## 1 Introduction  
Advances in next-generation sequencing can be seen in the rapid decrease in sequencing costs attributed to genome sequencing, which dropped from USD 1 million in 2007 to USD 600 in 2021 [1]. This drop has led to the exponential growth of sequencing data, which ranges from gigabytes to terabytes and even petabytes. The fact that the capacity of sequence data doubles within 12 months while the processor processing capacity doubles within 18 months results in a significant challenge. Consequently, it requires a more powerful sequence processing program to manage the data deluge [2]. Sequence alignment is the most fundamental process in the analysis of genomic data. Across most variation detection and annotation processes, it is the starting point, leading to biologically significant consequences. It is a highly computing-intensive and resource-heavy process. In genomics, efficient sequence alignment certainly has practical implications, primarily for evaluating data correctly and aligning millions to billions of short-read sequences accurately. The greatest computational challenge, however, is handling the ever-growing data in genomic sequences. Among the operations that the alignment of sequences provides is variant analysis, which is crucial in the process of the interpretation of genetic information or the discovery of cryptic features in DNA, RNA, and protein sequences. It also enables researchers and scientists to analyze and interpret genomic data more compactly and precisely, advancing personalized medicine, genetic disease diagnosis, and drug discovery. The FM index is renowned in the large bioinformatics community for its enormous ability to handle large-scale short-read sequence alignment projects [3]. Its small size and fast query capabilities in the next-generation sequencing platform pose an optimal condition to accommodate the vast amounts of short-read data. [PERSON] and [PERSON] [4] designed the succinct model of the FM index. This approach was adopted in 1994 when [PERSON] and [PERSON] suggested compressing text using the Burrows-Wheeler Transform (BWT) before building the FM index. This allows characters in the text sequence to be permuted with the BWT, making the patterns of the transformed data similar to those of the original characters, generally allowing for the better compressibility of the modified data. In addition, the BWT was combined with the sequence alignment properties to form the FM index, further consolidating a wavelet tree data structure. This combination highly augmented efficiency during the pattern matching and searching performed within the compressed datasets, primarily for sequence data. Nowadays, the FM index is widely employed in many short-read sequence aligners to facilitate the efficient and accurate mapping of short reads to genome references [5], including BOWTIE, BWA, and SOAP2. Heterogeneous architecture nodes could result in a reliable and efficient computational framework within an HPC system [6]. The heterogeneous system utilizes different computing infrastructures that come with various processor architectures to support parallel processing and maximize the extent of computation that can be executed simultaneously [7]. Thus, the heterogeneous system proves to be more reliable for sequence alignment and requires extensive computation. In addition, the heterogeneous system consists of NUMA architectures, which are prevalent in HPC systems. The effective utilization of NUMA can enhance the performance of crucial applications such as short-read sequence alignments. This NUMA architecture enables processing optimization, allowing processors to have much faster access to memory segments and significantly decreasing the bottleneck in data processing. Therefore, the enhancement in such heterogeneous systems can result from the implementation of NUMA-aware optimizations in the computational effectiveness of HPC environments [8].