Comparative Analysis of Classic Garbage-Collection Algorithms for a Lisp-like Language
April 30, 2015 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tyler Hannan, Chester Holtz, Jonathan Liao
arXiv ID
1505.00017
Category
cs.PL: Programming Languages
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In this paper, we demonstrate the effectiveness of Cheney's Copy Algorithm for a Lisp-like system and experimentally show the infeasability of developing an optimal garbage collector for general use. We summarize and compare several garbage-collection algorithms including Cheney's Algorithm, the canonical Mark and Sweep Algorithm, and Knuth's Classical Lisp 2 Algorithm. We implement and analyze these three algorithms in the context of a custom MicroLisp environment. We conclude and present the core considerations behind the development of a garbage collector---specifically for Lisp---and make an attempt to investigate these issues in depth. We also discuss experimental results that imply the effectiveness of Cheney's algorithm over Mark-Sweep for Lisp-like languages.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted