## 5 Results and Evaluations
### Speedup Evaluation of FM Aligners  
To accurately assess the speedup provided by the LF-EP-based FM aligner, we performed the following task of analyzing several sequence datasets. Some sequence mimic techniques, such as randDNA and wgsim, were used to establish these artificial datasets. In order to provide a complete picture of the speedup evaluation in these aligners, we included sequences whose average lengths ranged from 50 to 500, as well as a varied number of sequences ranging from 100 to 10,000. In Figure 5, we provide a detailed comparison of the speedup achieved with the LF-EP-based FM aligner versus the original FM aligner, which was implemented in the native mode. We calculate the speedup based on the original FM aligner divided by the LF-EP-based FM aligner in each instance. Our performance analysis of the proposed algorithm shows an excellent speedup of up to 24.2\(\times\). It is seen that the speedup decreases as the average sequence length increases. This is because the computational complexity of the FM aligner is sequence length-based. As the analyzed sequences become more extensive, the demand for physical memory and computing power becomes high, resulting in a performance bottleneck for FM aligner implementation in such a heterogeneous architecture. Figure 4: Sequence complexity comparison among various FM aligners. Furthermore, our study investigates those performance differences that really exist between the LF-EP-based FM aligner and the multi-threading FM aligner. This further analysis enables us to understand the relative strengths and limitations of each approach. Figure 6 gives a valuable overview of the speedup achieved by the LF-EP-based FM aligner concerning the variant of the multi-threading FM aligner. Speedup is obtained concerning the performance of the original FM aligner. Three sequence datasets were generated using mimic sequence tools in the experiments, each varying in average sequence length and number of sequences. The first dataset had an average length of 50, the second an average length of 200, and the third an average length of 500. Each dataset contained sequences ranging from 100 to 10,000. The results shown in Figure 6 indicate that both the LF-EP-based FM aligner and the multi-threading FM aligner demonstrate remarkable speedup for the dataset with an average sequence length of 50. Additionally, a 24.2\(\times\) speedup is obtained for the LF-EP-based aligner in a heterogeneous system, while this speedup is 16.3\(\times\) for the multi-threading FM aligner. These results indicate substantial benefits of the LF-EP-based aligner, leading to a largely reduced execution time compared to the multi-threading alignment. Thus, performance is significantly more different when analyzing other datasets whose average sequence lengths are 200 and 500. In these cases, although still impressively show  
Figure 5: Performance comparison between original FM aligner and LF-EP-based FM aligner. Figure 6: Performance comparison between multi-threading FM aligner and LF-EP-based FM aligner, t = threads, L = length. ing the speedup, the FM aligner based on the LF-EP method attains a lower value of \(8.9\times\). The drop in speedup may be attributed to various factors, particularly the complexities of longer sequences and the corresponding increase in computational demands. The efficacy of our parallel sequence alignment approach is apparent in its enhanced performance on sequences of moderate length, such as 50 or 200 lengths. However, it unveils intrinsic constraints when examining lengthier and more intricate data. The approach under consideration demonstrates significant improvements in computational efficiency when tested on artificially generated datasets, with average sequence lengths varying from 50 to 200 and average sequence quantities ranging from 100 to 1000. However, it is noticed that when the size of the datasets became greater than 200 in length or greater than 1000 in number, the expected improvement in computational performance became less significant.