Optimizing Data-Intensive Applications Automatically By Leveraging Parallel Data Processing Frameworks

Author:

Ahmad Maaz Bin Safeer1,Cheung Alvin1

Affiliation:

1. University of Washington, Seattle, WA, USA

Funder

NSF

DARPA

DOE

Publisher

ACM

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Lifting Micro-Update Models from RTL for Formal Security Analysis;Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2;2024-04-27

2. C2TACO: Lifting Tensor Code to TACO;Proceedings of the 22nd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences;2023-10-22

3. mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR Using Program Synthesis;2023 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT);2023-10-21

4. Towards Auto-Generated Data Systems;Proceedings of the VLDB Endowment;2023-08

5. An Architecture for Translating Sequential Code to Parallel;Proceedings of the 2nd International Conference on Information System and Data Mining - ICISDM '18;2018

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