@article{dias,
  author = {Baziotis, Stefanos and Kang, Daniel and Mendis, Charith},
  title = {Dias: Dynamic Rewriting of Pandas Code},
  year = {2024},
  issue_date = {February 2024},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {2},
  number = {1},
  url = {https://doi.org/10.1145/3639313},
  doi = {10.1145/3639313},
  abstract = {In recent years, dataframe libraries, such as pandas have exploded in popularity. Due to their flexibility, they are increasingly used in ad-hoc exploratory data analysis (EDA) workloads. These workloads are diverse, including custom functions which can span libraries or be written in pure Python. The majority of systems available to accelerate EDA workloads focus on bulk-parallel workloads, which contain vastly different computational patterns, typically within a single library. As a result, they can introduce excessive overheads for ad-hoc EDA workloads due to their expensive optimization techniques. Instead, we identify source-to-source, external program rewriting as a lightweight technique which can optimize across representations, and offer substantial speedups while also avoiding slowdowns. We implemented Dias, which rewrites notebook cells to be more efficient for ad-hoc EDA workloads. We develop techniques for efficient rewrites in Dias, including checking the preconditions under which rewrites are correct, dynamically, at fine-grained program points. We show that Dias can rewrite individual cells to be 57\texttimes{} faster compared to pandas and 1909\texttimes{} faster compared to optimized systems such as modin. Furthermore, Dias can accelerate whole notebooks by up to 3.6\texttimes{} compared to pandas and 27.1\texttimes{} compared to modin.},
  journal = {Proc. ACM Manag. Data},
  month = {mar},
  articleno = {58},
  numpages = {27},
  keywords = {cross-representation, dynamic, pandas, rewriting}
  }

@inproceedings{hydride,
  author = {Kothari, Akash and Noor, Abdul Rafae and Xu, Muchen and Uddin, Hassam and Baronia, Dhruv and Baziotis, Stefanos and Adve, Vikram and Mendis, Charith and Sengupta, Sudipta},
  title = {Hydride: A Retargetable and Extensible Synthesis-based Compiler for Modern Hardware Architectures},
  year = {2024},
  isbn = {9798400703850},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3620665.3640385},
  doi = {10.1145/3620665.3640385},
  abstract = {As modern hardware architectures evolve to support increasingly diverse, complex instruction sets for meeting the performance demands of modern workloads in image processing, deep learning, etc., it has become ever more crucial for compilers to provide robust support for evolution of their internal abstractions and retargetable code generation support to keep pace with emerging instruction sets. We propose Hydride, a novel approach to compiling for complex, emerging hardware architectures. Hydride uses vendor-defined pseudocode specifications of multiple hardware ISAs to automatically design retargetable instructions for AutoLLVM IR, an extensible compiler IR which consists of (formally defined) language-independent and target-independent LLVM IR instructions to compile to those ISAs, and automatically generated instruction selection passes to lower AutoLLVM IR to each of the specified hardware ISAs. Hydride also includes a code synthesizer that automatically generates code generation support for schedule-based languages, such as Halide, to optimally generate AutoLLVM IR. Our results show that Hydride is able to represent 3,557 instructions combined in x86, Hexagon, ARM architectures using only 397 AutoLLVM IR instructions, including (Intel) SSE2, SSE4, AVX, AVX2, AVX512, (Qualcomm) Hexagon HVX, and (ARM) NEON vector ISAs. We created a new Halide compiler with Hydride using only a formal semantics of Halide IR, leveraging the auto-generated AutoLLVM IR and back-ends for the three hardware architectures. Across kernels from deep learning and image processing, this compiler is able to perform just as well as the mature, production Halide compiler on Hexagon, and outperform on x86 by 8\% and ARM by 3\%. Hydride also outperforms the production Halide's LLVM back end by 12\% on x86, 100\% on HVX, and 26\% on ARM across the same kernels.},
  booktitle = {Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2},
  pages = {514–529},
  numpages = {16},
  location = {La Jolla, CA, USA},
  series = {ASPLOS '24}
  }