10,100 research outputs found
Multidisciplinary computational aerosciences
As the challenges of single disciplinary computational physics are met, such as computational fluid dynamics, computational structural mechanics, computational propulsion, computational aeroacoustics, computational electromagnetics, etc., scientists have begun investigating the combination of these single disciplines into what is being called multidisciplinary computational aerosciences (MCAS). The combination of several disciplines not only offers simulation realism but also formidable computational challenges. The solution of such problems will require computers orders of magnitude larger than those currently available. Such computer power can only be supplied by massively parallel machines because of the current speed-of-light limitation of conventional serial systems. Even with such machines, MCAS problems will require hundreds of hours for their solution. To efficiently utilize such a machine, research is required in three areas that include parallel architectures, systems software, and applications software. The main emphasis of this paper is the applications software element. Examples that demonstrate application software for multidisciplinary problems currently being solved at NASA Ames Research Center are presented. Pacing items for MCAS are discussed such as solution methodology, physical modeling, computer power, and multidisciplinary validation experiments
First Evaluation of the CPU, GPGPU and MIC Architectures for Real Time Particle Tracking based on Hough Transform at the LHC
Recent innovations focused around {\em parallel} processing, either through
systems containing multiple processors or processors containing multiple cores,
hold great promise for enhancing the performance of the trigger at the LHC and
extending its physics program. The flexibility of the CMS/ATLAS trigger system
allows for easy integration of computational accelerators, such as NVIDIA's
Tesla Graphics Processing Unit (GPU) or Intel's \xphi, in the High Level
Trigger. These accelerators have the potential to provide faster or more energy
efficient event selection, thus opening up possibilities for new complex
triggers that were not previously feasible. At the same time, it is crucial to
explore the performance limits achievable on the latest generation multicore
CPUs with the use of the best software optimization methods. In this article, a
new tracking algorithm based on the Hough transform will be evaluated for the
first time on a multi-core Intel Xeon E5-2697v2 CPU, an NVIDIA Tesla K20c GPU,
and an Intel \xphi\ 7120 coprocessor. Preliminary time performance will be
presented.Comment: 13 pages, 4 figures, Accepted to JINS
Analysing Astronomy Algorithms for GPUs and Beyond
Astronomy depends on ever increasing computing power. Processor clock-rates
have plateaued, and increased performance is now appearing in the form of
additional processor cores on a single chip. This poses significant challenges
to the astronomy software community. Graphics Processing Units (GPUs), now
capable of general-purpose computation, exemplify both the difficult
learning-curve and the significant speedups exhibited by massively-parallel
hardware architectures. We present a generalised approach to tackling this
paradigm shift, based on the analysis of algorithms. We describe a small
collection of foundation algorithms relevant to astronomy and explain how they
may be used to ease the transition to massively-parallel computing
architectures. We demonstrate the effectiveness of our approach by applying it
to four well-known astronomy problems: Hogbom CLEAN, inverse ray-shooting for
gravitational lensing, pulsar dedispersion and volume rendering. Algorithms
with well-defined memory access patterns and high arithmetic intensity stand to
receive the greatest performance boost from massively-parallel architectures,
while those that involve a significant amount of decision-making may struggle
to take advantage of the available processing power.Comment: 10 pages, 3 figures, accepted for publication in MNRA
GPU Acceleration of Image Convolution using Spatially-varying Kernel
Image subtraction in astronomy is a tool for transient object discovery such
as asteroids, extra-solar planets and supernovae. To match point spread
functions (PSFs) between images of the same field taken at different times a
convolution technique is used. Particularly suitable for large-scale images is
a computationally intensive spatially-varying kernel. The underlying algorithm
is inherently massively parallel due to unique kernel generation at every pixel
location. The spatially-varying kernel cannot be efficiently computed through
the Convolution Theorem, and thus does not lend itself to acceleration by Fast
Fourier Transform (FFT). This work presents results of accelerated
implementation of the spatially-varying kernel image convolution in multi-cores
with OpenMP and graphic processing units (GPUs). Typical speedups over ANSI-C
were a factor of 50 and a factor of 1000 over the initial IDL implementation,
demonstrating that the techniques are a practical and high impact path to
terabyte-per-night image pipelines and petascale processing.Comment: 4 pages. Accepted to IEEE-ICIP 201
CERN openlab Whitepaper on Future IT Challenges in Scientific Research
This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates
Report from the MPP Working Group to the NASA Associate Administrator for Space Science and Applications
NASA's Office of Space Science and Applications (OSSA) gave a select group of scientists the opportunity to test and implement their computational algorithms on the Massively Parallel Processor (MPP) located at Goddard Space Flight Center, beginning in late 1985. One year later, the Working Group presented its report, which addressed the following: algorithms, programming languages, architecture, programming environments, the way theory relates, and performance measured. The findings point to a number of demonstrated computational techniques for which the MPP architecture is ideally suited. For example, besides executing much faster on the MPP than on conventional computers, systolic VLSI simulation (where distances are short), lattice simulation, neural network simulation, and image problems were found to be easier to program on the MPP's architecture than on a CYBER 205 or even a VAX. The report also makes technical recommendations covering all aspects of MPP use, and recommendations concerning the future of the MPP and machines based on similar architectures, expansion of the Working Group, and study of the role of future parallel processors for space station, EOS, and the Great Observatories era
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