Publications

2026

1. TACO 2026

   A Decoupled Analytical Model for Tile Size Selection in Affine Programs

   Shihan Yuan^*, Zuoyan Zhang^*, Guanghui Song, Junhui Peng, and 4 more authors

   ACM Trans. Archit. Code Optim., Apr 2026

   Abs DOI Bib PDF

   Existing tile size selection approaches are tightly coupled with compiler transformation pipelines, often leading to inaccurate modeling of cache behavior and limited effectiveness for non-rectangular tile shapes. This paper presents TileMind, a decoupled analytical model that combines compile-time and runtime information for tile size selection in affine programs. It introduces a transformation-aware pre-tiling step that enables the decoupled selector to remain consistent with compiler transformations while extracting compile-time metadata. The extracted metadata is then combined with profiled runtime characteristics to construct a richer yet tractable feasible domain, within which a nonlinear objective for tile size selection is formulated. This objective is subsequently transformed into a binary product linearization problem, with its nonlinear constraints also linearized for efficient optimization. Finally, an intra-tile optimization aligns computation with data layout to enhance data reuse within tiles. Across two multi-core Intel CPUs, TileMind achieves 1.49x (sequential) and 1.33x (parallel) mean speedups on twenty PolyBench kernels, and 2.08-3.54x speedups on three deep learning workloads over the state-of-the-art analytical model Pluto-tss. Compared with TVM’s latest autotuner MetaSchedule, TileMind delivers 1.35-1.46x mean speedups while reducing tuning overhead by 2-4 orders of magnitude. While demonstrating effectiveness on selecting tile sizes for non-rectangular tile shapes and compatibility with PPCG, Pluto, and TVM, we further provide proof-of-concept results on GPUs, illustrating the potential portability of TileMind across architectures.

   @article{tilemind2026,
     author = {Yuan, Shihan and Zhang, Zuoyan and Song, Guanghui and Peng, Junhui and Wang, Feng and Tang, Zhuo and Li, Kenli and Zhao, Jie},
     title = {A Decoupled Analytical Model for Tile Size Selection in Affine Programs},
     year = {2026},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     issn = {1544-3566},
     url = {https://doi.org/10.1145/3806056},
     doi = {10.1145/3806056},
     journal = {ACM Trans. Archit. Code Optim.},
     month = apr,
     keywords = {Loop Tiling, Polyhedral Compilation, Analytical Model, Cache Optimization}
   }

2025

1. QRS 2025

   Scalable Detection of Floating-point Errors via Adaptive Parallel Subdomain Search

   Zuoyan Zhang, Shihan Yuan, Hongru Yang, Jie Zhao, and 1 more author

   In 2025 25th International Conference on Software Quality, Reliability and Security (QRS), Hangzhou, China, Apr 2025

   Abs DOI Bib PDF Slides

   Floating-point error detection is crucial in numerical computing, particularly for multi-parameter functions where even minor errors can propagate and significantly impact results. The sparse distribution of floating-point errors poses a significant detection challenge, as significant deviations are triggered by only rare inputs. Existing search algorithms face two major limitations: poor scalability for multi-parameter functions and insufficient utilization of floating-point representation characteristics. To address these challenges, we propose SDPS (Scalable Detection via Parallel Subdomain Search), a novel algorithm that combines adaptive domain partitioning with floating-point-specific heuristics. SDPS employs a multi-level error classification system and specialized point generation strategies, supported by efficient parallel processing through dynamic task allocation. Our comprehensive evaluation demonstrates that SDPS significantly outperforms state-of-the-art methods in both detection accuracy and computational efficiency, especially for multi-parameter functions where it effectively addresses the exponential growth of search space that limits existing approaches.

   @inproceedings{qrs2025,
     author = {Zhang, Zuoyan and Yuan, Shihan and Yang, Hongru and Zhao, Jie and Xu, Jinchen},
     title = {Scalable Detection of Floating-point Errors via Adaptive Parallel Subdomain Search},
     year = {2025},
     address = {Hangzhou, Zhejiang, China},
     booktitle = {2025 25th International Conference on Software Quality, Reliability and Security (QRS)},
     publisher = {IEEE},
     pages = {392-403},
     volume = {},
     number = {},
     keywords = {Software testing;Scalability;Software quality;Parallel processing;Partitioning algorithms;Classification algorithms;Software reliability;Performance analysis;Security;Resource management;floating-point error;error detection;program analysis;dynamic analysis;software testing},
     location = {Hangzhou, China},
     series = {QRS 2025},
     doi = {10.1109/QRS65678.2025.00047}
   }

2024

1. ISSTA 2024

   Arfa: An Agile Regime-Based Floating-Point Optimization Approach for Rounding Errors

   Jinchen Xu^*, Mengqi Cui^*, Fei Li, Zuoyan Zhang, and 3 more authors

   In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, Vienna, Austria, Apr 2024

   Abs DOI Bib PDF Slides

   We introduce a floating-point (FP) error optimization approach called Arfa that partitions the domain D of an FP expression fe into regimes and rewrites fe in each regime where fe shows larger errors. First, Arfa seeks a rewrite substitution fo with lower errors across D, whose error distribution is plotted for effective regime inference. Next, Arfa generates an incomplete set of ordered rewrite candidates within each regime of interest, so that searching for the best rewrite substitutions is performed efficiently. Finally, Arfa selects the best rewrite substitution by inspecting the errors of top ranked rewrite candidates, with enhancing precision also considered. Experiments on 56 FPbench examples and four real-life programs show that Arfa not only reduces the maximum and average errors of fe by 4.73 and 2.08 bits on average (and up to 33 and 16 bits), but also exhibits lower errors, sometimes to a significant degree, than Herbie and NumOpt.

