做科学计算或AI+HPC交叉研究的团队，这些技巧能帮你避开常见坑，直接提升工作流效率，建议收藏。
高性能计算（HPC）集群传统上执行确定性线性管道，但AI和基础模型的普及引入了迭代、数据驱动和概率性的新计算范式。本文提供了12条实用技巧，帮助研究人员设计高效、可扩展且可复现的AI驱动HPC工作流，涵盖容器化、作业数组部署、反馈循环机制和小文件I/O优化等关键系统级瓶颈。这些原则广泛适用于分布式环境，尤其针对现代计算生物学中资源密集型的吞吐需求。
Twelve quick tips for designing AI-driven HPC workflows
High-performance computing (HPC) clusters remain the backbone of large-scale scientific computation, traditionally executing deterministic, linear pipelines optimised for predictable performance. However, the pervasive integration of artificial intelligence (AI) and foundation models into scientific research has introduced a fundamentally new computational paradigm. AI-driven workflows are characteristically iterative, data-driven, and probabilistic, introducing unique challenges regarding data gravity, heterogeneous resource management, and complex workflow orchestration. This guide provides twelve practical tips designed to help researchers design efficient, scalable, and reproducible AI-driven HPC workflows. By addressing critical system-level bottlenecks - such as containerisation for environment portability, strategic deployment of job arrays, explicit feedback loop mechanics, and I/O optimisation for small files - this article offers a framework for transitioning from rigid execution pipelines to adaptive, intelligent computational environments. While these architectural principles are broadly applicable across distributed environments, they are particularly tailored to the resource-intensive throughput demands of modern computational biology.