Computational Molecular Biology 2025, Vol.15, No.3, 151-159 http://bioscipublisher.com/index.php/cmb 154 4.3 Use HPC middleware (such as SLURM, nextflow, cromwell) for workflow orchestration To run thousands of tasks simultaneously and still finish them in an orderly manner, coordination is no simpler than calculation. Workflow middleware comes in handy precisely in this regard. Systems like Nextflow and Cromwell (with WDL) enable researchers to write the script for each step first and then hand over the scheduling to the underlying system, such as SLURM or PBS. Users don't have to keep an eye on the job themselves, manually retry or log. The workflow engine will automatically handle these cumbersome tasks. Cullen et al. once reported that WAGS pipelines combined with SLURM can efficiently manage thousands of NGS samples; Mulone et al. (2023) found that StreamFlow performs more flexibly in a hybrid HPC and cloud environment, and its performance is no worse than that of Snakemake. Recently, automated assessment tools such as RecallME and Benchmarker have also been added to continuously monitor pipeline performance, making the process more stable and reproducible. What is more valuable is that this type of middleware has high versatility. The workflow defined declaratively can be directly run regardless of whether it is placed in a local cluster, a national HPC center, or the cloud. With the containerization support of Docker or Singularity, the environment is unified and the versions are consistent, making the analysis results more reliable. It can be said that with the help of these tools, NGS analysis can finally be smoothly expanded from small laboratories to large-scale, cross-institutional genomic projects. 5 Emerging Technologies for Enhancing Performance 5.1 Accelerated application of GPU and FPGA in mutation detection Hardware acceleration has become a hot topic in NGS analysis in recent years. Illumina's DRAGEN platform is a typical example, which uses FPGA chips to run genome alignment and variant invocation. The test results show that its speed is ten to thirty times faster than that of traditional CPU tools (Arram et al., 2017). NVIDIA took a different route - Clara Parabricks directly moved the GATK workflow onto the GPU cluster. Early tests showed that it could increase the speed by about 35 times. Later, with the release of more powerful Gpus, this figure rose to 65 times. Some studies have also found that GPU-accelerated mutation detection can achieve an overall speed increase of 10 to 66 times, and the accuracy rate can still remain above 99% (Vats et al., 2022). The performance of the FPGA platform is also not weak. It can be about 28 times faster than the CPU in comparison and mutation analysis (Guo et al., 2019). Of course, hardware is not omnipotent. Costs, compatibility and development difficulties still exist, but it has indeed brought about a qualitative leap in the running speed of traditional processes. 5.2 Containerization and repeatability: docker, singularity and CWL Installing bioinformatics tools on different HPC systems is a headache-inducing task. The emergence of containerization technology has somewhat alleviated this chaos. Docker, Singularity, and standardized description methods like the Common Workflow Language (CWL) have made environment configuration and tool deployment much simpler. For large-scale projects like WARP and Terra, directly binding WDL workflows with Docker images results in smooth switching between local clusters and cloud platforms. Singularity has become the "default choice" in HPC because it does not require administrator privileges and can ensure consistent operation across nodes. Research shows that whether using Nextflow + Docker or CWL + Singularity, portability and repeatability can be enhanced without sacrificing performance - even in large-scale tasks involving exome or whole genome. These standardized approaches make cross-platform workflows less prone to "errors", which is particularly important for clinical-level process validation. 5.3 Integration with cloud high-performance computing and hybrid computing models Cloud computing has completely rewritten the concept of scalability. In the past, when people talked about HPC, they always thought that they had to rely on expensive local clusters. Nowadays, many research teams tend to "combine" - using local HPC and cloud resources together rather than completely replacing them. Mulone et al. (2023) presented an example where they used StreamFlow to integrate local clusters with the cloud, optimizing both cost and flexibility while maintaining performance. The same team also proposed a heterogeneous cloud model integrating GPU, TPU and FPGA to dynamically allocate tasks through performance prediction. Today's cloud service providers - AWS, GCP, Azure - all offer accelerated instances of Gpus and FPgas, often equipped
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