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AxPcoords & parallel AxParafit: statistical co-phylogenetic analyses on thousands of taxa.

Stamatakis A, Auch AF, Meier-Kolthoff J, Göker M - BMC Bioinformatics (2007)

Bottom Line: The sophisticated statistical test for host-parasite co-phylogenetic analyses implemented in Parafit does not allow it to handle large datasets in reasonable times.Via optimization of the algorithm and the C code as well as integration of highly tuned BLAS and LAPACK methods AxParafit runs 5-61 times faster than Parafit with a lower memory footprint (up to 35% reduction) while the performance benefit increases with growing dataset size.We outline the substantial benefits of using parallel AxParafit by example of a large-scale empirical study on smut fungi and their host plants.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ecole Polytechnique Fédérale de Lausanne, School of Computer & Communication Sciences, Laboratory for Computational Biology and Bioinformatics STATION 14, CH-1015 Lausanne, Switzerland. Alexandros.Stamatakis@epfl.ch

ABSTRACT

Background: Current tools for Co-phylogenetic analyses are not able to cope with the continuous accumulation of phylogenetic data. The sophisticated statistical test for host-parasite co-phylogenetic analyses implemented in Parafit does not allow it to handle large datasets in reasonable times. The Parafit and DistPCoA programs are the by far most compute-intensive components of the Parafit analysis pipeline. We present AxParafit and AxPcoords (Ax stands for Accelerated) which are highly optimized versions of Parafit and DistPCoA respectively.

Results: Both programs have been entirely re-written in C. Via optimization of the algorithm and the C code as well as integration of highly tuned BLAS and LAPACK methods AxParafit runs 5-61 times faster than Parafit with a lower memory footprint (up to 35% reduction) while the performance benefit increases with growing dataset size. The MPI-based parallel implementation of AxParafit shows good scalability on up to 128 processors, even on medium-sized datasets. The parallel analysis with AxParafit on 128 CPUs for a medium-sized dataset with an 512 by 512 association matrix is more than 1,200/128 times faster per processor than the sequential Parafit run. AxPcoords is 8-26 times faster than DistPCoA and numerically stable on large datasets. We outline the substantial benefits of using parallel AxParafit by example of a large-scale empirical study on smut fungi and their host plants. To the best of our knowledge, this study represents the largest co-phylogenetic analysis to date.

Conclusion: The highly efficient AxPcoords and AxParafit programs allow for large-scale co-phylogenetic analyses on several thousands of taxa for the first time. In addition, AxParafit and AxPcoords have been integrated into the easy-to-use CopyCat tool.

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Speedup of Parallel AxParafit. Speedup of parallelized part and speedup for sequential plus parallel part of AxParParafit for a quadratic association matrix of size 512 on 4, 8, 16, 32, 64 and 128 CPUs.
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Figure 5: Speedup of Parallel AxParafit. Speedup of parallelized part and speedup for sequential plus parallel part of AxParParafit for a quadratic association matrix of size 512 on 4, 8, 16, 32, 64 and 128 CPUs.

Mentions: We assessed scalability of parallel AxParafit using the association matrix A of size 512 on 4, 8, 16, 32, 64, and 128 processors with p = 99. Figure 5 provides the speedup with respect to the number of worker processes. We indicate speedup values for the parallel part (SpeedupIndividual, computation of individual host-parasite links) as well as for the sequential plus the parallel part of the program (SpeedupWhole), i.e., we added the sequential computation time for the global test to the parallel execution time. On 128 processors the computation took only 50 seconds. An analysis of this dataset with the sequential version of Parafit would take approximately 20 hours.


AxPcoords & parallel AxParafit: statistical co-phylogenetic analyses on thousands of taxa.

Stamatakis A, Auch AF, Meier-Kolthoff J, Göker M - BMC Bioinformatics (2007)

Speedup of Parallel AxParafit. Speedup of parallelized part and speedup for sequential plus parallel part of AxParParafit for a quadratic association matrix of size 512 on 4, 8, 16, 32, 64 and 128 CPUs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2194794&req=5

Figure 5: Speedup of Parallel AxParafit. Speedup of parallelized part and speedup for sequential plus parallel part of AxParParafit for a quadratic association matrix of size 512 on 4, 8, 16, 32, 64 and 128 CPUs.
Mentions: We assessed scalability of parallel AxParafit using the association matrix A of size 512 on 4, 8, 16, 32, 64, and 128 processors with p = 99. Figure 5 provides the speedup with respect to the number of worker processes. We indicate speedup values for the parallel part (SpeedupIndividual, computation of individual host-parasite links) as well as for the sequential plus the parallel part of the program (SpeedupWhole), i.e., we added the sequential computation time for the global test to the parallel execution time. On 128 processors the computation took only 50 seconds. An analysis of this dataset with the sequential version of Parafit would take approximately 20 hours.

Bottom Line: The sophisticated statistical test for host-parasite co-phylogenetic analyses implemented in Parafit does not allow it to handle large datasets in reasonable times.Via optimization of the algorithm and the C code as well as integration of highly tuned BLAS and LAPACK methods AxParafit runs 5-61 times faster than Parafit with a lower memory footprint (up to 35% reduction) while the performance benefit increases with growing dataset size.We outline the substantial benefits of using parallel AxParafit by example of a large-scale empirical study on smut fungi and their host plants.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ecole Polytechnique Fédérale de Lausanne, School of Computer & Communication Sciences, Laboratory for Computational Biology and Bioinformatics STATION 14, CH-1015 Lausanne, Switzerland. Alexandros.Stamatakis@epfl.ch

ABSTRACT

Background: Current tools for Co-phylogenetic analyses are not able to cope with the continuous accumulation of phylogenetic data. The sophisticated statistical test for host-parasite co-phylogenetic analyses implemented in Parafit does not allow it to handle large datasets in reasonable times. The Parafit and DistPCoA programs are the by far most compute-intensive components of the Parafit analysis pipeline. We present AxParafit and AxPcoords (Ax stands for Accelerated) which are highly optimized versions of Parafit and DistPCoA respectively.

Results: Both programs have been entirely re-written in C. Via optimization of the algorithm and the C code as well as integration of highly tuned BLAS and LAPACK methods AxParafit runs 5-61 times faster than Parafit with a lower memory footprint (up to 35% reduction) while the performance benefit increases with growing dataset size. The MPI-based parallel implementation of AxParafit shows good scalability on up to 128 processors, even on medium-sized datasets. The parallel analysis with AxParafit on 128 CPUs for a medium-sized dataset with an 512 by 512 association matrix is more than 1,200/128 times faster per processor than the sequential Parafit run. AxPcoords is 8-26 times faster than DistPCoA and numerically stable on large datasets. We outline the substantial benefits of using parallel AxParafit by example of a large-scale empirical study on smut fungi and their host plants. To the best of our knowledge, this study represents the largest co-phylogenetic analysis to date.

Conclusion: The highly efficient AxPcoords and AxParafit programs allow for large-scale co-phylogenetic analyses on several thousands of taxa for the first time. In addition, AxParafit and AxPcoords have been integrated into the easy-to-use CopyCat tool.

Show MeSH