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BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach

Summary: BladeDISC is a compiler-driven optimizer for dynamic-shape ML workloads, tackling fusion and codegen with unknown shapes. Key ideas: symbolic shape representation and shape-information propagation to enable shape-agnostic fusion and generic codegen. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h160c2f26e908fb5e
Venue
SIGMOD
Year
2023
Pagerank
5.142891e-05
Overall Rank
9,663 | 35.06%
DOI
10.1145/3617327

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zheng_sigmod23,
        title = {{BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach}},
        author = {Zheng, Zhen and Pan, Zaifeng and Wang, Dalin and Zhu, Kai and Zhao, Wenyi and Guo, Tianyou and Qiu, Xiafei and Sun, Minmin and Bai, Junjie and Zhang, Feng and Du, Xiaoyong and Zhai, Jidong and Lin, Wei},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3617327},
        url = {https://dl.acm.org/doi/10.1145/3617327},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,198 Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization 2025 SIGMOD 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 30 of 30 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
21 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.00056835296
53 Eddies: Continuously Adaptive Query Processing 2000 SIGMOD 0.000408505
471 Robust Query Processing through Progressive Optimization 2004 SIGMOD 0.00017744392
523 PyTorch Distributed: Experiences on Accelerating Data Parallel Training 2020 VLDB 0.000169044
1,082 Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML 2014 VLDB 0.00012118261
1,195 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011574218
1,445 An Architecture for Compiling UDF-centric Workflows 2015 VLDB 0.00010628379
1,534 Pipelined Query Processing in Coprocessor Environments 2018 SIGMOD 0.00010327147
1,542 HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics 2016 VLDB 0.0001030858
1,803 Tuplex: Data Science in Python at Native Code Speed 2021 SIGMOD 9.602292e-05
2,251 Evaluating End-to-End Optimization for Data Analytics Applications in Weld 2018 VLDB 8.750953e-05
2,281 NeutronStar: Distributed GNN Training with Hybrid Dependency Management 2022 SIGMOD 8.7021423e-05
2,682 Accelerating Recommendation System Training by Leveraging Popular Choices 2022 VLDB 8.1384948e-05
3,103 On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML 2018 VLDB 7.6456038e-05
3,974 TCUDB: Accelerating Database with Tensor Processors 2022 SIGMOD 6.8848857e-05
3,986 Designing an Open Framework for Query Optimization and Compilation 2022 VLDB 6.8697828e-05
4,117 Resource Elasticity for Large-Scale Machine Learning 2015 SIGMOD 6.7929814e-05
4,647 Improving Execution Efficiency of Just-in-time Compilation based Query Processing on GPUs 2021 VLDB 6.4850635e-05
4,949 HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training 2022 SIGMOD 6.3405861e-05
4,965 Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce 2021 SIGMOD 6.3354086e-05
5,011 Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism 2023 VLDB 6.3131083e-05
5,635 NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access 2022 SIGMOD 6.0549984e-05
6,444 Grizzly: Efficient Stream Processing Through Adaptive Query Compilation 2020 SIGMOD 5.7807677e-05
7,787 Measuring and Optimizing Distributed Array Programs 2016 VLDB 5.4511698e-05
7,966 Excalibur: A Virtual Machine for Adaptive Fine-grained JIT-Compiled Query Execution based on VOILA 2023 VLDB 5.4141361e-05
8,528 FuseME: Distributed Matrix Computation Engine based on Cuboid-based Fused Operator and Plan Generation 2022 SIGMOD 5.3212942e-05
8,562 Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers 2022 VLDB 5.3127204e-05
9,171 Optimizing Inference Serving on Serverless Platforms 2022 VLDB 5.2128799e-05
9,466 COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression 2022 VLDB 5.1710538e-05
10,018 ETO: Accelerating Optimization of DNN Operators by High-Performance Tensor Program Reuse 2022 VLDB 5.0947732e-05
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