A Novel Design of a Parallel Machine Learnt Generational Garbage Collector

TL;DR

This paper proposes a new parallel machine learning-based generational garbage collector that predicts object lifetimes to optimize memory reclamation and reduce promotion overhead.

Contribution

It introduces a novel method that predicts object lifetimes to improve garbage collection efficiency by minimizing unnecessary promotions.

Findings

Enhanced memory reclamation efficiency

Reduced promotion overhead in garbage collection

Improved performance in memory management

Abstract

The Generational Garbage collection involves organizing the heap into different divisions of memory space in-order to filter long-lived objects from short-lived objects through moving the surviving object of each generation Garbage Collection cycle to another memory space updating its age and reclaiming space from the dead ones. The problem in this method is that the longer an object is alive during its initial generations the longer the garbage collector will have to deal with it by checking for its reachability from the root and promoting it to other space divisions where as the ultimate goal of the Garbage Collector is to reclaim memory from unreachable objects at a minimal time possible. This paper is a proposal of a method where the lifetime of every object getting into the heap will be predicted and will be placed in heap accordingly for the garbage collector to deal more with reclaiming space from dead objects and less in promoting the live ones to the higher level.