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Presenting Data from Experiments in Algorithmics

by Peter Sanders ,  Max Planck ,  Insitut Informatik
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Abstract:

Experimental algorithmics yields large amounts of data that depends on many parameters. This paper collects a number of rules for presenting this data in concise, meaningful, understandable diagrams that have sufficiently high quality to be printed in scientific journals.

Citations

709 The Visual Display of Quantitative Information – Tufte - 2001
301 Mersenne twister: a 623-dimensionally equidistributed uniform pseudorandom number generator – Matsumoto, Nishimura - 1998
180 The Elements of Graphing Data – Cleveland - 1985
173 Balanced allocations – Azar, Broder, et al. - 1999
151 Graphic Methods for Data Analysis – Chambers, Cleveland, et al. - 1983
37 Fast Priority Queues for Cached Memory – Sanders
35 Balanced allocations: the heavily loaded case – Berenbrink, Czumaj, et al. - 2005
32 Towards a discipline of experimental algorithmics – Moret - 2001
12 Asynchronous scheduling of redundant disk arrays – Sanders - 2000
10 A Bandwidth Latency Tradeoff for Broadcast and – Sanders, Sibeyn - 2003
9 Lastverteilungsalgorithmen fur parallele Tiefensuche. Number 463 – Sanders - 1997
7 How to present a paper on experimental work with algorithms – McGeoch, Moret - 1999
4 Parallel integer optimization for crew scheduling – Alefragis, Sanders, et al. - 2000
4 How to find big-oh in your data set (and how not to – McGeoch, Precup, et al. - 1997
3 Asymptotic complexity from experiments? A case study for randomized algorithms – Sanders, Fleischer - 2000
1 Pet peeves and pitfalls in the experimental analysis of algorithms. Paper underlying talk on Dagstuhl Seminar on Experimental Algorithmics – Johnson - 2000