## 6 Conclusions and Future Works  
In this research, we develop and assess an optimized approach to the FM-based approach of short-read alignment on heterogeneous NUMA architectures. The novel integration of the longest-first algorithm and enhanced partitioning technique significantly enhances load balancing and data parallelism, addressing the computational challenges exhibited due to the exponential growth in sequencing data.  
The proposed LF-EP-based FM aligner was benchmarked against popular tools such as BALIBASE, OXBENCH, SABRE, and PREFAB. These experiments demonstrate that our method, in terms of alignment quality and computational efficiency, outperforms some popular existing FM-based aligners. Our process has shown remarkable speedup and reduction in execution time, particularly in the handling of complex and large-scale genomic datasets within HPC systems. This approach we conducted is scalable and highly efficient, and our alignment management is robust in handling data deluge, thus assuring huge potential in personalized medicine for genetic disease diagnostics and drug discovery. Furthermore, our comprehensive quantification and correlation of sequence complexity underline the robustness of the LF-EP-based FM aligner in the management of high-complexity data from artificial data and the 1000 Genome Project.  
Therefore, the LF-EP-based FM aligner is an impressive improvement to the sequence alignment technique. It is a very reliable and effective tool for genomics research. This research contributes to and opens new projects on the optimization of computational frameworks in heterogeneous environments within the field of bioinformatics, thereby enhancing the processing and interpretation ability of vast amounts of genomic data.  
In the future, it is worth exploring and further developing the FM-based mechanism in areas of genomic studies [33]. Consequently, it is highly recommended that integration with other robust algorithms and data structures be conducted so that it becomes more enhanced, improved, and effective towards sequence alignment in the heterogeneous NUMA architectures across various HPC platforms such as CPU plus GPU, FPGA, etc. [34]. Furthermore, evaluations are recommended to be conducted on other reference sequences from different types of organisms. Therefore, this could ensure we validate our algorithm in detail concerning its efficiency and robustness.  
Conceptualization, S.C. and Y.D.; Methodology, S.C.; Validation, Y.D., L.L. and X.Y.; Investigation, Y.D.; Resources, X.Y.; Data Curation, L.L.; Writing--Original Draft Preparation, Y.D.; Writing--Review & Editing, S.C.; Visualization, L.L. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Jiangxi Province, with grant numbers 20212 ABAB212007 and 20212 BAB202016.  
The data presented in this study are concluded in Table 3.  
**Acknowledgments:** The authors would like to acknowledge all the support received from Jiangxi Normal University and Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance.  
**Conflicts of Interest:** The authors declare no conflicts of interest.