### Table 1: Simulation results with multiprocessor computer.

### Table 2. JparNSS computing times for multiprocessor architectures

"... In PAGE 11: ... 5.2 Computing Times for Multiprocessors In Table2 JParNSS is run on a variety of architectures. PentiPentium II 300 148 12000 355 256 1.... In PAGE 12: ....1.6 2 Sun Ultra 60 148 192000 283342 1100 1.2beta5 Table2 . JparNSS computing times for multiprocessor architectures Number of processors Architecture Number of blocks Number of cells Computing time [s] Memory [MB] JDK version 12 of 14 with different numbers of processors.... In PAGE 12: ... This corresponds to the fact that a grid with several thousand blocks has to be run on multiprocessor system whose number of processors is substantially smaller than the number of blocks. As can be seen from Table2 , the present paper is restricted to architectures with a small number of processors. In Fig.... ..."

### Table 2. JparNSS computing times for multiprocessor architectures

"... In PAGE 11: ... 5.2 Computing Times for Multiprocessors In Table2 JParNSS is run on a variety of architectures. PentiPentium II 300 148 12000 355 256 1.... In PAGE 11: ...1.6 Table2 . JparNSS computing times for multiprocessor architectures Number of processors Architecture Number of blocks Number of cells Computing time [s] Memory [MB] JDK version 2 Pentium II 300 MHz 3 48000 1273 256 1.... In PAGE 12: ... This corresponds to the fact that a grid with several thousand blocks has to be run on multiprocessor system whose number of processors is substantially smaller than the number of blocks. As can be seen from Table2 , the present paper is restricted to architectures with a small number of processors. In Fig.... ..."

### Table 2. JparNSS computing times for multiprocessor architectures

"... In PAGE 10: ...In Table2 JParNSS is run on a variety of architectures. Table 1.... In PAGE 11: ...Table2 . JparNSS computing times for multiprocessor architectures 11 of 13 with different numbers of processors.... In PAGE 11: ... This corresponds to the fact that a grid with several thousand blocks has to be run on multiprocessor system whose number of processors is substantially smaller than the number of blocks. As can be seen from Table2 , the present paper is restricted to architectures with a small number of processors. In Fig.... ..."

### Table 4. Correlation Coefficients Obtained between Angle of Impact and Slot Dimensions.

1974

Cited by 2

### Table 1: Summary of Basic Multiprocessor Scheduling Theorems

1995

"... In PAGE 16: ... The complexity results from deterministic scheduling theory for multiprocessing where tasks are non-preemptive, have a partial order among themselves, have resource constraints (even a single resource constraint), and have a single deadline show that almost all the problems are NP-complete. To delineate the boundary between polynomial and NP-hard problems and to present basic results that every real-time designer should know, we list the following theorems without proof and compare them in Table1 . The metric used in the following theorems is the amount of computation time required for determining a schedule which satis es the partial order and resource constraints, and completes all required processing before a given xed deadline.... ..."

Cited by 94

### Table 2: Grams of Fat Absorbed per Batch for Four Brands of Shortening

"... In PAGE 14: ...1 Introduction Screening is concerned with the problem of identifying a subset of candidate treat- ments containing the best treatment from a set of potential candidates. As mo- tivation, consider the data given in Table2 on the mean amounts of fat absorbed by batches of doughnuts during cooking using one of four brands of shortening (A, B, C, D). These data are from a completely randomized experiment conducted Table 2: Grams of Fat Absorbed per Batch for Four Brands of Shortening... In PAGE 17: ...an be computed using the program find-zt as illustrated in Example 2.1. Example 3.1 For the fat absorption data in Table2 , the sample variance, pooled over the four brands, is a5 a20 a0 a6 a26a31a42a24a42a44a29a3a2 based on a1 a6 a19 a17a25 a10 a37 a26a34a33 a6 a13 a42 d.f.... ..."

### Table 2. Physical and hydraulic data during study periods. Parameter Oswego

"... In PAGE 2: ... Figure 1 shows a schematic of the circular tanks, and Table 1 shows physical characteristics of those tanks. Table2 shows the five study periods and the average flow rates, overflow rates, and detention times for each period. In order to measure the temperature distributions in these tanks, thermistors at five varying depths were attached to poles 3.... ..."

### Table 2: 2-D registration: Timings for the multiprocessor mode.

"... In PAGE 17: ... All computations have been done using the block Jacobi preconditioning. The results are given in Table2 . One can see that an approximately double decrease of the computation time can be achieved by a double increase of the number of processors used.... ..."

### Table 3. Data obtained for computing all answer sets with the multi-processor version of platypus.

2005

Cited by 5