2,865 research outputs found
An Extensible Timing Infrastructure for Adaptive Large-scale Applications
Real-time access to accurate and reliable timing information is necessary to
profile scientific applications, and crucial as simulations become increasingly
complex, adaptive, and large-scale. The Cactus Framework provides flexible and
extensible capabilities for timing information through a well designed
infrastructure and timing API. Applications built with Cactus automatically
gain access to built-in timers, such as gettimeofday and getrusage,
system-specific hardware clocks, and high-level interfaces such as PAPI. We
describe the Cactus timer interface, its motivation, and its implementation. We
then demonstrate how this timing information can be used by an example
scientific application to profile itself, and to dynamically adapt itself to a
changing environment at run time
From Tensor Equations to Numerical Code -- Computer Algebra Tools for Numerical Relativity
In this paper we present our recent work in developing a computer-algebra
tool for systems of partial differential equations (PDEs), termed "Kranc". Our
work is motivated by the problem of finding solutions of the Einstein equations
through numerical simulations. Kranc consists of Mathematica based
computer-algebra packages, that facilitate the task of dealing with symbolic
tensorial calculations and realize the conversion of systems of partial
differential evolution equations into parallelized C or Fortran code.Comment: LaTeX llncs style, 9 pages, 1 figure, to appaer in the proceedings of
"SYNASC 2004 - 6th International Symposium on Symbolic and Numeric Algorithms
for Scientific Computing", Timisoara, Romania, September 26-30 200
From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation
Starting from a high-level problem description in terms of partial
differential equations using abstract tensor notation, the Chemora framework
discretizes, optimizes, and generates complete high performance codes for a
wide range of compute architectures. Chemora extends the capabilities of
Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient
manner for complex applications, without low-level code tuning. Chemora
achieves parallelism through MPI and multi-threading, combining OpenMP and
CUDA. Optimizations include high-level code transformations, efficient loop
traversal strategies, dynamically selected data and instruction cache usage
strategies, and JIT compilation of GPU code tailored to the problem
characteristics. The discretization is based on higher-order finite differences
on multi-block domains. Chemora's capabilities are demonstrated by simulations
of black hole collisions. This problem provides an acid test of the framework,
as the Einstein equations contain hundreds of variables and thousands of terms.Comment: 18 pages, 4 figures, accepted for publication in Scientific
Programmin
AstroGrid-D: Grid Technology for Astronomical Science
We present status and results of AstroGrid-D, a joint effort of
astrophysicists and computer scientists to employ grid technology for
scientific applications. AstroGrid-D provides access to a network of
distributed machines with a set of commands as well as software interfaces. It
allows simple use of computer and storage facilities and to schedule or monitor
compute tasks and data management. It is based on the Globus Toolkit middleware
(GT4). Chapter 1 describes the context which led to the demand for advanced
software solutions in Astrophysics, and we state the goals of the project. We
then present characteristic astrophysical applications that have been
implemented on AstroGrid-D in chapter 2. We describe simulations of different
complexity, compute-intensive calculations running on multiple sites, and
advanced applications for specific scientific purposes, such as a connection to
robotic telescopes. We can show from these examples how grid execution improves
e.g. the scientific workflow. Chapter 3 explains the software tools and
services that we adapted or newly developed. Section 3.1 is focused on the
administrative aspects of the infrastructure, to manage users and monitor
activity. Section 3.2 characterises the central components of our architecture:
The AstroGrid-D information service to collect and store metadata, a file
management system, the data management system, and a job manager for automatic
submission of compute tasks. We summarise the successfully established
infrastructure in chapter 4, concluding with our future plans to establish
AstroGrid-D as a platform of modern e-Astronomy.Comment: 14 pages, 12 figures Subjects: data analysis, image processing,
robotic telescopes, simulations, grid. Accepted for publication in New
Astronom
A Massive Data Parallel Computational Framework for Petascale/Exascale Hybrid Computer Systems
Heterogeneous systems are becoming more common on High Performance Computing
(HPC) systems. Even using tools like CUDA and OpenCL it is a non-trivial task
to obtain optimal performance on the GPU. Approaches to simplifying this task
include Merge (a library based framework for heterogeneous multi-core systems),
Zippy (a framework for parallel execution of codes on multiple GPUs), BSGP (a
new programming language for general purpose computation on the GPU) and
CUDA-lite (an enhancement to CUDA that transforms code based on annotations).
In addition, efforts are underway to improve compiler tools for automatic
parallelization and optimization of affine loop nests for GPUs and for
automatic translation of OpenMP parallelized codes to CUDA.
In this paper we present an alternative approach: a new computational
framework for the development of massively data parallel scientific codes
applications suitable for use on such petascale/exascale hybrid systems built
upon the highly scalable Cactus framework. As the first non-trivial
demonstration of its usefulness, we successfully developed a new 3D CFD code
that achieves improved performance.Comment: Parallel Computing 2011 (ParCo2011), 30 August -- 2 September 2011,
Ghent, Belgiu
Numerical Relativity As A Tool For Computational Astrophysics
The astrophysics of compact objects, which requires Einstein's theory of
general relativity for understanding phenomena such as black holes and neutron
stars, is attracting increasing attention. In general relativity, gravity is
governed by an extremely complex set of coupled, nonlinear, hyperbolic-elliptic
partial differential equations. The largest parallel supercomputers are finally
approaching the speed and memory required to solve the complete set of
Einstein's equations for the first time since they were written over 80 years
ago, allowing one to attempt full 3D simulations of such exciting events as
colliding black holes and neutron stars. In this paper we review the
computational effort in this direction, and discuss a new 3D multi-purpose
parallel code called ``Cactus'' for general relativistic astrophysics.
Directions for further work are indicated where appropriate.Comment: Review for JCA
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