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  1. Proceedings of the 2013 international symposium on memory management (ISMM '13)
  2. Rigorous benchmarking in reasonable time
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Safety-first approach to memory consistency models
Towards hinted collection: annotations for decreasing garbage collector pause times
Generating sound and effective memory debuggers
Precise and scalable context-sensitive pointer analysis via value flow graph
Adaptive scanning reduces sweep time for the Lisp2 mark-compact garbage collector
Rigorous benchmarking in reasonable time
Analyzing memory ownership patterns in C libraries
Control theory for principled heap sizing
ACDC: towards a universal mutator for benchmarking heap management systems
Elephant tracks: portable production of complete and precise gc traces
Pacman: program-assisted cache management
A bloat-aware design for big data applications

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Rigorous benchmarking in reasonable time

Content Provider ACM Digital Library
Author Kalibera, Tomas Jones, Richard
Abstract Experimental evaluation is key to systems research. Because modern systems are complex and non-deterministic, good experimental methodology demands that researchers account for uncertainty. To obtain valid results, they are expected to run many iterations of benchmarks, invoke virtual machines (VMs) several times, or even rebuild VM or benchmark binaries more than once. All this repetition costs time to complete experiments. Currently, many evaluations give up on sufficient repetition or rigorous statistical methods, or even run benchmarks only in training sizes. The results reported often lack proper variation estimates and, when a small difference between two systems is reported, some are simply unreliable. In contrast, we provide a statistically rigorous methodology for repetition and summarising results that makes efficient use of experimentation time. Time efficiency comes from two key observations. First, a given benchmark on a given platform is typically prone to much less non-determinism than the common worst-case of published corner-case studies. Second, repetition is most needed where most uncertainty arises (whether between builds, between executions or between iterations). We capture experimentation cost with a novel mathematical model, which we use to identify the number of repetitions at each level of an experiment necessary and sufficient to obtain a given level of precision. We present our methodology as a cookbook that guides researchers on the number of repetitions they should run to obtain reliable results. We also show how to present results with an effect size confidence interval. As an example, we show how to use our methodology to conduct throughput experiments with the DaCapo and SPEC CPU benchmarks on three recent platforms.
Starting Page 63
Ending Page 74
Page Count 12
File Format PDF
ISBN 9781450321006
DOI 10.1145/2464157.2464160
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-06-20
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Benchmarking methodology Dacapo Statistical methods Spec cpu
Content Type Text
Resource Type Article
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