10,333 research outputs found
Object-oriented implementations of the MPDATA advection equation solver in C++, Python and Fortran
Three object-oriented implementations of a prototype solver of the advection
equation are introduced. The presented programs are based on Blitz++ (C++),
NumPy (Python), and Fortran's built-in array containers. The solvers include an
implementation of the Multidimensional Positive-Definite Advective Transport
Algorithm (MPDATA). The introduced codes exemplify how the application of
object-oriented programming (OOP) techniques allows to reproduce the
mathematical notation used in the literature within the program code. A
discussion on the tradeoffs of the programming language choice is presented.
The main angles of comparison are code brevity and syntax clarity (and hence
maintainability and auditability) as well as performance. In the case of
Python, a significant performance gain is observed when switching from the
standard interpreter (CPython) to the PyPy implementation of Python. Entire
source code of all three implementations is embedded in the text and is
licensed under the terms of the GNU GPL license
Object-oriented construction of a multigrid electronic-structure code with Fortran 90
We describe the object-oriented implementation of a higher-order
finite-difference density-functional code in Fortran 90. Object-oriented models
of grid and related objects are constructed and employed for the implementation
of an efficient one-way multigrid method we have recently proposed for the
density-functional electronic-structure calculations. Detailed analysis of
performance and strategy of the one-way multigrid scheme will be presented.Comment: 24 pages, 6 figures, to appear in Comput. Phys. Com
Plyades: A Python Library for Space Mission Design
Plyades: A Python Library for Space Mission Design Designing a space mission
is a computation-heavy task. Software tools that conduct the necessary
numerical simulations and optimizations are therefore indispensable. The
usability of existing software, written in Fortran and MATLAB, suffers because
of high complexity, low levels of abstraction and out-dated programming
practices. We propose Python as a viable alternative for astrodynamics tools
and demonstrate the proof-of-concept library Plyades which combines powerful
features with Pythonic ease of use
Using Java for distributed computing in the Gaia satellite data processing
In recent years Java has matured to a stable easy-to-use language with the
flexibility of an interpreter (for reflection etc.) but the performance and
type checking of a compiled language. When we started using Java for
astronomical applications around 1999 they were the first of their kind in
astronomy. Now a great deal of astronomy software is written in Java as are
many business applications.
We discuss the current environment and trends concerning the language and
present an actual example of scientific use of Java for high-performance
distributed computing: ESA's mission Gaia. The Gaia scanning satellite will
perform a galactic census of about 1000 million objects in our galaxy. The Gaia
community has chosen to write its processing software in Java. We explore the
manifold reasons for choosing Java for this large science collaboration.
Gaia processing is numerically complex but highly distributable, some parts
being embarrassingly parallel. We describe the Gaia processing architecture and
its realisation in Java. We delve into the astrometric solution which is the
most advanced and most complex part of the processing. The Gaia simulator is
also written in Java and is the most mature code in the system. This has been
successfully running since about 2005 on the supercomputer "Marenostrum" in
Barcelona. We relate experiences of using Java on a large shared machine.
Finally we discuss Java, including some of its problems, for scientific
computing.Comment: Experimental Astronomy, August 201
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
A Test Suite for High-Performance Parallel Java
The Java programming language has a number of features that make it attractive for writing high-quality, portable parallel programs. A pure object formulation, strong typing and the exception model make programs easier to create, debug, and maintain. The elegant threading provides a simple route to parallelism on shared-memory machines. Anticipating great improvements in numerical performance, this paper presents a suite of simple programs that indicate how a pure Java Navier-Stokes solver might perform. The suite includes a parallel Euler solver. We present results from a 32-processor Hewlett-Packard machine and a 4-processor Sun server. While speedup is excellent on both machines, indicating a high-quality thread scheduler, the single-processor performance needs much improvement
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