858 research outputs found
Interactive Visualization of the Largest Radioastronomy Cubes
3D visualization is an important data analysis and knowledge discovery tool,
however, interactive visualization of large 3D astronomical datasets poses a
challenge for many existing data visualization packages. We present a solution
to interactively visualize larger-than-memory 3D astronomical data cubes by
utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the
data volume into smaller sub-volumes that are distributed over the rendering
workstations. A GPU-based ray casting volume rendering is performed to generate
images for each sub-volume, which are composited to generate the whole volume
output, and returned to the user. Datasets including the HI Parkes All Sky
Survey (HIPASS - 12 GB) southern sky and the Galactic All Sky Survey (GASS - 26
GB) data cubes were used to demonstrate our framework's performance. The
framework can render the GASS data cube with a maximum render time < 0.3 second
with 1024 x 1024 pixels output resolution using 3 rendering workstations and 8
GPUs. Our framework will scale to visualize larger datasets, even of Terabyte
order, if proper hardware infrastructure is available.Comment: 15 pages, 12 figures, Accepted New Astronomy July 201
Assessing the Utility of a Personal Desktop Cluster
The computer workstation, introduced by Sun Microsystems in 1982, was the tool of
choice for scientists and engineers as an interactive computing environment for the development
of scientific codes. However, by the mid-1990s, the performance of workstations
began to lag behind high-end commodity PCs. This, coupled with the disappearance of
BSD-based operating systems in workstations and the emergence of Linux as an opensource
operating system for PCs, arguably led to the demise of the workstation as we
knew it.
Around the same time, computational scientists started to leverage PCs running
Linux to create a commodity-based (Beowulf) cluster that provided dedicated compute
cycles, i.e., supercomputing for the rest of us, as a cost-effective alternative to large
supercomputers, i.e., supercomputing for the few. However, as the cluster movement
has matured, with respect to cluster hardware and open-source software, these clusters
have become much more like their large-scale supercomputing brethren — a shared
datacenter resource that resides in a machine room.
Consequently, the above observations, when coupled with the ever-increasing performance
gap between the PC and cluster supercomputer, provide the motivation for a
personal desktop cluster workstation — a turnkey solution that provides an interactive and parallel computing environment with the approximate form factor of a Sun SPARCstation
1 “pizza box” workstation. In this paper, we present the hardware and software
architecture of such a solution as well as its prowess as a developmental platform for parallel codes. In short, imagine a 12-node personal desktop cluster that achieves 14 Gflops on Linpack but sips only 150-180 watts of power, resulting in a performance-power ratio that is over 300% better than our test SMP platform
State-of-the-Art in Parallel Computing with R
R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly useful for general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems four different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix
The Astrophysical Multipurpose Software Environment
We present the open source Astrophysical Multi-purpose Software Environment
(AMUSE, www.amusecode.org), a component library for performing astrophysical
simulations involving different physical domains and scales. It couples
existing codes within a Python framework based on a communication layer using
MPI. The interfaces are standardized for each domain and their implementation
based on MPI guarantees that the whole framework is well-suited for distributed
computation. It includes facilities for unit handling and data storage.
Currently it includes codes for gravitational dynamics, stellar evolution,
hydrodynamics and radiative transfer. Within each domain the interfaces to the
codes are as similar as possible. We describe the design and implementation of
AMUSE, as well as the main components and community codes currently supported
and we discuss the code interactions facilitated by the framework.
Additionally, we demonstrate how AMUSE can be used to resolve complex
astrophysical problems by presenting example applications.Comment: 23 pages, 25 figures, accepted for A&
MPICH-G2: A Grid-Enabled Implementation of the Message Passing Interface
Application development for distributed computing "Grids" can benefit from
tools that variously hide or enable application-level management of critical
aspects of the heterogeneous environment. As part of an investigation of these
issues, we have developed MPICH-G2, a Grid-enabled implementation of the
Message Passing Interface (MPI) that allows a user to run MPI programs across
multiple computers, at the same or different sites, using the same commands
that would be used on a parallel computer. This library extends the Argonne
MPICH implementation of MPI to use services provided by the Globus Toolkit for
authentication, authorization, resource allocation, executable staging, and
I/O, as well as for process creation, monitoring, and control. Various
performance-critical operations, including startup and collective operations,
are configured to exploit network topology information. The library also
exploits MPI constructs for performance management; for example, the MPI
communicator construct is used for application-level discovery of, and
adaptation to, both network topology and network quality-of-service mechanisms.
We describe the MPICH-G2 design and implementation, present performance
results, and review application experiences, including record-setting
distributed simulations.Comment: 20 pages, 8 figure
Cluster Computing and Performance Measurement
There is a continual demand for greater computational power from computer systems
than is currently possible. Areas requiring great computational speed include numerical
simulation of scientific and engineering problems. Such problems often need huge
quantities of repetitive calculations on large amount of data to give valid results.
Cluster computing offers many advantages as a highly cost-effective and often scalable
approach for high-performance computing in general. To achieve the full potential of
high performance computing systems, centralized configuration services are the starting
point. For a large scale of projects, cluster computing is required where it is supposed
to be optimized for the system topology and management of the project. This paper
presents the consequences of using cluster computing and performance management
and the consequences without this technology. The experimental results of this paper
highlight the affects of the design of this service and provide a comprehensive
performance analysis of the project
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