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Table 1. Average number of communicating peers per process in several large-scale appli- cations (Courtesy of J. Vetter, et. al [17])
"... In PAGE 2: ...In fact, research on communication characteristics of parallel programs [17] indicates that not all pairs of MPI processes communicate among each other with equal frequency. Table1 shows the average number of communicating peers per process in some scien- tific applications. The majority of process pairs do not communicate between each other.... ..."
Table 1: Specification of four large-scale clusters System Name Jacquard RENCI BlueGene/L DataStar Seaborg
"... In PAGE 2: ...OS Linux Linux AIX AIX We conducted our MILC performance studies on four HPC systems: the Jacquard [3] Linux cluster at NERSC (National Energy Research Scientific Computing Center), an IBM BlueGene/L (BGL) system [4] at RENCI, the Seaborg [5] IBM POWER3 SMP cluster at NERSC, and the DataStar [6] IBM POWER4 SMP cluster at SDSC (San Diego Supercomputing Center). Table1 shows the configuration of the four systems. For these performance studies we used several performance tool sets that are well-suited for the kinds of scalability experiments we performed.... In PAGE 3: ... Performance analysis results For MILC 7.4, we conducted both weak scaling and strong scaling experiments on the four platforms listed in Table1 using Prophesy, and cache utilization studies on Jacquard using TAU. We also did computation efficiency studies for MILC 7.... ..."
Table 2. Large-scale epigenome mapping projects as of October 2007
in BIOINFORMATICS REVIEW doi:10.1093/bioinformatics/btm546 Genome analysis Computational epigenetics
2007
"... In PAGE 4: ... However, manual refinement is often necessary, suggesting that further improvements of primer design programs are needed. 5 EPIGENOME DATA ANALYSIS Rapid progress of experimental technologies has given rise to several epigenome mapping initiatives ( Table2 ). These projects have been breaking ground not only in terms of applying and improving large-scale experimental methods, but also in terms of developing bioinformatic methods for analyzing their data.... In PAGE 5: ... The AHEAD task force for example acknowledges the critical importance of bioinformatic methods and infrastructure in their proposal for a human epigenome project (Alliance for Human Epigenomics and Disease, 2007). Although the bioinformatic focus of the other large-scale epigenome projects ( Table2 ) was less pronounced than in the ENCODE project, important bioinformatic progress arose from them as well. The HEROIC project played a catalyzing role for the development of epigenome data storage, visualiza- tion and analysis infrastructure in Europe.... ..."
Table 7: Large Scale Multicast Applications Taxonomy
"... In PAGE 26: ... The classi cation of communications parameters, their type, and their \strictest requirement quot; values for large scale multicast application has been proposed in [8]. Table7 gives a list of... ..."
Table 1. Average number of distinct destina- tions per process in several large-scale appli- cations.
2002
Cited by 2
Table 3. Results of large-scale application of RelEx on a comprehensive set of MEDLINE abstracts (C241 million abstracts) and comparison
2007
"... In PAGE 6: ... These relations can be compared against HPRD, which contains interactions that were manually extracted from MEDLINE full-text articles. The comparison provides information with respect to dif- ferences and overlaps of the two approaches ( Table3 ). A large fraction of the HPRD interactions cannot be retrieved from the abstracts.... In PAGE 6: ...2.1 Comparing RelEx relations with HPRD interactions The hprd50 dataset allows us to estimate the performance based on the abstracts referenced by HPRD ( Table3 ) and thus to examine the differences between RelEx relations and HPRD interactions. The performance on this data set is slightly lower than on the LLL-challenge dataset.... ..."
Cited by 1
Table 6. HPC Applications predicted. To ensure integrity of predictions, the actual runtimes of applications were measured by an independent team cited in the acknowledgments. The actual runtimes are given in
"... In PAGE 8: ...1 Experimental Results We predicted the performance of the 3 large-scale scientific applications, Hycom, Avus and Overflow2, from TI-07 Benchmark suite for several CPU counts ranging from 32 to 128. Table6 lists the input data size and the exact CPU counts for the applications we used in our experiments. ... ..."
Table 2. Solution CPU time for the large-scale examples from the MATLAB Opti- misation Toolbox. Timings were obtained by averaging over several repetitions of 10 evaluations.
2005
"... In PAGE 14: ... As pointed out in [12], despite the gradient being dense the functions brownf and tbroy are partially value separable and allows the sparse deriva- tive computation in MAD and MSAD to be several times faster than sfd(nls), which cannot make use of the intermediate sparsity. Table2 lists the total run-time in obtaining a solution to the given optimi- sation problem with derivatives supplied using the various differentiation meth- ods. Compared to the overloading approach in fmad, a substantial saving in the total run-time can be seen when derivatives are supplied through source trans- formed code.... ..."
Table 2. Comparison of pass rates of multipath fading distributions using full band
"... In PAGE 3: ... Tables below show, how different CDFs fit to the data. Table2 depicts all of the measurement environments from LOS to NLOS2 (c.f.... ..."
Table 2 Large-scale scenarios
2005
"... In PAGE 5: ...3. Large-scale scenarios The setup for each scenario is displayed in Table2 . They have been designed so that we could investigate the effectiveness and characteristics of the implemented QoS mechanisms in order to provide a measurable improvement to various types of traffic that has been marked as premium traffic.... ..."
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