Efficient iterative programs with distributed data collections
Big data programming frameworks have become 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 t...
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| Published in | Journal of logical and algebraic methods in programming Vol. 144; pp. 101047 - 36 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.03.2025
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2352-2208 |
| DOI | 10.1016/j.jlamp.2025.101047 |
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| Abstract | Big data programming frameworks have become 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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| AbstractList | Big data programming frameworks have become 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. |
| ArticleNumber | 101047 |
| Author | Chlyah, Sarah Genevès, Pierre Gesbert, Nils Layaïda, Nabil |
| Author_xml | – sequence: 1 givenname: Sarah orcidid: 0009-0004-1769-5109 surname: Chlyah fullname: Chlyah, Sarah email: sarah.chlyah@inria.fr – sequence: 2 givenname: Nils surname: Gesbert fullname: Gesbert, Nils – sequence: 3 givenname: Pierre surname: Genevès fullname: Genevès, Pierre – sequence: 4 givenname: Nabil surname: Layaïda fullname: Layaïda, Nabil |
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| Cites_doi | 10.1017/S0956796817000193 10.1145/3281629 10.1017/S1471068417000436 10.1145/362384.362685 10.1145/2934664 10.1017/S0960129500001560 10.1007/s00224-016-9676-2 10.1145/2850413 10.14778/2556549.2556572 10.1016/0304-3975(82)90125-6 10.1007/s10009-012-0244-z 10.1109/32.42731 10.14778/2824032.2824052 10.1145/1924421.1924436 10.1145/2389241.2389250 10.1016/0304-3975(95)00024-Q 10.1145/377674.377676 10.1145/2724712 10.14778/3007263.3007270 10.1145/2949741.2949754 10.1145/2812805 |
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| Keywords | Fixpoint operator Distributed data Rewrite rules Optimization rewrite rules fixpoint operator optimization distributed data collections Efficient |
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