Sentence examples for using the genome workload from inspiring English sources

Table 5 presents the results of the experiments using the Genome workload.

Increasing the values of s max experimented with when using the Genome workload is expected to generate a similar trend in performance to the results of the CyberShake workload.

Table 2 and Table 3 present the results of the experiments when using the LIGO workload and the Genome workload, respectively.

This did not happen when using the other two workloads because the Genome workload comprises jobs with very high average execution times (~160,213 s on a single resource), which is significantly higher compared to those of the CyberShake and LIGO workloads.

The results of the experiments using the LIGO workload (see Table 8) and the Genome workload (see Table 9) follow a similar trend in system performance to that of the CyberShake workload when using PD-SL-TSP1: P decreases, T decreases, and O tends to decrease as m increases.

PD-SL-TSP1 PD-SL-TSP1 PD-SL-TSP1 PD-SL-TSP1kload.

More specifically, the two RM-DCWF configurations that are compared for each workload type are summarized: PD-SL-TSP1 PD-SL-TSP1 PD-SL-TSP1 CyberShake workload PD-SL-TSP1 vs ED-SL-TSP2 for the LIGO workload PD-SL-TSP1 vs ED-SL-TSP1 for the Genome workload.

Unlike the CyberShake workload, when using the LIGO and Genome workloads, configuring RM-DCWF to use ED with TSP2 did not produce a better performance in comparison to using ED with TSP1.

The results of the experiments using the LIGO workload and Genome workload are presented in Table 6 and Table 7, respectively.

It is observed that the trend in performance observed for both systems when using the LIGO and Genome workloads are identical to that of the CyberShake workload when using PD-SL-TSP1: P decreases, O decreases, and T remains approximately at the same level as em max increases.

However, when using the LIGO and Genome workloads, the best performance in terms of P is achieved by ED-SL-TSP1.