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Table 2. Grid-enabled applications for Protein Folding
Table 2. Grid-enabled applications for Protein Folding
Table 1 Comparing major grid-enabling technology with DataMiningGrid Feature/system DataMiningGrid GridMiner K-Grid DMGA/WekaG SODDM FAEHIM
in An
"... In PAGE 15: ... Related work The purpose of this section is to briefly review some of the main efforts in the area of distributed, and in particular grid- enabled, data mining technologies and compare these, as much as this is possible, to the DataMiningGrid system. The criteria used to compare and discuss these systems are listed below, and a summary of the comparison is provided in Table1 . Although Please cite this article in press as: V.... ..."
Table 3.2 shows the variations of performance statistics between runs of a Grid-enabled application (presented in Section 4). The same problem instance was solved eight times, each time on a different set of processors. A user- de ned benchmark task was used to de ne the normaliza- tion factor. As expected, the statistics exhibit large variance of a95
2000
"... In PAGE 6: ...84 502 - - - 31.06 Table3 . The Computational Pool.... ..."
Cited by 61
Table 2a shows the input parameters of the three experiments from the first set. These simulations were run on a single computer , with Intel P4 processor at 2.5 GHz, 512 MB RAM, and Linux OS. The second set of experiments conducted aimed to simply confirm that Grid-enabling the VaR application was
2007
Table 1: Main Healthgrid Projects around the world Project Name Country/Region total funding Period main application areas
"... In PAGE 3: ... Main Healthgrid Projects around the world Since 2001, there have been a variety of Healthgrid projects around the world. Table1 lists some of the key projects in EU, UK, USA and Japan. These projects are focused on medical imaging processing (e.... ..."
Table 5.1: Control Channel Subnets for GMPLS multi-layer network
Table 1: Testbed resources We then evaluated the performance of the grid-enabled sample application by recording and comparing the execution times for varying values of N and T. Note that if T is one, then GridThreads are not utilized and the algorithm is run locally on a single resource (belle.cs.mu.oz.au). Table 2 below lists the performance results obtained from our tests and Figure 3 graphs our results for the PrimeFinder with T = 1, 2, 4, 8 and 14. Our results show that for values of N larger than 50 million, a performance increase of approximately 300 to 360 percent (as compared to the non-threaded performance results) when utilizing 8 or more GridThreads.
2005
"... In PAGE 6: ...Table1... ..."
Cited by 1
Table 1: Control plane parameters
"... In PAGE 11: ... Stability can be im- proved by damping route updates at the expense of in- creased network reaction time to topology changes. Our choice of system parameters is shown in Table1 , and has been experimentally verified to yield satisfactory perfor- mance. Table 2: Data plane parameters Parameter Description Value Number of path sections 2 Scope of flooding for local replication 1 Maximum retransmissions to the next hop 5 Maximum cached replication entries 4 Data plane parameters The heart of pathDCS lies in the data plane.... ..."
Table 1: Control plane parameters
"... In PAGE 10: ... Stability can be im- proved by damping route updates at the expense of in- creased network reaction time to topology changes. Our choice of system parameters is shown in Table1 , and has been experimentally verified to yield satisfactory perfor- mance. Data plane parameters The heart of pathDCS lies in the data plane.... ..."
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