## 1 Introduction
In this paper, it was observed that the common FM-based alignment algorithms showed limitations in terms of computational efficiency when dealing with longer and more complex datasets. We proposed a parallel approach for short-read sequence alignment using the longest-first alignment method combined with enhanced partitioning to improve the performance of FM aligners for the evaluation of large-scale genomic short-read sequence data. The proposed algorithm employs LF and EP to achieve efficient load balancing and parallelization on heterogeneous NUMA processors of the HPC system. We conducted numerous experiments using sequence datasets from the 1000 Genomes Project and various benchmark datasets to assess the alignment performance of various FM-based algorithms. The alignment performance of the LF-EP-based FM aligner was shown to be particularly outstanding in comparison to established well-known FM aligners, like SOAP2, BOWTIE2, BWA, and GEM3, according to the popular benchmark BALIBASE, OXBENCH, SABRE, and PREFAB. These results in the experiments suggest that the LF-EP-based FM aligner results in better speedup and more accurate alignment results in better quality than other state-of-the-art FM aligners, thus making it a reliable, precise approach for large-scale genomic short-read sequence data. Furthermore, the results also provide valuable insightsinto future study directions to improve the efficiency of FM aligners in heterogeneous architecture systems. The following is a summary of the contributions of this investigation:  
1. We developed a convenient parallelism technique for NUMA systems that couples the longest-first distribution strategy with enhanced partitioning. This approach attains effective load balancing and optimal parallelization across heterogeneous NUMA processors on HPC systems. This contribution focuses primarily on the effective and rapid deployment of bioinformatics algorithms on modern heterogeneous architectures. 2. We introduced the LF-EP-based FM aligner, which utilizes the FM index for short-read sequence alignment with a novel implementation tailored for popular heterogeneous computing platforms that constitute NUMA nodes. This proposed aligner shows significant performance improvements in scalability, speedup, and quality, which is convenient for genomic research where massive data volumes are processed rapidly. 3. We conducted a comprehensive evaluation of the popular NUMA infrastructures. Our paper demonstrates improvements in parallelism through a thorough review of both main processor platforms. We tested our approach on a wide variety of NUMA architectures, including Intel Xeon and AMD Opteron. 4. We compared it to prevailing FM aligners and sequence benchmarks. These FM aligners include BWA, SOAP2, BOWTIE2, and GEM and various sequence benchmarks--BALIBASE, OXBENCH, SABRE, and PREFAB. These comprehensive aligners and benchmarks not only highlight the practical advantages but also establish a baseline for future studies to be compared with. The rest of the paper is organized as follows: a critical survey of related studies in the literature is performed in Section 2. Basic concepts of sequence alignment, an in-depth review of the FM aligner, and details of heterogeneous architectures are presented in Section 3. Our proposed algorithm, longest-first combined with enhanced partitioning, is provided in Section 4. The experimental results and performance evaluation of the proposed algorithm are presented in Section 5. Experimental findings are concluded in Section 6, with prospective research directions detailed in brief.