   @inproceedings{arfa,
     author = {Xu, Jinchen and Cui, Mengqi and Li, Fei and Zhang, Zuoyan and Yang, Hongru and Zhou, Bei and Zhao, Jie},
     title = {Arfa: An Agile Regime-Based Floating-Point Optimization Approach for Rounding Errors},
     year = {2024},
     isbn = {9798400706127},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3650212.3680378},
     doi = {10.1145/3650212.3680378},
     booktitle = {Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis},
     pages = {1516-1528},
     numpages = {13},
     keywords = {FPbench, dynamic analysis, e-graph, floating-point errors, numerical analysis, rewrite},
     location = {Vienna, Austria},
     series = {ISSTA 2024}
   }

2023

1. TJSC

   Hierarchical search algorithm for error detection in floating-point arithmetic expressions

   Zuoyan Zhang^*, Jinchen Xu^*, Jiangwei Hao, Yang Qu, and 2 more authors

   J. Supercomput., Jul 2023

   Abs DOI Bib PDF

   Scientific and engineering applications rely on floating-point arithmetic to approximate real numbers. Due to the inherent rounding errors in floating-point numbers, error propagation during calculations can accumulate and lead to serious errors that may compromise the safety and reliability of the program. In theory, the most accurate method of error detection is to exhaustively search all possible floating-point inputs, but this is not feasible in practice due to the huge search space involved. Effectively and efficiently detecting maximum floating-point errors has been a challenge. To address this challenge, we design and implement an error detection tool for floating-point arithmetic expressions called HSED. It leverages modified mantissas under double precision floating-point types to simulate hierarchical searches from either half or single precision to double precision. Experimental results show that for 32 single-parameter arithmetic expressions in the FPBench benchmark test set, the error detection effects and performance of HSED are significantly better than the state-of-the-art error detection tools Herbie, S3FP and ATOMU. HSED outperforms Herbie, Herbie+, S3FP and ATOMU in 24, 19, 27 and 25 cases, respectively. The average time taken by Herbie, Herbie+, and S3FP is 1.82, 11.20, and 129.15 times longer than HSED, respectively.

   @article{hsed,
     author = {Zhang, Zuoyan and Xu, Jinchen and Hao, Jiangwei and Qu, Yang and He, Haotian and Zhou, Bei},
     title = {Hierarchical search algorithm for error detection in floating-point arithmetic expressions},
     year = {2023},
     issue_date = {Jan 2024},
     publisher = {Kluwer Academic Publishers},
     address = {USA},
     volume = {80},
     number = {1},
     issn = {0920-8542},
     url = {https://doi.org/10.1007/s11227-023-05523-6},
     doi = {10.1007/s11227-023-05523-6},
     journal = {J. Supercomput.},
     month = jul,
     pages = {1183-1205},
     numpages = {23},
     keywords = {Floating-point arithmetic, Error detection, Dynamic analysis, Hierarchical search}
   }

2. ASE 2023

   

   Eiffel: Inferring Input Ranges of Significant Floating-Point Errors via Polynomial Extrapolation

   Zuoyan Zhang, Bei Zhou, Jiangwei Hao, Hongru Yang, and 6 more authors

   In Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, Echternach, Luxembourg, Jul 2023

   Abs DOI Bib PDF Video Slides

   Existing search heuristics used to find input values that result in significant floating-point (FP) errors or small ranges that cover them are accompanied by severe constraints, complicating their implementation and restricting their general applicability. This paper introduces an error analysis tool called Eiffel to infer error-inducing input ranges instead of searching them. Given an FP expression with its domain D, Eiffel first constructs an error data set by sampling values across a smaller domain ℛ and assembles these data into clusters. If more than two clusters are formed, Eiffel derives polynomial curves that best fit the bound coordinates of the error-inducing ranges in ℛ, extrapolating them to infer all target ranges of D and reporting the maximal error. Otherwise, Eiffel simply returns the largest error across ℛ. Experimental results show that Eiffel exhibits a broader applicability than Atomu and S3FP by successfully detecting the errors of all 70 considered benchmarks while the two baselines only report errors for part of them. By taking as input the inferred ranges of Eiffel, Herbie obtains an average accuracy improvement of 3.35 bits and up to 53.3 bits.

   @inproceedings{ase 2023,
     author = {Zhang, Zuoyan and Zhou, Bei and Hao, Jiangwei and Yang, Hongru and Cui, Mengqi and Zhou, Yuchang and Song, Guanghui and Li, Fei and Xu, Jinchen and Zhao, Jie},
     title = {Eiffel: Inferring Input Ranges of Significant Floating-Point Errors via Polynomial Extrapolation},
     year = {2023},
     isbn = {9798350329964},
     publisher = {IEEE Press},
     url = {https://doi.org/10.1109/ASE56229.2023.00139},
     doi = {10.1109/ASE56229.2023.00139},
     booktitle = {Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering},
     pages = {1441-1453},
     numpages = {13},
     location = {Echternach, Luxembourg},
     series = {ASE '23}
   }