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超智融合高性能计算技术发展探讨.

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Title: 超智融合高性能计算技术发展探讨.
Alternate Title: Discussion on the development of HPC-AI converged high performance computing technologie.
Authors: 卢锡城1,2 xclu@nudt.edu.cn, 杨 博1,2 yb@nudt.edu.cn, 刘 杰1,2, 黄立波1, 陈新海1,2
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Apr2026, Vol. 48 Issue 4, p571-579. 9p.
Subjects: High performance computing, Artificial intelligence, Computer architecture, Energy consumption, Scientific computing, Scalability
Abstract (English): 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 adapta-tion. 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. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 高性能计算(HPC)技术演进始终与国防军事、基础科学及产业工程等领域的战略需求紧密 交织,其发展历程大致可划分为专用向量机、大规模并行计算机、异构并行计算机和超智融合计算机4个 关键阶段,各阶段在体系结构、软件生态和应用模式上不断演进。当前,高性能计算正经历一场由人工智 能驱动的深刻范式转移,"AI for Science"成为一种新型科学研究范式,科学计算的高性能、高精度与智能 计算的高性能、混合精度特征呈现出深度融合态势,对底层计算架构在精度协同、数据交换以及I/O 模式 适配等方面提出了严峻挑战。展望未来基于超智融合的高性能计算技术发展,竞争焦点正从单一的浮点 峰值性能,转向数据搬移效率、能效比以及系统可扩展性的综合考量。计算单元间更紧密的集成、更高效 的数据流动以及更统一的编程抽象,将成为下一代高性能计算系统的关键特征。CPU-SIMT 融合计算架 构作为一种有前景的超智融合计算体系结构,采用的"融合计算架构+层次化互连网络+融合并行存储" 方案,有望突破超智融合紧耦合计算应用的"通信墙"瓶颈,为构建下一代高性能计算系统提供新的技术路 径,高效支撑新型"AI for Science"计算范式应用。. [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 超智融合高性能计算技术发展探讨.
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  Data: 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 adapta-tion. 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. [ABSTRACT FROM AUTHOR]
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  Label: Abstract (Chinese)
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  Data: 高性能计算(HPC)技术演进始终与国防军事、基础科学及产业工程等领域的战略需求紧密 交织,其发展历程大致可划分为专用向量机、大规模并行计算机、异构并行计算机和超智融合计算机4个 关键阶段,各阶段在体系结构、软件生态和应用模式上不断演进。当前,高性能计算正经历一场由人工智 能驱动的深刻范式转移,"AI for Science"成为一种新型科学研究范式,科学计算的高性能、高精度与智能 计算的高性能、混合精度特征呈现出深度融合态势,对底层计算架构在精度协同、数据交换以及I/O 模式 适配等方面提出了严峻挑战。展望未来基于超智融合的高性能计算技术发展,竞争焦点正从单一的浮点 峰值性能,转向数据搬移效率、能效比以及系统可扩展性的综合考量。计算单元间更紧密的集成、更高效 的数据流动以及更统一的编程抽象,将成为下一代高性能计算系统的关键特征。CPU-SIMT 融合计算架 构作为一种有前景的超智融合计算体系结构,采用的"融合计算架构+层次化互连网络+融合并行存储" 方案,有望突破超智融合紧耦合计算应用的"通信墙"瓶颈,为构建下一代高性能计算系统提供新的技术路 径,高效支撑新型"AI for Science"计算范式应用。. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: Chinese
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      – SubjectFull: High performance computing
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Computer architecture
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      – SubjectFull: Energy consumption
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      – SubjectFull: Scientific computing
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      – SubjectFull: Scalability
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              Text: Apr2026
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