Optimizing Tensor Programs on Flexible Storage

Summary: Declarative storage mappings enable flexible tensor layouts (e.g., COO, CSR) in a unified tensor query language. A cost-based optimizer tunes tensor programs to the chosen layout, delivering empirically significant improvements over state-of-the-art tensor processing systems. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6602
Venue: SIGMOD
Year: 2023
Pagerank: 6.2258658e-05
Overall Rank: 5,414 | 62.86%
DOI: 10.1145/3588717

Incoming Non-self Citations Over Time

Authors

1. Maximilian Schleich (RelationalAI)
2. Amir Shaikhha (University of Edinburgh)
3. Dan Suciu (University of Washington)

BibTeX Citation

@inproceedings{schleich_sigmod23,
        title = {{Optimizing Tensor Programs on Flexible Storage}},
        author = {Schleich, Maximilian and Shaikhha, Amir and Suciu, Dan},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588717},
        url = {https://dl.acm.org/doi/10.1145/3588717},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,075	Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity	2024	VLDB	5.7099047e-05
9,834	EinDecomp: Decomposition of Declaratively-Specified Machine Learning and Numerical Computations for Parallel Execution	2025	VLDB	5.2112879e-05
9,880	MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training	2025	SIGMOD	5.2040783e-05
9,960	Optimizing Nested Recursive Queries	2024	SIGMOD	5.1879626e-05
10,185	A Backend-Agnostic Compiler for Approximate Query Processing with Probabilistic Tensor Algebra	2026	SIGMOD	5.093636e-05
10,292	SPALM: A Sparsity-Pattern-Adaptive Library for Matrices	2026	SIGMOD	5.093636e-05
10,514	Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation	2026	VLDB	5.093636e-05
11,261	Efficient Enumeration of Recursive Plans in Transformation-based Query Optimizers	2024	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 18 of 18 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
17	Provenance Semirings	2007	PODS	0.00059843817
103	DuckDB: an Embeddable Analytical Database	2019	SIGMOD	0.00034161428
570	The GMAP: A Versatile Tool for Physical Data Independence	1994	VLDB	0.00016328445
715	Learning Generalized Linear Models Over Normalized Data	2015	SIGMOD	0.00014655327
724	The Multidimensional Database System RasDaMan	1998	SIGMOD	0.00014620119
1,235	Towards Linear Algebra over Normalized Data	2017	VLDB	0.00011548457
1,298	Analysis of Two Existing and One New Dynamic Programming Algorithm for the Generation of Optimal Bushy Join Trees without Cross Products	2006	VLDB	0.00011259156
1,740	Adopting Worst-Case Optimal Joins in Relational Database Systems	2020	VLDB	9.875587e-05
2,769	A Layered Aggregate Engine for Analytics Workloads	2019	SIGMOD	8.1465406e-05
3,205	On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML	2018	VLDB	7.6386536e-05
4,442	SciQL: Array Data Processing Inside an RDBMS	2013	SIGMOD	6.700604e-05
4,976	SPORES: Sum-Product Optimization via Relational Equality Saturation for Large Scale Linear Algebra	2020	VLDB	6.4168322e-05
5,189	Performance Analysis of a Relational Data Base Management System	1979	SIGMOD	6.3272576e-05
5,195	Tensor Relational Algebra for Distributed Machine Learning System Design	2021	VLDB	6.3232105e-05
6,604	Machine Learning, Linear Algebra, and More: Is SQL All You Need?	2022	CIDR	5.8240599e-05
7,043	Automatic Synthesis of Out-of-Core Algorithms	2013	SIGMOD	5.7188039e-05
8,960	An Intermediate Representation for Hybrid Database and Machine Learning Workloads	2021	VLDB	5.3444589e-05
8,999	HADAD: A Lightweight Approach for Optimizing Hybrid Complex Analytics Queries	2021	SIGMOD	5.3354529e-05

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