Corrigendum: Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.

[This corrects the article DOI: 10.3389/fninf.2018.00002.].

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.

Massively parallel implementation of 3D-RISM calculation with volumetric 3D-FFT.

A new three-dimensional reference interaction site model (3D-RISM) program for massively parallel machines combined with the volumetric 3D fast Fourier transform (3D-FFT) was developed, and tested on the RIKEN K supercomputer. The ordinary parallel 3D-RISM program has a limitation on the number of parallelizations because of the limitations of the slab-type 3D-FFT. The volumetric 3D-FFT relieves this limitation drastically. We tested the 3D-RISM calculation on the large and fine calculation cell (2048(3) grid points) on 16,384 nodes, each having eight CPU cores. The new 3D-RISM program achieved excellent scalability to the parallelization, running on the RIKEN K supercomputer. As a benchmark application, we employed the program, combined with molecular dynamics simulation, to analyze the oligomerization process of chymotrypsin Inhibitor 2 mutant. The results demonstrate that the massive parallel 3D-RISM program is effective to analyze the hydration properties of the large biomolecular systems.

Gene sampling can bias multi-gene phylogenetic inferences: the relationship between red algae and green plants as a case study.

The monophyly of Plantae including glaucophytes, red algae, and green plants (green algae plus land plants) has been recovered in recent phylogenetic analyses of large multi-gene data sets (e.g., those including >30,000 amino acid [aa] positions). On the other hand, Plantae monophyly has not been stably reconstructed in inferences from multi-gene data sets with fewer than 10,000 aa positions. An analysis of 5,216 aa positions in Nozaki et al. (Nozaki H, Iseki M, Hasegawa M, Misawa K, Nakada T, Sasaki N, Watanabe M. 2007. Phylogeny of primary photosynthetic eukaryotes as deduced from slowly evolving nuclear genes. Mol Biol Evol. 24:1592-1595.) strongly rejected the monophyly of Plantae, whereas Hackett et al. (Hackett JD, Yoon HS, Li S, Reyes-Prieto A, Rummele SE, Bhattacharya D. 2007. Phylogenomic analysis supports the monophyly of cryptophytes and haptophytes and the association of rhizaria with chromalveolates. Mol Biol Evol. 24:1702-1713.) robustly recovered the Plantae clade in an analysis of 6,735 aa positions. We suspected that the significant incongruity observed between the two studies was attributable to a bias generally overlooked in multi-gene phylogenetic estimation, rather than data size, taxon sampling, or methods for tree reconstruction. Although glaucophytes were excluded from our analyses due to a shortage of sequence data, we found that the recovery of a sister-group relationship between red algae and green plants primarily depends on gene sampling in phylogenetic inferences from <10,000 aa positions. Phylogenetic analyses of data sets with fewer than 10,000 aa positions, which can be prepared without large-scale sequencing (e.g., expressed sequence tag analyses), are practical in challenging various unresolved issues in eukaryotic evolution. However, our results indicate that severe biases can arise from gene sampling in multi-gene inferences from <10,000 aa positions. We also address the validity of fast-evolving gene exclusion in multi-gene phylogenetic analyses, in light of this gene sampling bias.