Weld: A Common Runtime for High Performance Data Analytics
Summary: Weld presents a common data-parallel IR and runtime to optimize across disjoint libraries (SQL, ML, graph, array), eliminating expensive cross-library data movement. By operator fusion and whole-workflow code generation, Weld plugs into Spark/TensorFlow/NumPy/Pandas and yields up to 30× end-to-end speedups. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Shoumik Palkar (Stanford University)
- 2. James J. Thomas (Stanford University)
- 3. Anil Shanbhag (Massachusetts Institute of Technology)
- 4. Deepak Narayanan (Stanford University)
- 5. Holger Pirk (Massachusetts Institute of Technology)
- 6. Malte Schwarzkopf (Massachusetts Institute of Technology)
- 7. Saman Amarasinghe (Massachusetts Institute of Technology)
- 8. Matei Zaharia (Stanford University)
BibTeX Citation
@inproceedings{palkar_cidr17,
address = {Amsterdam, Netherlands},
series = {{CIDR} '17},
title = {{Weld: A Common Runtime for High Performance Data Analytics}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Palkar, Shoumik and Thomas, James J. and Shanbhag, Anil and Narayanan, Deepak and Pirk, Holger and Schwarzkopf, Malte and Amarasinghe, Saman and Zaharia, Matei},
year = {2017}
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