Kindergarten Cop : dynamic nursery resizing for GHC
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Generational garbage collectors are among the most popular garbage collectors used in programming language runtime systems. Their performance is known to depend heavily on choosing the appropriate size of the area where new objects are allocated (the nursery). In imperative languages, it is usual to make the nursery as large as possible, within the limits imposed by the heap size. Functional languages, however, have quite different memory behaviour. In this paper, we study the effect that the nursery size has on the performance of lazy functional programs, through the interplay between cache locality and the frequency of collections. We demonstrate that, in contrast with imperative programs, having large nurseries is not always the best solution. Based on these results, we propose two novel algorithms for dynamic nursery resizing that aim to achieve a compromise between good cache locality and the frequency of garbage collections. We present an implementation of these algorithms in the state-of-the-art GHC compiler for the functional language Haskell, and evaluate them using an extensive benchmark suite. In the best case, we demonstrate a reduction in total execution times of up to 88.5%, or an 8.7 overall speedup, compared to using the production GHC garbage collector. On average, our technique gives an improvement of 9.3% in overall performance across a standard suite of 63 benchmarks for the production GHC compiler.
Ferreiro , H , Castro , L , Janjic , V & Hammond , K 2016 , Kindergarten Cop : dynamic nursery resizing for GHC . in CC 2016 Proceedings of the 25th International Conference on Compiler Construction . ACM , New York , pp. 56-66 , 25th International Conference on Compiler Construction , Barcelona , Spain , 17/03/16 . DOI: 10.1145/2892208.2892223conference
CC 2016 Proceedings of the 25th International Conference on Compiler Construction
© 2016, Publisher / the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at dl.acm.org / https://dx.doi.org/10.1145/2892208.2892223
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