There Ain't No Such Thing as a Free Custom Memory Allocator
Gunnar Kudrjavets (University of Groningen), Jeff Thomas (Meta, Platforms, Inc.), Aditya Kumar (Snap, Inc.), Nachiappan Nagappan (Meta, Platforms, Inc.), and Ayushi Rastogi (University of Groningen)
TL;DR
This paper discusses the benefits and challenges of using custom memory allocators in industrial software, offering practical guidance based on extensive industry experience to optimize performance while managing maintenance issues.
Contribution
It provides a comprehensive set of lessons learned and recommendations for effectively integrating custom memory allocators in industrial software projects.
Findings
Custom allocators improve performance but increase maintenance complexity.
Effective integration requires careful planning and experience-based guidelines.
Industry insights highlight common pitfalls and best practices.
Abstract
Using custom memory allocators is an efficient performance optimization technique. However, dependency on a custom allocator can introduce several maintenance-related issues. We present lessons learned from the industry and provide critical guidance for using custom memory allocators and enumerate various challenges associated with integrating them. These recommendations are based on years of experience incorporating custom allocators into different industrial software projects.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.