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<p>The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. Although the transition from petaflops to exascale computing has been marked by a steady increase in computational power, it is accompanied by a shift towards heterogeneous architectures, with graphical processing units (GPUs) in particular gaining a dominant role. The exascale era therefore demands a fundamental shift in software development strategies. This Perspective examines the changing landscape of hardware and software for exascale computing, highlighting the limitations of traditional algorithms and software implementations in light of the increasing use of heterogeneous architectures in high-end systems. We discuss the challenges of adapting quantum chemistry software to these new architectures, including the fragmentation of the software stack, the need for more efficient algorithms (including reduced precision versions) tailored for GPUs, and the importance of developing standardized libraries and programming models.</p>

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Shifting sands of hardware and software in exascale quantum mechanical simulations

  • 期刊论文

  • 发表日期：

    2025年4月25日

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  • Ravindra Shinde,
  • Claudia Filippi,
  • Anthony Scemama,
  • William Jalby

摘要

The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. Although the transition from petaflops to exascale computing has been marked by a steady increase in computational power, it is accompanied by a shift towards heterogeneous architectures, with graphical processing units (GPUs) in particular gaining a dominant role. The exascale era therefore demands a fundamental shift in software development strategies. This Perspective examines the changing landscape of hardware and software for exascale computing, highlighting the limitations of traditional algorithms and software implementations in light of the increasing use of heterogeneous architectures in high-end systems. We discuss the challenges of adapting quantum chemistry software to these new architectures, including the fragmentation of the software stack, the need for more efficient algorithms (including reduced precision versions) tailored for GPUs, and the importance of developing standardized libraries and programming models.