### TABLE 3. Speedup and efficiency for instances of the JSP. Algorithm is the parallel implementation of GRASP. Instances are abz6, mt10, orb5, and la21, with target values 960, 960, 920, and 1120, respectively. The estimated parameters for the exponential distributions are shown for each pair of instance/target value.

2005

Cited by 1

### Table 2. Throughput, delay and delay variance compar- isons for Reno, Vegas and Santa Cruz for basic bottle- neck configuration.

1999

"... In PAGE 6: ...16.5) and C6D3D4. The algorithm maintains this steady-state value for the duration of the connection. Table2 compares the throughput, average delay and delay vari- ance for Reno, Vegas and Santa Cruz. For TCP Santa Cruz we vary the amount of queueing tolerated in the network from C6D3D4 = 1 to 5 packets.... ..."

Cited by 40

### Table 2. Throughput, delay and delay variance compar- isons for Reno, Vegas and Santa Cruz for basic bottle- neck configuration.

1999

"... In PAGE 6: ...16.5) and C6D3D4. The algorithm maintains this steady-state value for the duration of the connection. Table2 compares the throughput, average delay and delay vari- ance for Reno, Vegas and Santa Cruz. For TCP Santa Cruz we vary the amount of queueing tolerated in the network from C6D3D4 = 1 to 5 packets.... ..."

Cited by 40

### Table 1: Fast, e cient low-contention parallel algorithms for several fundamental problems. For the

1996

"... In PAGE 3: ... This paper considers ve such problems | generating a random permutation, multiple compaction, distributive sorting, parallel hashing, and load balancing | and presents fast, work-optimal qrqw pram algorithms for these fundamental problems. These results are summarized in Table1 , and are contrasted with the best known erew pram algorithms for the same problems. All of our algorithms are randomized, and are of the \Las Vegas quot; type;; they always output correct results, and obtain the stated bounds with high probability.... In PAGE 19: ... Examples of cyclic and noncyclic permutations are given in Figure 1. As indicated in Table1 , the best known linear work random permutation algorithm for the erew pram run in O(n ) time, for xed gt;0. This is also the best bound known for the random cyclic permutation problem.... In PAGE 26: ... Our result is for distinct keys. As shown in Table1 , the best known linear work erew pram algorithm for this problem runs in O(n ) time. 6.... In PAGE 29: ...1 Distributive Sorting The sorting from U(0;; 1) problem is to sort n numbers chosen uniformly at random from the range (0;; 1). As indicated in Table1 , the best known linear work erew pram algorithm for this problem runs in O(n ) time, for xed gt;0. erew pram algorithms that run in polylog time are work ine cien tbyatleasta p lg n lg lgn factor.... ..."

### Table 1: Fast, e#0Ecient low-contention parallel algorithms for several fundamental problems. For the

"... In PAGE 11: ...1 Distributive Sorting The sorting from U#280; 1#29 problem is to sort n numbers chosen uniformly at random from the range #280; 1#29. As indicated in Table1 , the best known linear work erew pram algorithm for this problem runs in O#28n #0F #29 time, for #0Cxed #0F#3E0. erew pram algorithms that run in polylog time are work ine#0Ecientby at least a p lg n lg lgn factor.... In PAGE 14: ... Our result is for distinct keys. As shown in Table1 , the best known linear work erew pram algorithm for this problem runs in O#28n #0F #29 time. 6.... In PAGE 21: ... Examples of cyclic and noncyclic permutations are given in Figure 1. As indicated in Table1 , the best known linear work random permutation algorithm for the erew pram run in O#28n #0F #29 time, for #0Cxed #0F#3E0. This is also the best bound known for the random cyclic permutation problem.... In PAGE 37: ... This paper considers #0Cve such problems | generating a random permutation, multiple compaction, distributive sorting, parallel hashing, and load balancing | and presents fast, work-optimal qrqw pram algorithms for these fundamental problems. These results are summarized in Table1 , and are contrasted with the best known erew pram algorithms for the same problems. All of our algorithms are randomized, and are of the #5CLas Vegas quot; type; they always output correct results, and obtain the stated bounds with high probability.... ..."

### Table 1: Results obtained with two SA algorithms using and LA.

"... In PAGE 5: ...1 Comparison between and LA In order to compare both evaluation functions we used them within the SA algorithm pre- sented in Section 3 (call these SA algorithms SA- and SA-LA) and test them on the set of 21 instances. Both SA- and SA-LA were run 20 times on each instance and the results are presented in Table1 . In this table columns 1 to 3 show the name of the graph, the number of vertices and edges.... In PAGE 5: ... The last column presents the improvement obtained when the evaluation function was used. The results presented in Table1 show clearly that the new evaluation function allows the SA algorithm to obtain better results for many classes of graphs with very weak additional computing time. We can observe an average improvement of 9:18%, with a peak of 62:31% (see column Improvement).... ..."

### Table 1: Results obtained with two SA algorithms using and LA.

"... In PAGE 5: ...1 Comparison between and LA In order to compare both evaluation functions we used them within the SA algorithm pre- sented in Section 3 (call these SA algorithms SA- and SA-LA) and test them on the set of 21 instances. Both SA- and SA-LA were run 20 times on each instance and the results are presented in Table1 . In this table columns 1 to 3 show the name of the graph, the number of vertices and edges.... In PAGE 5: ... The last column presents the improvement obtained when the evaluation function was used. The results presented in Table1 show clearly that the new evaluation function allows the SA algorithm to obtain better results for many classes of graphs with very weak additional computing time. We can observe an average improvement of 9:18%, with a peak of 62:31% (see column Improvement).... ..."

### Table 2: Parallel Algorithm Characteristics

1999

"... In PAGE 18: ...Table 2: Parallel Algorithm Characteristics While there might seem to be a plethora of information on parallel association rule mining presented above, without an impression of the larger picture, it is instructive to refer to Table2 . It shows the essential differences among the different methods reviewed above, and groups related algorithms together.... ..."

Cited by 73

### TABLE 1 Average edge length obtained with various refinements of Algorithm LA

1999

Cited by 7

### Table 10: Parallel reduction a la Tait amp; Martin-Lof

2000

"... In PAGE 64: ...y Aczel [Acz78]. For an extensive discussion see van Raamsdonk [Raa96]. We use the notation ?! for parallel reduction. In the style of Tait and Martin-Lof, it is de ned by the inductive clauses in Table10 . It characterizes... ..."

Cited by 8