Copertina

When GPUs Fail Quietly: Observability-Aware Early Warning Beyond Numeric Telemetry

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Bidollahkhani, Michael, Nordsiek, Freja, Kunkel, Julian M.
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Distributed, Parallel, and Cluster Computing Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Communication-Avoiding Linear Algebraic Kernel K-Means on GPUs
di: Bellavita, Julian, et al.
Pubblicazione: (2026)

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

GOGH: Correlation-Guided Orchestration of GPUs in Heterogeneous Clusters
di: Raeisi, Ahmad, et al.
Pubblicazione: (2025)

LLMQ: Efficient Lower-Precision Pretraining for Consumer GPUs
di: Schultheis, Erik, et al.
Pubblicazione: (2025)

Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
di: Hu, Tiancheng, et al.
Pubblicazione: (2026)

Scaling State-Space Models on Multiple GPUs with Tensor Parallelism
di: Dutt, Anurag, et al.
Pubblicazione: (2026)

AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
di: Akinwande, Victor, et al.
Pubblicazione: (2024)

Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels
di: Kim, Taehyeon, et al.
Pubblicazione: (2024)

HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs
di: Wu, Yongji, et al.
Pubblicazione: (2025)

AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
di: Yan, Ran, et al.
Pubblicazione: (2025)

FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion
di: Chang, Li-Wen, et al.
Pubblicazione: (2024)

PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
di: Arfeen, Daiyaan, et al.
Pubblicazione: (2024)

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs
di: Li, Kaiwei, et al.
Pubblicazione: (2016)

FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs
di: Zhou, Yuanchang, et al.
Pubblicazione: (2024)

MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
di: Jiang, Ziheng, et al.
Pubblicazione: (2024)

SkipPredict: When to Invest in Predictions for Scheduling
di: Shahout, Rana, et al.
Pubblicazione: (2024)

DECICE: AI-Driven Scheduling and Digital Twin Integration for the Cloud-HPC-Edge Compute Continuum
di: Sharma, Aasish Kumar, et al.
Pubblicazione: (2026)

A Review of Tools and Techniques for Optimization of Workload Mapping and Scheduling in Heterogeneous HPC System
di: Sharma, Aasish Kumar, et al.
Pubblicazione: (2025)

GrapheonRL: A Graph Neural Network and Reinforcement Learning Framework for Constraint and Data-Aware Workflow Mapping and Scheduling in Heterogeneous HPC Systems
di: Sharma, Aasish Kumar, et al.
Pubblicazione: (2025)

Peer-to-Peer Deep Learning for Beyond-5G IoT
di: Pranav, Srinivasa, et al.
Pubblicazione: (2023)

SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization
di: Gül, Baran Can, et al.
Pubblicazione: (2025)

Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetry
di: Chen, Ziji, et al.
Pubblicazione: (2025)

Efficient Training on Multiple Consumer GPUs with RoundPipe
di: Luo, Yibin, et al.
Pubblicazione: (2026)

Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference
di: Zhang, Haolin, et al.
Pubblicazione: (2025)

Beyond Efficiency: Scaling AI Sustainably
di: Wu, Carole-Jean, et al.
Pubblicazione: (2024)

Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical
di: Behera, Adarsh Prasad, et al.
Pubblicazione: (2024)

TP-Aware Dequantization
di: Hoque, Adnan, et al.
Pubblicazione: (2024)

Dynamic Rebatching for Efficient Early-Exit Inference with DREX
di: Liu, Xuting, et al.
Pubblicazione: (2025)

Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow
di: Mei, Yixuan, et al.
Pubblicazione: (2024)

Uncertainty-Aware Decarbonization for Datacenters
di: Li, Amy, et al.
Pubblicazione: (2024)

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach
di: Saroliya, Urvij, et al.
Pubblicazione: (2024)

Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
di: Ma, Mengmeng, et al.
Pubblicazione: (2024)

Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving
di: Dai, Yinwei, et al.
Pubblicazione: (2023)

Dependency Aware Incident Linking in Large Cloud Systems
di: Ghosh, Supriyo, et al.
Pubblicazione: (2024)

Priority-Aware Model-Distributed Inference at Edge Networks
di: Li, Teng, et al.
Pubblicazione: (2024)

Drift-Aware Federated Learning: A Causal Perspective
di: Fang, Yunjie, et al.
Pubblicazione: (2025)

Energy-Aware Decentralized Learning with Intermittent Model Training
di: Dhasade, Akash, et al.
Pubblicazione: (2024)

Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
di: Balivada, Nihal, et al.
Pubblicazione: (2026)

Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
di: Yang, Mengwei, et al.
Pubblicazione: (2024)

Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning
di: Ryum, Keumseo, et al.
Pubblicazione: (2025)