Siyu Wang

Orcid: 0009-0002-4064-6984

Affiliations:
  • Alibaba Group, Beijing, China


According to our database1, Siyu Wang authored at least 12 papers between 2020 and 2025.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
DyOrc: Efficient Serving of Dynamic Machine Learning Workflows.
Proceedings of the 2025 ACM Symposium on Cloud Computing, 2025

Concerto: Automatic Communication Optimization and Scheduling for Large-Scale Deep Learning.
Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2025

2024
HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis.
Proceedings of the Nineteenth European Conference on Computer Systems, 2024

2023
HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis.
Dataset, November, 2023

HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis.
Dataset, November, 2023

Expediting Distributed DNN Training With Device Topology-Aware Graph Deployment.
IEEE Trans. Parallel Distributed Syst., April, 2023

Ada-Grouper: Accelerating Pipeline Parallelism in Preempted Network by Adaptive Group-Scheduling for Micro-Batches.
CoRR, 2023

Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform.
CoRR, 2023

2022
Optimizing DNN Compilation for Distributed Training With Joint OP and Tensor Fusion.
IEEE Trans. Parallel Distributed Syst., 2022

Accelerating large-scale distributed neural network training with SPMD parallelism.
Proceedings of the 13th Symposium on Cloud Computing, SoCC 2022, 2022

2021
DAPPLE: a pipelined data parallel approach for training large models.
Proceedings of the PPoPP '21: 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2021

2020
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads.
CoRR, 2020


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