Developing complex, reliable advanced accelerators requires a coordinated, extensible, and comprehensive approach in modeling, from source to the end of beam lifetime. We present highlights in Exascale Computing to scale accelerator modeling software to the requirements set for contemporary science drivers. In particular, we present the first laser-plasma modeling on an exaflop supercomputer using the US DOE Exascale Computing Project WarpX. Leveraging developments for Exascale, the new DOE SCIDAC-5 Consortium for Advanced Modeling of Particle Accelerators (CAMPA) will advance numerical algorithms and accelerate community modeling codes in a cohesive manner: from beam source, over energy boost, transport, injection, storage, to application or interaction. Such start-to-end modeling will enable the exploration of hybrid accelerators, with conventional and advanced elements, as the next step for advanced accelerator modeling. Following open community standards, we seed an open ecosystem of codes that can be readily combined with each other and machine learning frameworks. These will cover ultrafast to ultraprecise modeling for future hybrid accelerator design, even enabling virtual test stands and twins of accelerators that can be used in operations.

翻译：开发复杂、可靠的先进加速器需要从束流源到束流寿命终点的协同、可扩展且全面的建模方法。我们展示了百亿亿次计算在将加速器建模软件扩展到满足当代科学驱动需求方面的亮点。特别地，我们首次在百亿亿次超级计算机上使用美国能源部百亿亿次计算项目WarpX进行了激光-等离子体建模。借助百亿亿次计算的发展，新的能源部SCIDAC-5高级加速器建模联盟（CAMPA）将以协同方式推进数值算法并加速社区建模代码：从束流源，经过能量提升、传输、注入、存储，到应用或相互作用。这种端到端建模将能够探索包含常规和先进元件的混合加速器，作为先进加速器建模的下一步。遵循开放社区标准，我们构建了一个可相互组合并与机器学习框架无缝集成的开放代码生态系统。这些代码将覆盖从超快到超精密的建模，以支持未来混合加速器的设计，甚至能够建立可用于实际运行的虚拟测试台和加速器数字孪生。