6 papers
Isolating Recurring Execution-Dependent Abnormal Patterns on NISQ Quantum Devices
Zhenyu Qi, Haotang Li, Mominul Islam +3
Quantum devices increasingly expose a fundamental gap between compiler-modeled noise and hardware execution. Today's compilers approximate noise as calibration-derived costs over g…
Tensor Algebraic Property Skeletons: Amplifying Property-Based Testing for AI Compilers
Yuxin Qiu, Ben Limpanukorn, Seongmin Lee +3
Deep learning (DL) compilers such as TVM and ONNX-MLIR lower tensor computation graphs into optimized executables for target backends. Testing these compilers has made substantial…
Finding Compiler-Platform Interaction Bugs in Deep Learning Pipelines via Cross-Layer Constraints
Yuxin Qiu, Jiyuan Wang, Ronak Badhe +3
The growing deployment of artificial intelligence (AI) necessitates robust deep learning (DL) compilers, such as TVM and ONNX-MLIR. These compilers take as input high-level AI mode…
Needle in the Repo: A Benchmark for Maintainability in AI-Generated Repository Edits
Haichao Zhu, Qian Zhang, Jiyuan Wang +2
AI coding agents can now complete complex programming tasks, but existing evaluations largely emphasize behavioral correctness and often overlook maintainability risks such as weak…
Targeted Testing of Compiler Optimizations via Grammar-Level Composition Styles
Zitong Zhou, Ben Limpanukorn, Hong Jin Kang +5
Ensuring the correctness of compiler optimizations is critical, but existing fuzzers struggle to test optimizations effectively. First, most fuzzers use optimization pipelines (heu…
PerfGen: Automated Performance Benchmark Generation for Big Data Analytics
Jiyuan Wang, Jason Teoh, Muhammand Ali Gulza +2
Many symptoms of poor performance in big data analytics such as computational skews, data skews, and memory skews are input dependent. However, due to the lack of inputs that can t…