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Table 3. Shared configuration parameters.
"... In PAGE 7: ... In order to examine the behavior of AXCIS across a wide range of designs, we simulated 12 machine configurations, shown in Table 2 that differ in issue width, number of primary miss tags, memory latency, and number of functional units for each instruction type. Table3 shows the functional unit laten- cies, cache, and branch predictor parameters that are shared by all configurations. Table 4 shows the limiting configurations used in the limit- based and relaxed limit-based compression schemes.... ..."
Table 12: Benchmark Share Parameters used in the Algebraic Model
Table 3 Data and Resulting Model Parameterization Used in Base Case Analyses A: Base Case Level Values1 Used as Input to Calibration Korea (1968) Thailand (1975)
"... In PAGE 14: ... The latter are key elements of the base case data, because the larger the wage rate dispersion in the base case data, the larger the gain in real income that will occur from generating intersectoral labour transfers in the model not reported in the paper. As mentioned above wage dispersion on the basis of wage rates calculated from compensation to employees are very high both for Thailand and Korea compared to what we report in Table3 . Another reason for downward revision of these estimates is to reflect human capital differences across sectors.... ..."
Table 1 Model Parameters for the Direct Path Parameter: Time Share of
"... In PAGE 6: ... A set of about 3000 files and 50 GByte of data have been used to select typical results representing the 50%- quantile of the delay spread values. Numeric values of the parameters of the direct path are given in Table1 , the parameters for the near and far echoes are given in Table 2 and 3, respectively. All parameters are given for an handheld user, except for the highway environment where a car-roof mounted antenna was used.... ..."
Table 3 Parameters for Simulated Shared-Bus Configurations. Module Parameters Ranges
1999
"... In PAGE 16: ... Furthermore, the data phase may incur a long queueing delay for data returns. Table3 summarizes the parameters for our simulations of bus-based systems. 5 Performance Evaluation Results 5.... ..."
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Table 1: Shared simulation parameters. All simulations use the above values of the listed parameters; other parameters such as simulation size, density, and time vary.
2001
"... In PAGE 8: ...1 Simulation Parameters Unless noted otherwise, all of our simulations use a common set of parameters for the radios, DSDV local routing protocol, and location service. These parameters are summarized in Table1 . All simulations occur in a square universe.... ..."
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Table 1: Shared simulation parameters. All simulations use the above values of the listed parameters; other parameters such as simulation size, density, and time vary.
2001
"... In PAGE 8: ...1 Simulation Parameters Unless noted otherwise, all of our simulations use a common set of parameters for the radios, DSDV local routing protocol, and location service. These parameters are summarized in Table1 . All simulations occur in a square universe.... ..."
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Table 14: Workload parameters Parameter SharedHot HotCold Private Uniform
"... In PAGE 98: ....7.2 Supporting Transactions Four di erent workloads are used in the rst set of experiments designed to evaluate slice consistency in a transactions-only environment. Table14 lists the parameters used for the di erent workloads. Figure 25 shows the performance of di erent methods in a high data contention... In PAGE 104: ... In the rst experiment, 50% of the clients have the hot-cold workload and the rest use the shared-hot workload. The parameters for the two workloads are the same as before ( Table14 ). For slice consistency, the clients running the hot-cold workload use the aggressive variation, while the clients with shared-hot workload use the lazy variation.... In PAGE 108: ... Figures 36 and 37 show the throughput when 10% of the transactions are queries with 100 references each in the hot-cold and the uniform workloads, respectively. The remaining parameters are the same as in Table14 . Here again, avoiding contention between queries and transactions results in 25% to 50% higher throughput for slice consistency for both the workloads.... ..."
TABLE I Parameters that were adjusted to the problem complexity: population size (N), niche radius (objective space: share, parameter space: share), and domination pressure (tdom). number of parameters number of items knapsacks 250 500 750
in Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach
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