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As the Next-Generation Sequencing (NGS) techniques need to process enormous amounts of data, cost-efficientfand high-throughput computational analysis is essential in genomicsfstudy. Conventional computing platforms face great challenges to meet these demands due to their limited processing speed and scalability. Hardware accelerators, such as Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), offer transformative solutions to these computational challenges. This paper provides a state-of-the-art review of the roles of hardware accelerators in genomic analysis.We performed a comprehensive and in-depth analysis of cutting-edge genomics hardware accelerators, such as GPUs, FPGAs, and ASICs, in the context of the specific algorithms they aim to enhance. Besides reviewing opportunities in hardware genome acceleration, we also provide insights into the challenges regarding processing speed, cost efficiency, and scalability. 

Genomic analysis is the study of genes which includes the identification, measurement, or comparison of genomic features. Genomics research is of great importance to our society because it can be used to detect diseases, create vaccines, and develop drugs and treatments. As a type of general-purpose accelerators with massive parallel processing capability, GPUs have been recently used for genomics analysis. Developing GPU-based hardware and software frameworks for genome analysis is becoming a promising research area. To support this type of research, benchmarks are needed that can feature representative, concurrent, and diverse applications running on GPUs. In this work, we created a benchmark suite called Genomics-GPU, which contains 10 widely-used genomic analysis applications. It covers genome comparison, matching, and clustering for DNAs and RNAs. We also adapted these applications to exploit the CUDA Dynamic Parallelism (CDP), a recent advanced feature supporting dynamic GPU programming, to further improve the performance. Our benchmark suite can serve as a basis for algorithm optimization and also facilitate GPU architecture development for genomics analysis.