Improving Performance Through Object Lifetime Profiling: the DataFrame Case - Pharo
Conference Papers Year : 2023

Improving Performance Through Object Lifetime Profiling: the DataFrame Case

Abstract

Being capable of profiling the object lifetimes of an application gives information that can be used to optimize the GC performance and improve overall execution time. One can pre-tenure objects based on profiler information, tune the GC parameters, or take decisions about pre-allocating bigger memory segments. However, accessing object lifetimes is difficult because it requires monitoring any object GC reclamation. We developed an open-source lifetime profiler. Our current implementation does not require Virtual Machine modification. It is based on ephemerons and method proxies. We profiled DataFrame and we observed a significant number of objects that lived a long time. We used this information to tune the garbage collector parameters and we got up to 6.8 times of performance improvements.
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Dates and versions

hal-04253865 , version 1 (23-10-2023)

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  • HAL Id : hal-04253865 , version 1

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Sebastian Jordan Montaño, Nahuel Palumbo, Guillermo Polito, Stéphane Ducasse, Pablo Tesone. Improving Performance Through Object Lifetime Profiling: the DataFrame Case. IWST 2023 - International Workshop on Smalltalk Technologies, Aug 2023, Lyon, France. ⟨hal-04253865⟩
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