Analyzing algorithms by simulation: variance reduction techniques and simulation speedups
ACM Computing Surveys (CSUR)
The influence of caches on the performance of heaps
Journal of Experimental Algorithmics (JEA)
A few logs suffice to build (almost) all trees (l): part I
Random Structures & Algorithms
The influence of caches on the performance of sorting
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Cache performance analysis of traversals and random accesses
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Matrix multiplication: a case study of enhanced data cache utilization
Journal of Experimental Algorithmics (JEA)
Hybrid tree reconstruction methods
Journal of Experimental Algorithmics (JEA)
Absolute convergence: true trees from short sequences
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Performance study of phylogenetic methods: (unweighted) quartet methods and neighbor-joining
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Inferring evolutionary trees with strong combinatorial evidence
Theoretical Computer Science - computing and combinatorics
Improving memory performance of sorting algorithms
Journal of Experimental Algorithmics (JEA)
Steps toward accurate reconstructions of phylogenies from gene-order data
Journal of Computer and System Sciences - Computational biology 2002
High-Performance Algorithm Engineering for Computational Phylogenetics
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Quartet Cleaning: Improved Algorithms and Simulations
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
High-Performance Algorithm Engineering for Computational Phylogenetics
The Journal of Supercomputing - Special issue on computational issues in fluid dynamics optimization and simulation
Rec-I-DCM3: A Fast Algorithmic Technique for Reconstructing Large Phylogenetic Trees
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Testing convexity properties of tree colorings
STACS'07 Proceedings of the 24th annual conference on Theoretical aspects of computer science
Improving inference of transcriptional regulatory networks based on network evolutionary models
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
ProPhyC: a probabilistic phylogenetic model for refining regulatory networks
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Refining Regulatory Networks through Phylogenetic Transfer of Information
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The benefits of experimental algorithmics and algorithm engineering need to be extended to applications in the computational sciences. In this paper, we present on one such application: the reconstruction of evolutionary histories (phylogenies) from molecular data such as DNA sequences. Our presentation is not a survey of past and current work in the area, but rather a discussion of what we see as some of the important challenges in experimental algorithmics that arise from computational phylogenetics. As motivational examples or examples of possible approaches, we briefly discuss two specific uses of algorithm engineering and of experimental algorithmics from our recent research. The first such use focused on speed: we reimplemented Sankoff and Blanchette's breakpoint analysis and obtained a 200, 000-fold speedup for serial code and 108-fold speedup on a 512-processor supercluster. We report here on the techniques used in obtaining such a speedup. The second use focused on experimentation: we conducted an extensive study of quartet-based reconstruction algorithms within a parameter-rich simulation space, using several hundred CPU-years of computation. We report here on the challenges involved in designing, conducting, and assessing such a study.