Empirical measurements of six allocation-intensive C programs

Author:

Zorn Benjamin,Grunwald Dirk

Abstract

Dynamic memory management is an important part of a large class of computer programs and high-performance algorithms for dynamic memory management have been, and will continue to be, of considerable interest. This paper presents empirical data from a collection of six allocation-intensive C programs. Extensive statistics about the allocation behavior of the programs measured, including the distributions of object sizes, lifetimes, and interarrival times, are presented. This data is valuable for the following reasons: first, the data from these programs can be used to design high-performance algorithms for dynamic memory management. Second, these programs can be used as a benchmark test suite for evaluating and comparing the performance of different dynamic memory management algorithms. Finally, the data presented gives readers greater insight into the storage allocation patterns of a broad range of programs. The data presented in this paper is an abbreviated version of more extensive statistics that are publically available on the internet.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Cited by 24 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MIFP: Selective Fat-Pointer Bounds Compression for Accurate Bounds Checking;Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses;2023-10-16

2. Profile Dynamic Memory Allocation in Autonomous Driving Software;2023 10th International Conference on Dependable Systems and Their Applications (DSA);2023-08-10

3. In-fat pointer: hardware-assisted tagged-pointer spatial memory safety defense with subobject granularity protection;Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems;2021-04-17

4. Deep Parameter Optimisation;Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation;2015-07-11

5. ACDC-JS;ACM SIGPLAN Notices;2015-05-12

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3