Alexis Stamatakis

@stamatak.bsky.social

ERA Chair at Institute of Computer Science FORTH Research Group Leader Heidelberg Institute for Theoretical Studies Full Professor at Karlsruhe Institute of Technology Crete lab: https://www.biocomp.gr/ Heidelberg Lab: http://www.exelixis-lab.org/

As computer scientist, I am very excited about our new biogeography simulator: bigrig - my PostDoc Ben Bettisworth implemented two algoritmically totally distinct simulators from scratch to conduct statistical tests for showing that bigrig is correctly implemented: www.biorxiv.org/content/10.1...

bigrig: A range simulator for the DEC[+J] model

Quality software tools for science are, as a necessity, rigorously tested and verified. However, there is a major challenge to testing software used for phylogenetics and similar analysis. There is a ...

biorxiv.org

Check out our new preprint on reproducible parallel phylogenetic inference under varying core counts - it also includes a generic method for reproducible parallel associative reduction operations www.biorxiv.org/content/10.1...

Bit-Reproducible Phylogenetic Tree Inference under Varying Core-Counts via Reproducible Parallel Reduction Operators

Motivation: Phylogenetic trees describe the evolutionary history among biological species based on their genomic data. Maximum Likelihood (ML) based phylogenetic inference tools search for the tree and evolutionary model that best explain the observed genomic data. Given the independence of likelihood score calculations between different genomic sites, parallel computation is commonly deployed. This is followed by a parallel summation over the per-site scores to obtain the overall likelihood score of the tree. However, basic arithmetic operations on IEEE 754 floating-point numbers, such as addition and multiplication, inherently introduce rounding errors. Consequently, the order by which floating-point operations are executed affects the exact resulting likelihood value since these operations are not associative. Moreover, parallel reduction algorithms in numerical codes re-associate operations as a function of the core count and cluster network topology, inducing different round-off errors. These low-level deviations can cause heuristic searches to diverge and induce high-level result discrepancies (e.g., yield topologically distinct phylogenies). This effect has also been observed in multiple scientific fields, beyond phylogenetics. Results: We observe that varying the degree of parallelism results in diverging phylogenetic tree searches (high level results) for over 31 % out of 10 130 empirical datasets. More importantly, 8 % of these diverging datasets yield trees that are statistically significantly worse than the best known ML tree for the dataset (AU-test, p < 0.05). To alleviate this, we develop a variant of the widely used phylogenetic inference tool RAxML-NG, which does yield bit-reproducible results under varying core-counts, with a slowdown of only 0 to 12.7 % (median 0.8 %) on up to 768 cores. We further introduce the ReproRed reduction algorithm, which yields bit-identical results under varying core-counts, by maintaining a fixed operation order that is independent of the communication pattern. ReproRed is thus applicable to all associative reduction operations – in contrast to competitors, which are confined to summation. Our ReproRed reduction algorithm only exchanges the theoretical minimum number of messages, overlaps communication with computation, and utilizes fast base-cases for local reductions. ReproRed is able to all-reduce (via a subsequent broadcast) 4.1 · 106 operands across 48 to 768 cores in 19.7 to 48.61 µs, thereby exhibiting a slowdown of 13 to 93 % over a non-reproducible all-reduce algorithm. ReproRed outperforms the state-of-the-art reproducible all-reduction algorithm ReproBLAS (offers summation only) beyond 10 000 elements per core. In summary, we re-assess non-reproducibility in parallel phylogenetic inference, present the first bit-reproducible parallel phylogenetic inference tool, as well as introduce a general algorithm and open-source code for conducting reproducible associative parallel reduction operations. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, https://ror.org/0472cxd90, 882500 European Union, https://ror.org/019w4f821, 101087081

biorxiv.org

Another new term we invented together with a colleague is specimen-drain: the export of valuable biodiversity, ancient DNA, or other samples to countries of the global North that have money to process them coupled with losing the lead on the respective research papers

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Before somebody else comes up with it, here is a new term I invented: brain-redrain Definition: brains that return to their underdeveloped home country in the hope to help improving things but then leave again since they are frustrated because nothing will ever change there.

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