Singh, Sonali, Kesidis, George, Kandemir, Mahmut T., Das, Chita R.
Format:	Preprint
Published:	2024
Subjects:	Performance 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

The Energy Cost of Execution-Idle in GPU Clusters
by: Lei, Yiran, et al.
Published: (2026)

Optimizing Attention on GPUs by Exploiting GPU Architectural NUMA Effects
by: Choudhary, Mansi, et al.
Published: (2025)

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

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

On the Partitioning of GPU Power among Multi-Instances
by: Vamja, Tirth, et al.
Published: (2025)

Minos: Systematically Classifying Performance and Power Characteristics of GPU Workloads on HPC Clusters
by: Jain, Rutwik, et al.
Published: (2026)

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

Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers
by: Maczan, Jędrzej
Published: (2026)

Toward Smart Scheduling in Tapis
by: Stubbs, Joe, et al.
Published: (2024)

LLMPerf: GPU Performance Modeling meets Large Language Models
by: Nguyen, Khoi N. M., et al.
Published: (2025)

When Less is More: Achieving Faster Convergence in Distributed Edge Machine Learning
by: Basani, Advik Raj, et al.
Published: (2024)

Accelerating Mobile Inference through Fine-Grained CPU-GPU Co-Execution
by: Li, Zhuojin, et al.
Published: (2025)

Scalable GPU Performance Variability Analysis framework
by: Lahiry, Ankur, et al.
Published: (2025)

A Practical Two-Stage Framework for GPU Resource and Power Prediction in Heterogeneous HPC Systems
by: Oztop, Beste, et al.
Published: (2026)

Disaggregated Design for GPU-Based Volumetric Data Structures
by: Meneghin, Massimiliano, et al.
Published: (2025)

Taking GPU Programming Models to Task for Performance Portability
by: Davis, Joshua H., et al.
Published: (2024)

Is Intelligence the Right Direction in New OS Scheduling for Multiple Resources in Cloud Environments?
by: Dou, Xinglei, et al.
Published: (2025)

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

CUTHERMO: Understanding GPU Memory Inefficiencies with Heat Map Profiling
by: Zhao, Yanbo, et al.
Published: (2025)

KEET: Explaining Performance of GPU Kernels Using LLM Agents
by: Davis, Joshua H., et al.
Published: (2026)

Hiku: Pull-Based Scheduling for Serverless Computing
by: Akbari, Saman, et al.
Published: (2025)

High-Performance Portable GPU Primitives for Arbitrary Types and Operators in Julia
by: Pilliat, Emmanuel
Published: (2026)

Efficient allocation of image recognition and LLM tasks on multi-GPU system
by: Lawenda, Marcin, et al.
Published: (2025)

Data-Driven Analysis to Understand GPU Hardware Resource Usage of Optimizations
by: Islam, Tanzima Z., et al.
Published: (2024)

Efficient GPU-Centered Singular Value Decomposition Using the Divide-and-Conquer Method
by: Liu, Shifang, et al.
Published: (2025)

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

Asymptotically Optimal Scheduling of Multiple Parallelizable Job Classes
by: Berg, Benjamin, et al.
Published: (2024)

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs
by: Wahlgren, Jacob, et al.
Published: (2025)

LEO: Tracing GPU Stall Root Causes via Cross-Vendor Backward Slicing
by: Xia, Yuning, et al.
Published: (2026)

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

HybridGen: Efficient LLM Generative Inference via CPU-GPU Hybrid Computing
by: Lin, Mao, et al.
Published: (2026)

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

Operational Strategies for Non-Disruptive Scheduling Transitions in Production HPC Systems
by: MacLachlan, Glen, et al.
Published: (2026)

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

DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction
by: Mumenin, Khondoker Mirazul, et al.
Published: (2025)

Towards Portability at Scale: A Cross-Architecture Performance Evaluation of a GPU-enabled Shallow Water Solver
by: Villalobos, Johansell, et al.
Published: (2025)

CASA: A Framework for SLO and Carbon-Aware Autoscaling and Scheduling in Serverless Cloud Computing
by: Qi, S., et al.
Published: (2024)

Flowshop Machine Scheduling: Markov Modeling, Optimal Schedules and Heuristics
by: Ghanem, Samah A. M.
Published: (2025)

Performance Debugging through Microarchitectural Sensitivity and Causality Analysis
by: Dutilleul, Alban, et al.
Published: (2024)

The SAP Cloud Infrastructure Dataset: A Reality Check of Scheduling and Placement of VMs in Cloud Computing
by: Uhlig, Arno, et al.
Published: (2025)