The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for non-redundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust.

Through grid computing and cloud computing technologies, SCE (Scientific Computing Environment) integrates massive computing, storage and application resources. These resources are packaged as easy-to-use open APIs in RESTful web services. These APIs are used to develop terminal software for multi-disciplinary and cross-scenario. Around authentication and authorization issues among users, several services for open APIs, authentication and authorization are proposed and implemented in this paper. The proposed services provide single sign-on for several WEB communities by SCE accounts, support users to authorize terminal software that could access massive resources and personal private data in proxy mode, and also help administrators determine which open APIs a client could access. Atop the proposed services, all related people consisting of users, developers and administrators, no longer need to worry about and solve complex problems with authentication and authorization. What they need to pay much attention on are specific business logics and application scenarios for their interested areas. The proposed services have been applied to general computing portal, operation and management portal in national high-performance computing environment, and also WEB communities for computational chemistry, bioinformatics, etc. These examples show that the proposed services have achieved good performance and user experience.

As scientific workflows grow more complex and green computing becomes a priority, efficient multi-objective scheduling is essential to optimize makespan, cost, and energy consumption for large task graphs. However, existing methods often suffer from scalability bottlenecks and insufficient modeling of task dependencies, leading to degraded performance on large-scale workflows. This paper proposes HiGAT-AC, a framework that combines a hierarchical graph attention network with actor-critic reinforcement learning for scalable workflow scheduling in heterogeneous systems. HiGAT-AC splits large workflows into subgraphs via spectral clustering and uses a three-level hierarchy to capture local task dependencies, coordinate inter-subgraph information, and conduct global resource allocation. The actor-critic model employs Chebyshev scalarization to balance the three conflicting objectives. Experimental results show that HiGAT-AC achieves competitive composite scores across workflow scales from 500 to 1000 tasks, with scores reaching 0.954 on 500-task workflows and 1.000 on 1000-task workflows, while remaining stable above 0.70 across all scales. Compared with traditional and representative learning-based methods, HiGAT-AC exhibits favorable overall performance and relatively stable scalability on large task graphs, providing a promising solution for scientific workflow scheduling that balances performance and sustainability.