## 2 Literature Study  
The FM index finds a wide range of applications related to genomic studies, particularly in sequence alignment. It is a suitable approach for aligning short reads to large genome references with a small memory footprint [9]. Most efficient sequence aligners, including BOWTIE [10], BWA [11], HISAT [12], and SOAP2 [13], exploit the FM index-based mechanism. The FM index has two types of indices: a global FM index for the entire genome and many small FM indices for genome fragments that span the whole genome together [3].  
Additionally, another area where the FM index is used in genomics studies is whole-genome alignment [14]. Moreover, hardware-accelerated exact and inexact alignment using the FM index has been studied for FPGA-based alignment architectures [15]. An evaluation study, in the context of RNA alignment, showed a significant reduction in memory consumption and runtime of FM index-based aligners compared to hash table-based aligners at nearly identical precision and recall rates [16]. This test also showed the high tolerance of BOWTIE to indels and sequencing errors, so it was not the proper candidate for using high-tolerant but inexact hash-based aligners. While FM-based aligners have been widely adopted as the data structure for several years for the fast alignment of sequences, some performance limitations have been observed in memory-constrained systems when using FM-based aligners compared to hash-based aligners [17].  
Sequencing alignment is a collaborative research type in which heterogeneous architectures have been widely used. NUMA has emerged as one of the most widely utilized computing infrastructures [18]. Research focusing on sequence alignments as a trade-off between data locality and data dispersion in the systems based on NUMA highlights an interesting research topic on NUMA-based heterogeneous architecture [16]. Severalstudies [19; 20; 21] have quantitatively investigated the performance of various memory allocation policies and data partitioning strategies in NUMA architectures. Many algorithms and tools have been developed, but they do not consider the impact of the computing structure on the overall performance of sequence alignment. For instance, BarraCUDA is one of the short-read sequence aligners that rapidly align sequences using GPUs [22]. It uses a suffix-tree method to locate exact matches in the reference sequence. GSWABE provides all-to-all pairwise global, semi-global, and local alignment through a tile-based method that leverages the computational capability of CUDA-enabled GPUs [23]. However, these new hardware techniques are not aimed at improving the efficiency of the utilization of the existing infrastructures.  
Based on the above discussion, using NUMA-based heterogeneous architectures in HPC systems can generally improve sequence alignment algorithms [24]. Processors with NUMA architectures and parallelization techniques enhance the speed of the alignment process. It is very important to note that the size of biological databases is continuously increasing, which results in data being more complex in the alignment process and also needing more time for computation [25]. Additionally, there is a requirement for the development of alignment algorithms that can work with datasets where sequences have varying lengths, which is a must to achieve more precise results for applications requiring high precision [26]. However, much more research is needed to be conducted regarding the investigation of the interplay between stages of alignment and the processor architecture to enhance the performance of the sequence alignment algorithms for NUMA-based heterogeneous architectures. The research aims to address these challenges and contribute to better and more effective alignment algorithms suitable for datasets exhibiting heterogeneity in computing architectures within heterogeneous processors of HPC systems.