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MOTIVATION The computation of large phylogenetic trees with statistical models such as maximum likelihood or bayesian inference is computationally extremely intensive. It has repeatedly been demonstrated that these models are able to recover the true tree or a tree which is topologically closer to the true tree more frequently than less elaborate methods(More)
Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the Maximum Likelihood (ML) method is computationally extremely intensive. In order to accelerate computations we implemented RAxML-OMP, an efficient OpenMP-parallelization for Symmetric Multi-Processing machines (SMPs) based on the sequential program RAxML-V(More)
The computation of large phylogenetic trees with maximum likelihood is computationally intensive. In previous work we have introduced and implemented algorithmic optimizations in <b>PAxML.</b> The program shows run time improvements &gt; 25% over <b>parallel fastDNAml</b> yielding exactly the same results. This paper is focusing on computations of large(More)
Inference of phylogenetic trees comprising hundreds or even thousands of organisms based on the maximum likelihood method is computationally extremely expensive. We present simple new heuristics which yield accurate trees for synthetic (simulated) as well as real data and significantly reduce execution time. The new heuristics have been implemented in a(More)
The paper presents an operating system kernel for highly parallel supercomputers, which was implemented on an iPSC/2 Hypercube with 32 processors. The kernel offers a process model, which is well suited for most partitioning strategies of parallel algorithms. The base for the efficiency of this object oriented, global, and dynamic programming concept are(More)
—Today's computational science demands have resulted in ever larger parallel computers, and storage systems have grown to match these demands. Parallel file systems used in this environment are increasingly specialized to extract the highest possible performance for large I/O operations, at the expense of other potential workloads. While some applications(More)
Inference of large phylogenetic trees with statistical methods is computationally intensive. We recently introduced simple heuristics which yield accurate trees for synthetic as well as real data and are implemented in a sequential program called RAxML. We have demonstrated that RAxML outperforms the currently fastest statistical phylogeny programs(More)