12,633 research outputs found
SPH-EXA: Enhancing the Scalability of SPH codes Via an Exascale-Ready SPH Mini-App
Numerical simulations of fluids in astrophysics and computational fluid
dynamics (CFD) are among the most computationally-demanding calculations, in
terms of sustained floating-point operations per second, or FLOP/s. It is
expected that these numerical simulations will significantly benefit from the
future Exascale computing infrastructures, that will perform 10^18 FLOP/s. The
performance of the SPH codes is, in general, adversely impacted by several
factors, such as multiple time-stepping, long-range interactions, and/or
boundary conditions. In this work an extensive study of three SPH
implementations SPHYNX, ChaNGa, and XXX is performed, to gain insights and to
expose any limitations and characteristics of the codes. These codes are the
starting point of an interdisciplinary co-design project, SPH-EXA, for the
development of an Exascale-ready SPH mini-app. We implemented a rotating square
patch as a joint test simulation for the three SPH codes and analyzed their
performance on a modern HPC system, Piz Daint. The performance profiling and
scalability analysis conducted on the three parent codes allowed to expose
their performance issues, such as load imbalance, both in MPI and OpenMP.
Two-level load balancing has been successfully applied to SPHYNX to overcome
its load imbalance. The performance analysis shapes and drives the design of
the SPH-EXA mini-app towards the use of efficient parallelization methods,
fault-tolerance mechanisms, and load balancing approaches.Comment: arXiv admin note: substantial text overlap with arXiv:1809.0801
Symbolic Tensor Calculus -- Functional and Dynamic Approach
In this paper, we briefly discuss the dynamic and functional approach to
computer symbolic tensor analysis. The ccgrg package for Wolfram
Language/Mathematica is used to illustrate this approach. Some examples of
applications are attached
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
Review of analytical instruments for EEG analysis
Since it was first used in 1926, EEG has been one of the most useful
instruments of neuroscience. In order to start using EEG data we need not only
EEG apparatus, but also some analytical tools and skills to understand what our
data mean. This article describes several classical analytical tools and also
new one which appeared only several years ago. We hope it will be useful for
those researchers who have only started working in the field of cognitive EEG
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