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On the Optimization of Iterative Programming with Distributed Data Collections

Sarah Chlyah 1 Nils Gesbert 1 Pierre Genevès 1 Nabil Layaïda 1 
1 TYREX - Types and Reasoning for the Web
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Big data programming frameworks are becoming increasingly important for the development of applications for which performance and scalability are critical. In those complex frameworks, optimizing code by hand is hard and time-consuming, making automated optimization particularly necessary. In order to automate optimization, a prerequisite is to find suitable abstractions to represent programs; for instance, algebras based on monads or monoids to represent distributed data collections. Currently, however, such algebras do not represent recursive programs in a way which allows for analyzing or rewriting them. In this paper, we extend a monoid algebra with a fixpoint operator for representing recursion as a first class citizen and show how it enables new optimizations. Experiments with the Spark platform illustrate performance gains brought by these systematic optimizations.
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Submitted on : Tuesday, May 24, 2022 - 4:14:23 PM
Last modification on : Wednesday, July 6, 2022 - 4:13:07 AM


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  • HAL Id : hal-02066649, version 6



Sarah Chlyah, Nils Gesbert, Pierre Genevès, Nabil Layaïda. On the Optimization of Iterative Programming with Distributed Data Collections. 2022. ⟨hal-02066649v6⟩



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