Qizhen Weng

Orcid: 0000-0001-9195-6443

According to our database1, Qizhen Weng authored at least 14 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A New Representation of Universal Successor Features for Enhancing the Generalization of Target-Driven Visual Navigation.
IEEE Robotics Autom. Lett., November, 2024

Efficient Training of Large Language Models on Distributed Infrastructures: A Survey.
CoRR, 2024

InternLM2 Technical Report.
CoRR, 2024

CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference.
CoRR, 2024

Low Bitrate Codec with Joint Vector Quantization and Hoffman Coding for Internet of Things.
Proceedings of the 6th International Conference on Internet of Things, 2024

2023
Accelerating Distributed Learning in Non-Dedicated Environments.
IEEE Trans. Cloud Comput., 2023

Beware of Fragmentation: Scheduling GPU-Sharing Workloads with Fragmentation Gradient Descent.
Proceedings of the 2023 USENIX Annual Technical Conference, 2023

2022
MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters.
Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation, 2022

Workload consolidation in alibaba clusters: the good, the bad, and the ugly.
Proceedings of the 13th Symposium on Cloud Computing, SoCC 2022, 2022

2020
Metis: learning to schedule long-running applications in shared container clusters at scale.
Proceedings of the International Conference for High Performance Computing, 2020

Semi-dynamic load balancing: efficient distributed learning in non-dedicated environments.
Proceedings of the SoCC '20: ACM Symposium on Cloud Computing, 2020

2019
Towards Framework-Independent, Non-Intrusive Performance Characterization for Dataflow Computation.
Proceedings of the 10th ACM SIGOPS Asia-Pacific Workshop on Systems, 2019

2018
OpuS: Fair and Efficient Cache Sharing for In-Memory Data Analytics.
Proceedings of the 38th IEEE International Conference on Distributed Computing Systems, 2018

Fast Distributed Deep Learning via Worker-adaptive Batch Sizing.
Proceedings of the ACM Symposium on Cloud Computing, 2018


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