By Jerome Verbeke, Neelakanth Nadgir, Greg Ruetsch, Ilya Sharapov (auth.), Manish Parashar (eds.)
The progress of the net and the supply of strong desktops and hello- velocity networks as reasonably cheap commodity elements are altering the best way we do computing. those new applied sciences have enabled the clustering of a large choice of geographically allotted assets, reminiscent of supercomputers, garage platforms, information resources, and detailed units and providers, that can then be used as a uni?ed source. additionally, they've got enabled seamless entry to and interplay between those dispensed assets, companies, functions, and knowledge. the recent paradigm that has developed is popularly termed “Grid computing”. Grid computing and the usage of the worldwide Grid infrastructure have provided signi?cant demanding situations in any respect degrees, together with program improvement, progr- ming versions, structures, infrastructures and providers, networking, and protection, and feature resulted in the improvement of an international learn neighborhood. Grid 2002 is the 3rd in a chain of workshops constructed to supply a - rum for this becoming Grid Computing examine group. Grid 2000, the ?rst workshop within the sequence, was once chaired through Rajkumar Buyya and Mark Baker, and was once held at the side of HiPC 2002 in Bangalore, India. Grid 2001 (Chair: Craig A. Lee) and Grid 2002 have been held at the side of Supercomputing, the world’s best assembly for high-performance computing.
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Extra info for Grid Computing — GRID 2002: Third International Workshop Baltimore, MD, USA, November 18, 2002 Proceedings
The ongoing DOE-sponsored SciDAC projects  will enable the easy integration of our services in the near future, when we expect that Web services technologies are adopted within the Grid community. A cartoon of this use case is depicted in Figure 6. 9 Implementation Our implementation follows the strict separation of backend services from rendering services as depicted in Figure 2. On the backend, we provide an application factory service, which is capable of launching non-Grid-aware applications, after initializing them with parameters and other command line arguments.
This graphic image is then returned to the polyﬁt client, and the data can be viewed on the users terminal. The workﬂow for the components and services that are involved in this use case are indicated with the letter a in Figure 2. The portal interface is shown in Figure 3. Use Case B: Visualizing a reaction graph and obtaining information on the species in the graph. The user starts up the reaction visualization client and submits a reaction ﬁle to this client. A reaction ﬁle can be as simple as one chemical reaction, or it can be hundreds of reactions all working together.
This statistical property of Monte Carlo computation can be used to develop an approach for validating the partial results of a large grid-based Monte Carlo computation. Here is the proposed method for distributed Monte Carlo partial result validation. Suppose we are running n Monte Carlo subtasks on the grid, the ith subtask will eventually return a partial result, fi. We anticipate that fi are approximately normally distributed with mean, fˆ , and standard deviation, σ = s / n . We expect that about one of the fi in this group of n to lie outside a normal confidence interval with confidence 1 – 1/n.