Shifting sands of hardware and software in exascale quantum mechanical simulations

Abstract

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. The exascale era, driven by GPU-dominated architectures, demands a shift in quantum simulation software. This Perspective examines algorithm adaptation, software fragmentation, and the need for efficient GPU-optimized methods, standardized libraries and scalable programming models for high-performance quantum simulations.