An efficient single-pass trace compression technique utilizing instruction streams

Author:

Milenković Aleksandar1,Milenković Milena2

Affiliation:

1. The University of Alabama in Huntsville, Huntsville, AL

2. IBM, Austin, TX

Abstract

Trace-driven simulations have been widely used in computer architecture for quantitative evaluations of new ideas and design prototypes. Efficient trace compression and fast decompression are crucial for contemporary workloads, as representative benchmarks grow in size and number. This article presents Stream-Based Compression (SBC), a novel technique for single-pass compression of address traces. The SBC technique compresses both instruction and data addresses by associating them with a particular instruction stream, that is, a block of consecutively executing instructions. The compressed instruction trace is a trace of instruction stream identifiers. The compressed data address trace encompasses the data address stride and the number of repetitions for each memory-referencing instruction in a stream, ordered by the corresponding stream appearances in the trace. SBC reduces the size of SPEC CPU2000 Dinero instruction and data address traces from 18 to 309 times, outperforming the best trace compression techniques presented in the open literature. SBC can be successfully combined with general-purpose compression techniques. The combined SBC-gzip compression ratio is from 80 to 35,595, and the SBC-bzip2 compression ratio is from 75 to 191,257. Moreover, SBC outperforms other trace compression techniques when both decompression time and compression time are considered. This article also shows how the SBC algorithm can be modified for hardware implementation with very modest resources and only a minor loss in compression ratio.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,Modeling and Simulation

Reference35 articles.

1. Burger D. and Austin T. 1997. The SimpleScalar Tool Set version 2.0. CS-TR-97--1342 University of Wisconsin. 10.1145/268806.268810 Burger D. and Austin T. 1997. The SimpleScalar Tool Set version 2.0. CS-TR-97--1342 University of Wisconsin. 10.1145/268806.268810

Cited by 6 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. Data race detection on compressed traces;Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2018-10-26

2. Efficient Generation of Compact Execution Traces for Multicore Architectural Simulations;ACM Transactions on Architecture and Code Optimization;2017-09-06

3. Automated State-Dependent Importance Sampling for Markov Jump Processes via Sampling from the Zero-Variance Distribution;Journal of Applied Probability;2014-09

4. Efficient simulation of tail probabilities of sums of correlated lognormals;Annals of Operations Research;2009-10-28

5. Randomized Algorithms with Splitting: Why the Classic Randomized Algorithms Do Not Work and How to Make them Work;Methodology and Computing in Applied Probability;2009-03-27

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