Nsdi 23 Bgl Gpu Efficient

Media Summary: Yiran Lei, Carnegie Mellon University and MangoBoost; Dongjoo Lee, MangoBoost; Liangyu Zhao, University of Washington; ... RLBoost: Harvesting Preemptible Cloud Resources for Cost- Kai Zhang, Fudan University Network Function Virtualization (NFV) virtualizes software network functions to offer flexibility in their ...

Nsdi 23 Bgl Gpu Efficient - Detailed Analysis & Overview

Yiran Lei, Carnegie Mellon University and MangoBoost; Dongjoo Lee, MangoBoost; Liangyu Zhao, University of Washington; ... RLBoost: Harvesting Preemptible Cloud Resources for Cost- Kai Zhang, Fudan University Network Function Virtualization (NFV) virtualizes software network functions to offer flexibility in their ... FLARE: Anomaly Diagnostics for Divergent LLM Training in

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NSDI '23 - BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing

NSDI '23 - Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

NSDI '23-Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning

NSDI '23 - ARK: GPU-driven Code Execution for Distributed Deep Learning

NSDI '26 - FAST: An Efficient Scheduler for All-to-All GPU Communication

NSDI '23 - Transparent GPU Sharing in Container Clouds for Deep Learning Workloads

USENIX ATC '23 - TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs

NSDI '23 - Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge

NSDI '26 - RLBoost: Harvesting Preemptible Cloud Resources for Cost-Efficient Reinforcement Learning

NSDI '18 - G-NET: Effective GPU Sharing in NFV Systems

NSDI '25 - GPU-Disaggregated Serving for Deep Learning Recommendation Models at Scale

NSDI '26 - FLARE: Anomaly Diagnostics for Divergent LLM Training in GPU Clusters of Thousand-Plus...

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NSDI '23 - BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing

NSDI '23 - BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing

BGL

NSDI '23 - Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

NSDI '23 - Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

Zeus: Understanding and Optimizing

NSDI '23-Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning

NSDI '23-Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning

Shockwave: Fair and

NSDI '23 - ARK: GPU-driven Code Execution for Distributed Deep Learning

NSDI '23 - ARK: GPU-driven Code Execution for Distributed Deep Learning

ARK:

NSDI '26 - FAST: An Efficient Scheduler for All-to-All GPU Communication

NSDI '26 - FAST: An Efficient Scheduler for All-to-All GPU Communication

Yiran Lei, Carnegie Mellon University and MangoBoost; Dongjoo Lee, MangoBoost; Liangyu Zhao, University of Washington; ...

NSDI '23 - Transparent GPU Sharing in Container Clouds for Deep Learning Workloads

NSDI '23 - Transparent GPU Sharing in Container Clouds for Deep Learning Workloads

Transparent

USENIX ATC '23 - TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs

USENIX ATC '23 - TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs

USENIX ATC '

NSDI '23 - Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge

NSDI '23 - Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge

Gemel: Model Merging for Memory-

NSDI '26 - RLBoost: Harvesting Preemptible Cloud Resources for Cost-Efficient Reinforcement Learning

NSDI '26 - RLBoost: Harvesting Preemptible Cloud Resources for Cost-Efficient Reinforcement Learning

RLBoost: Harvesting Preemptible Cloud Resources for Cost-

NSDI '18 - G-NET: Effective GPU Sharing in NFV Systems

NSDI '18 - G-NET: Effective GPU Sharing in NFV Systems

Kai Zhang, Fudan University Network Function Virtualization (NFV) virtualizes software network functions to offer flexibility in their ...

NSDI '25 - GPU-Disaggregated Serving for Deep Learning Recommendation Models at Scale

NSDI '25 - GPU-Disaggregated Serving for Deep Learning Recommendation Models at Scale

GPU

NSDI '26 - FLARE: Anomaly Diagnostics for Divergent LLM Training in GPU Clusters of Thousand-Plus...

NSDI '26 - FLARE: Anomaly Diagnostics for Divergent LLM Training in GPU Clusters of Thousand-Plus...

FLARE: Anomaly Diagnostics for Divergent LLM Training in

OSDI '22 - Efficient and Scalable Graph Pattern Mining on GPUs

OSDI '22 - Efficient and Scalable Graph Pattern Mining on GPUs

OSDI '22 -