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supercomputer but have been held back by the demands of parallel programming in C
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supercomputer but have been held back by the demands of parallel programming in C
http://www-106.ibm.com/developerworks/library/j-super.html?open&l=766,t=gr,p=jSuperC
Over the past three years, parallel clustering has begun to change the face of supercomputing. Where once the monolithic multimillion-dollar machine ruled, the parallel cluster is fast becoming the supercomputer of choice. Predictably, enthusiasm in open-source circles has spawned hundreds -- if not thousands -- of parallel clustering projects. The first and most well-known open-source clustering system is Beowulf. Launched in 1994 by Thomas Sterling and Donald Becker under the auspices of NASA, Beowulf started out as a 16-node demonstration cluster. Today, there are hundreds of implementations of Beowulf, ranging from the Oak Ridge National Laboratory's Stone SouperComputer to Aspen Systems Inc.'s custom-built commercial clusters (see Resources).
Unfortunately for Java programmers, most clustering systems are built around C-based software messaging APIs such as Message Passing Interface (MPI) or Parallel Virtual Machine (PVM). Parallel programming in C is no easy task, so I have devised a workaround. In this article, I will show you how to use a combination of Java threads and Java Remote Method Invocation (RMI) to create your own Java-based supercomputer Java > Tips and Tutorials > Miscellaneous Oct 12, 2006
Unfortunately for Java programmers, most clustering systems are built around C-based software messaging APIs such as Message Passing Interface (MPI) or Parallel Virtual Machine (PVM). Parallel programming in C is no easy task, so I have devised a workaround. In this article, I will show you how to use a combination of Java threads and Java Remote Method Invocation (RMI) to create your own Java-based supercomputer Java > Tips and Tutorials > Miscellaneous Oct 12, 2006
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Structural Analysis for JavaTM (SA4J) is a technology that analyzes structural dependencies of Java applications in order to measure their stability. It detects structural "anti-patterns" (suspicious design elements) and provides dependency web browsing for detailed exploration of anti-patterns in the dependency web. SA4J also enables "what if" analysis in order to assess the impact of change on the functionality of the application; and it offers guidelines for package re-factoring.
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If you're looking for a unique programming challenge, try your hand at building a management application for a distributed, cross-platform network. Consider, for example, what it takes to build a storage network. Network switch and hub technology is typically purchased from one vendor, storage appliances from another vendor, and file servers and software from yet another set of vendors. And then it's up to you to make sure they all work together, with nary a hiccup. Luckily, two technologies have the potential to vastly improve the current state of affairs. This article is the first in a three-part series that looks at how Sun Microsystems's Jiro technology and the Distributed Management Task Force's Web-Based Enterprise Management Initiative (WBEM) can simplify the creation of management applications for heterogeneous environments. Author Paul Monday launches the series this week with a beginner's introduction to the Federated Management Architecture and Jiro technology.
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Here is a short article that may help you while creating a struts project in Eclipse's latest 3.30 i.e Ganymede (I wonder what they will do when all the satellites' names are used
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The microdevices that J2ME targets have 16- or 32-bit processors and a minimum total memory footprint of approximately 128 KB. They conform to a Connected Limited Device Configuration (CLDC) while maintaining the Java tradition of anytime, anywhere code portability, deployment flexibility, safe network delivery, and code stability. The prerequisite for the J2ME CLDC is a stripped-down JVM, called the K Virtual Machine (KVM). The KVM is designed for small-memory, resource-constrained, network-connected devices.
Another J2ME configuration, the Connected Device Configuration (CDC), targets advanced consumer electronic and embedded devices such as smart communicators, advanced "smart" pagers, smart personal digital assistants (PDAs), and interactive digital television set-top boxes. Typically, these devices run a 32-bit microprocessor/controller and have more than 2 MB of total memory for the storage of the virtual machine and libraries.
Another J2ME configuration, the Connected Device Configuration (CDC), targets advanced consumer electronic and embedded devices such as smart communicators, advanced "smart" pagers, smart personal digital assistants (PDAs), and interactive digital television set-top boxes. Typically, these devices run a 32-bit microprocessor/controller and have more than 2 MB of total memory for the storage of the virtual machine and libraries.
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Over the past three years, parallel clustering has begun to change the face of supercomputing. Where once the monolithic multimillion-dollar machine ruled, the parallel cluster is fast becoming the supercomputer of choice. Predictably, enthusiasm in open-source circles has spawned hundreds -- if not thousands -- of parallel clustering projects. The first and most well-known open-source clustering system is Beowulf. Launched in 1994 by Thomas Sterling and Donald Becker under the auspices of NASA, Beowulf started out as a 16-node demonstration cluster. Today, there are hundreds of implementations of Beowulf, ranging from the Oak Ridge National Laboratory's Stone SouperComputer to Aspen Systems Inc.'s custom-built commercial clusters (see Resources).
Unfortunately for Java programmers, most clustering systems are built around C-based software messaging APIs such as Message Passing Interface (MPI) or Parallel Virtual Machine (PVM). Parallel programming in C is no easy task, so I have devised a workaround. In this article, I will show you how to use a combination of Java threads and Java Remote Method Invocation (RMI) to create your own Java-based supercomputer
Unfortunately for Java programmers, most clustering systems are built around C-based software messaging APIs such as Message Passing Interface (MPI) or Parallel Virtual Machine (PVM). Parallel programming in C is no easy task, so I have devised a workaround. In this article, I will show you how to use a combination of Java threads and Java Remote Method Invocation (RMI) to create your own Java-based supercomputer
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