Peng Sun

Orcid: 0000-0001-8456-0491

Affiliations:

SenseTime Research, China
Shanghai AI Laboratory, Shanghai, China
Nanyang Technological University, Energy Research Institute, Interdisciplinary Graduate School, Singapore

According to our database¹, Peng Sun authored at least 44 papers between 2013 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

UniSched: A Unified Scheduler for Deep Learning Training Jobs With Different User Demands.

[BibT_eX]

[DOI]

IEEE Trans. Computers, June, 2024

Deep Learning Workload Scheduling in GPU Datacenters: A Survey.

[BibT_eX]

[DOI]

ACM Comput. Surv., June, 2024

Efficient Training of Large Language Models on Distributed Infrastructures: A Survey.

[BibT_eX]

[DOI]

CoRR, 2024

InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output.

[BibT_eX]

[DOI]

CoRR, 2024

LoongTrain: Efficient Training of Long-Sequence LLMs with Head-Context Parallelism.

[BibT_eX]

[DOI]

CoRR, 2024

InternLM2 Technical Report.

[BibT_eX]

[DOI]

et al.

CoRR, 2024

InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding.

[BibT_eX]

[DOI]

CoRR, 2024

FedDSE: Distribution-aware Sub-model Extraction for Federated Learning over Resource-constrained Devices.

[BibT_eX]

[DOI]

Proceedings of the ACM on Web Conference 2024, 2024

LoongServe: Efficiently Serving Long-Context Large Language Models with Elastic Sequence Parallelism.

[BibT_eX]

[DOI]

Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles, 2024

TorchGT: A Holistic System for Large-Scale Graph Transformer Training.

[BibT_eX]

[DOI]

Proceedings of the International Conference for High Performance Computing, 2024

dLoRA: Dynamically Orchestrating Requests and Adapters for LoRA LLM Serving.

[BibT_eX]

[DOI]

Proceedings of the 18th USENIX Symposium on Operating Systems Design and Implementation, 2024

Characterization of Large Language Model Development in the Datacenter.

[BibT_eX]

[DOI]

Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation, 2024

Lins: Reducing Communication Overhead of ZeRO for Efficient LLM Training.

[BibT_eX]

[DOI]

Proceedings of the 32nd IEEE/ACM International Symposium on Quality of Service, 2024

Ymir: A Scheduler for Foundation Model Fine-tuning Workloads in Datacenters.

[BibT_eX]

[DOI]

Proceedings of the 38th ACM International Conference on Supercomputing, 2024

AutoSched: An Adaptive Self-configured Framework for Scheduling Deep Learning Training Workloads.

[BibT_eX]

[DOI]

Proceedings of the 38th ACM International Conference on Supercomputing, 2024

Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Sylvie: 3D-Adaptive and Universal System for Large-Scale Graph Neural Network Training.

[BibT_eX]

[DOI]

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Centauri: Enabling Efficient Scheduling for Communication-Computation Overlap in Large Model Training via Communication Partitioning.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

2023

AMSP: Super-Scaling LLM Training via Advanced Model States Partitioning.

[BibT_eX]

[DOI]

CoRR, 2023

Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communication.

[BibT_eX]

[DOI]

CoRR, 2023

Hydro: Surrogate-Based Hyperparameter Tuning Service in Datacenters.

[BibT_eX]

[DOI]

Proceedings of the 17th USENIX Symposium on Operating Systems Design and Implementation, 2023

Lucid: A Non-intrusive, Scalable and Interpretable Scheduler for Deep Learning Training Jobs.

[BibT_eX]

[DOI]

Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2023

2022

Astraea: A Fair Deep Learning Scheduler for Multi-Tenant GPU Clusters.

[BibT_eX]

[DOI]

IEEE Trans. Parallel Distributed Syst., 2022

GradientFlow: Optimizing Network Performance for Large-Scale Distributed DNN Training.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, 2022

Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision.

[BibT_eX]

[DOI]

CoRR, 2022

A Simulation Platform for Multi-tenant Machine Learning Services on Thousands of GPUs.

[BibT_eX]

[DOI]

CoRR, 2022

Primo: Practical Learning-Augmented Systems with Interpretable Models.

[BibT_eX]

[DOI]

Proceedings of the 2022 USENIX Annual Technical Conference, 2022

Titan: a scheduler for foundation model fine-tuning workloads.

[BibT_eX]

[DOI]

Proceedings of the 13th Symposium on Cloud Computing, SoCC 2022, 2022

2021

ModelCI-e: Enabling Continual Learning in Deep Learning Serving Systems.

[BibT_eX]

[DOI]

CoRR, 2021

Characterization and prediction of deep learning workloads in large-scale GPU datacenters.

[BibT_eX]

[DOI]

Proceedings of the International Conference for High Performance Computing, 2021

Chronus: A Novel Deadline-aware Scheduler for Deep Learning Training Jobs.

[BibT_eX]

[DOI]

Proceedings of the SoCC '21: ACM Symposium on Cloud Computing, 2021

2020

GraphMP: I/O-Efficient Big Graph Analytics on a Single Commodity Machine.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, 2020

Elan: Towards Generic and Efficient Elastic Training for Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the 40th IEEE International Conference on Distributed Computing Systems, 2020

2019

Scalable Architectures for Big Data Analysis.

[BibT_eX]

[DOI]

Peng Sun

Yonggang Wen

Proceedings of the Encyclopedia of Big Data Technologies., 2019

Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes.

[BibT_eX]

[DOI]

CoRR, 2019

2018

MetaFlow: A Scalable Metadata Lookup Service for Distributed File Systems in Data Centers.

[BibT_eX]

[DOI]

IEEE Trans. Big Data, 2018

On Distributed Algorithms for Cost-Efficient Data Center Placement in Cloud Computing.

[BibT_eX]

[DOI]

CoRR, 2018

Speeding-Up Age Estimation in Intelligent Demographics System via Network Optimization.

[BibT_eX]

[DOI]

Zhenzhen Hu

Peng Sun

Yonggang Wen

Proceedings of the 2018 IEEE International Conference on Communications, 2018

2017

Towards Distributed Machine Learning in Shared Clusters: A Dynamically-Partitioned Approach.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Smart Computing, 2017

GraphMP: An Efficient Semi-External-Memory Big Graph Processing System on a Single Machine.

[BibT_eX]

[DOI]

Proceedings of the 23rd IEEE International Conference on Parallel and Distributed Systems, 2017

GraphH: High Performance Big Graph Analytics in Small Clusters.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Cluster Computing, 2017

2016

Timed Dataflow: Reducing Communication Overhead for Distributed Machine Learning Systems.

[BibT_eX]

[DOI]

Proceedings of the 22nd IEEE International Conference on Parallel and Distributed Systems, 2016

2014

CREATE: Correlation enhanced traffic matrix estimation in Data Center Networks.

[BibT_eX]

[DOI]

Proceedings of the 2014 IFIP Networking Conference, Trondheim, 2014

2013

Cloud3DView: an interactive tool for cloud data center operations.

[BibT_eX]

[DOI]

Proceedings of the ACM SIGCOMM 2013 Conference, 2013

Peng Sun

Timeline

Legend:

Links

Online presence:

On csauthors.net:

Bibliography

Loading...