Evaluating End-to-End Optimization for Data Analytics Applications in Weld

@article{Palkar2018EvaluatingEO,
  title={Evaluating End-to-End Optimization for Data Analytics Applications in Weld},
  author={Shoumik Palkar and James J. Thomas and Deepak Narayanan and Pratiksha Thaker and Rahul Palamuttam and Parimarjan Negi and Anil Shanbhag and Malte Schwarzkopf and Holger Pirk and Saman P. Amarasinghe and Samuel Madden and Matei A. Zaharia},
  journal={Proc. VLDB Endow.},
  year={2018},
  volume={11},
  pages={1002-1015}
}
Modern analytics applications use a diverse mix of libraries and functions. [] Key Method Our optimizer eliminates multiple forms of overhead that arise when composing imperative libraries like Pandas and NumPy, and uses lightweight measurements to make data-dependent decisions at run-time in ad-hoc workloads where no statistics are available, with sub-second overhead. We also evaluate which optimizations have the largest impact in practice and whether Weld can be integrated into libraries incrementally. Our…

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