6,530 research outputs found
Advanced Message Routing for Scalable Distributed Simulations
The Joint Forces Command (JFCOM) Experimentation Directorate (J9)'s recent Joint Urban Operations (JUO)
experiments have demonstrated the viability of Forces Modeling and Simulation in a distributed environment. The
JSAF application suite, combined with the RTI-s communications system, provides the ability to run distributed
simulations with sites located across the United States, from Norfolk, Virginia to Maui, Hawaii. Interest-aware
routers are essential for communications in the large, distributed environments, and the current RTI-s framework
provides such routers connected in a straightforward tree topology. This approach is successful for small to medium
sized simulations, but faces a number of significant limitations for very large simulations over high-latency, wide
area networks. In particular, traffic is forced through a single site, drastically increasing distances messages must
travel to sites not near the top of the tree. Aggregate bandwidth is limited to the bandwidth of the site hosting the
top router, and failures in the upper levels of the router tree can result in widespread communications losses
throughout the system.
To resolve these issues, this work extends the RTI-s software router infrastructure to accommodate more
sophisticated, general router topologies, including both the existing tree framework and a new generalization of the
fully connected mesh topologies used in the SF Express ModSAF simulations of 100K fully interacting vehicles.
The new software router objects incorporate the scalable features of the SF Express design, while optionally using
low-level RTI-s objects to perform actual site-to-site communications. The (substantial) limitations of the original
mesh router formalism have been eliminated, allowing fully dynamic operations. The mesh topology capabilities
allow aggregate bandwidth and site-to-site latencies to match actual network performance. The heavy resource load at
the root node can now be distributed across routers at the participating sites
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&
Performance comparison of point and spatial access methods
In the past few years a large number of multidimensional point access methods, also called
multiattribute index structures, has been suggested, all of them claiming good performance. Since no
performance comparison of these structures under arbitrary (strongly correlated nonuniform, short
"ugly") data distributions and under various types of queries has been performed, database
researchers and designers were hesitant to use any of these new point access methods. As shown in
a recent paper, such point access methods are not only important in traditional database applications.
In new applications such as CAD/CIM and geographic or environmental information systems, access
methods for spatial objects are needed. As recently shown such access methods are based on point
access methods in terms of functionality and performance. Our performance comparison naturally
consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in
part I I spatial access methods for rectangles will be compared. In part I we present a survey and
classification of existing point access methods. Then we carefully select the following four methods
for implementation and performance comparison under seven different data files (distributions) and
various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called
the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the
BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is
robust under ugly data and queries. In part I I we compare spatial access methods for rectangles.
After presenting a survey and classification of existing spatial access methods we carefully selected
the following four methods for implementation and performance comparison under six different data
files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the
BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree.
This comparison is a first step towards a standardized testbed or benchmark. We offer our data and
query files to each designer of a new point or spatial access method such that he can run his
implementation in our testbed
Components and Interfaces of a Process Management System for Parallel Programs
Parallel jobs are different from sequential jobs and require a different type
of process management. We present here a process management system for parallel
programs such as those written using MPI. A primary goal of the system, which
we call MPD (for multipurpose daemon), is to be scalable. By this we mean that
startup of interactive parallel jobs comprising thousands of processes is
quick, that signals can be quickly delivered to processes, and that stdin,
stdout, and stderr are managed intuitively. Our primary target is parallel
machines made up of clusters of SMPs, but the system is also useful in more
tightly integrated environments. We describe how MPD enables much faster
startup and better runtime management of parallel jobs. We show how close
control of stdio can support the easy implementation of a number of convenient
system utilities, even a parallel debugger. We describe a simple but general
interface that can be used to separate any process manager from a parallel
library, which we use to keep MPD separate from MPICH.Comment: 12 pages, Workshop on Clusters and Computational Grids for Scientific
Computing, Sept. 24-27, 2000, Le Chateau de Faverges de la Tour, Franc
Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks
In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach
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