The technological evolution of high-performance computing (HPC) has always been closely intertwined with the strategic demands in fields such as national defense and military affairs,fundamental science,and industrial engineering.Its development can be broadly divided into 4 key stages: dedicated vector machine, massively parallel computer, heterogeneous parallel computer, and HPC-AI converged computer.Each stage continuously advances in system architecture,software ecosystems,and application paradigms.Currently,HPC is undergoing a profound paradigm shift driven by artificial intelligence.“AI for Science” has emerged as a new scientific research paradigm,in which the high-performance with high-precision for scientific computing and high-performance with mixed-precision  characteristics for intelligent computing are converging deeply.This convergence poses formidable challenges to underlying computing architectures in terms of precision coordination,data exchange,and I/O pattern adaptation.Looking ahead to the development of HPC-AI  converged HPC technologies,the competitive focus is shifting from single floating-point peak performance toward a comprehensive consideration of data movement efficiency,energy-performance ratio,and system scalability.Tighter integration among computing units,more efficient data flow,and more unified programming abstractions will become crucial features of next-generation HPC systems.The CPU-SIMT converged computing architecture,as a promising HPC-AI converged computing architecture, employs a solution combining “converged computing architecture+hierarchical interconnection networks+converged parallel storage”. This solution is expected to break through the “communication wall” bottleneck in tightly coupled HPC-AI converged computing applications,offering a new technological pathway for building next-generation HPC systems and efficiently supporting applications under the emerging  “AI for Science” computing paradigm.