Results 1 - 10 of 18,804

Table 4: Overall runtime of the parallel SAT solver for different number of clients

in ABSTRACT Advances and Insights into Parallel SAT Solving
by Stephen Plaza, Ian Kountanis, Zaher Andraus, Valeria Bertacco, Trevor Mudge

TABLE VI OVERALL COMPONENT RUNTIMES (MINUTES)

in Automatic Generation of Social Network Data from Electronic-Mail Communications
by Jason Yee, Robert F. Mills, Gilbert L. Peterson, Summer E. Bartczak 2005
Cited by 2

Table 2.3: Overall runtimes and runtime ratios of QSIM functions. The abbreviation generate-pvalues denotes the function generate-possible-values.

in Design, Implementation, and Experimental Evaluation of Coprocessor Architectures for Fast Qualitative Simulation
by Marco Platzner

Table 5: Runtime breakdown for overall 10xRT system

in The CUHTK-Entropic 10xRT Broadcast News Transcription System
by Julian Odell, Philip Woodland, J. J. Odell, Thomas Hain 1999
"... In PAGE 5: ... The word error rate for the final system is shown in Ta- ble 4. The breakdown of the overall computation time for each stage shown in Table5 . Comparing Table 4 with Table 3 the advantage of including two passes can clearly be seen: on BNeval98 the error rate was reduced by 24% while the overall computation was increased by about a factor of three.... ..."
Cited by 12

Table 5: Runtime breakdown for overall 10xRT system

in The CUHTK-Entropic 10xRT Broadcast News Transcription System
by J. J. Odell, P. C. Woodland, T. Hain 1999
"... In PAGE 5: ... The word error rate for the final system is shown in Ta- ble 4. The breakdown of the overall computation time for each stage shown in Table5 . Comparing Table 4 with Table 3 the advantage of including two passes can clearly be seen: on BNeval98 the error rate was reduced by 24% while the overall computation was increased by about a factor of three.... ..."
Cited by 12

Table 5: Runtime breakdown for overall 10xRT system

in The CUHTK-Entropic 10xRT Broadcast News Transcription System
by J. J. Odell , P. C. Woodland, T. Hain
"... In PAGE 5: ... The word error rate for the final system is shown in Ta- ble 4. The breakdown of the overall computation time for each stage shown in Table5 . Comparing Table 4 with Table 3 the advantage of including two passes can clearly be seen: on BNeval98 the error rate was reduced by 24% while the overall computation was increased by about a factor of three.... ..."

Table 2: Comparisons for runtime per iteration and memory and area requirements among cluster refinement, O-tree, and B*-tree based on iterative algorithms (and overall runtimes and areas for the B*-tree based simulated annealing algorithm). NA: Not Available.

in B*-Trees: A New Representation for Non-Slicing Floorplans
by Yun-Chih Chang, Yao-wen Chang, Guang-Ming Wu, Shu-wei Wu 2000
"... In PAGE 5: ... We performed two sets of experiments: one was basedon the MCNC benchmark circuits used in [1], and the other on some artificial rectilin- ear modules. Table2 lists the names of the circuits, the numbers of mod- ules, the runtimes per iteration for the iterative, deterministic algorithm, memory requirements, and chip areas for the cluster refinement algo- rithm [16], the O-tree, the B*-tree based on the iterative algorithm. We also tested the total runtimes and areas resulted from using the B*-tree based simulated annealing algorithm.... ..."
Cited by 64

Table 2: Comparisons for runtime per iteration and memory and area requirements among cluster refinement, O-tree, and B*-tree based on iterative algorithms (and overall runtimes and areas for the B*-tree based simulated annealing algorithm). NA: Not Available.

in B*-Trees: A New Representation for Non-Slicing Floorplans
by unknown authors
"... In PAGE 5: ... We performed two sets of experiments: one was basedon the MCNC benchmark circuits used in [1], and the other on some artificial rectilin- ear modules. Table2 lists the names of the circuits, the numbers of mod- ules, the runtimes per iteration for the iterative, deterministic algorithm, memory requirements, and chip areas for the cluster refinement algo- rithm [16], the O-tree, the B*-tree based on the iterative algorithm. We also tested the total runtimes and areas resulted from using the B*-tree based simulated annealing algorithm.... ..."

Table 3: Here we see, for the LU factorization, the optimal runtime achieved for the 18 28 grid shape. We also see that using less nodes (last three lines utilize 256 nodes) achieves higher e ective per-node performance, but longer overall runtime.

in The Multicomputer Toolbox - First-Generation Scalable Libraries
by Anthony Skjellum, Alvin P. Leung, Steven G. Smith, Robert D. Falgout, Charles H. Still, Chuck H. Baldwin 1994
"... In PAGE 10: ... A number of similar grid shapes and node counts produce similar performance, suggesting an important degree of freedom left to applications that will call this kernel . Table3 . Size = 10000x10000, Scatter/Scatter... ..."
Cited by 11

Table 2 The cost in seconds of evaluating all 3 scores is seen to grow linearly with the population size while the overall runtime of evolving a panmictic population on a single SMP node across 100 generations rises much more sharply.

in Constrained De Novo Peptide Identification via Multi-objective Optimization
by J.M. Malard, A. Heredia-Langner, D.J. Baxter, K.H. Jarman, W.R. Cannon
"... In PAGE 6: ...5GHz Itanium 64-bit dual-processor workstations, linked together by a Quadrics QSNet 1 interconnect. Table2 illustrates how local populations can help reduce the bookkeeping time of the genetic algorithms while keeping the number of score evaluations constant. Table 2 shows the runtime in seconds for 100 generations of panmictic populations of various sizes evolved by a 2-process genetic algorithm.... In PAGE 6: ... Table 2 illustrates how local populations can help reduce the bookkeeping time of the genetic algorithms while keeping the number of score evaluations constant. Table2 shows the runtime in seconds for 100 generations of panmictic populations of various sizes evolved by a 2-process genetic algorithm. The time spent evaluating scores increases linearly with the number of score evaluations.... In PAGE 6: ...Table 3 shows the time needed by a genetic algorithm to evolve an island structured population for 100 generations using the same peptide identification problem as in Table2... ..."
Results 1 - 10 of 18,804