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粒子源到束流生命周期结束的全过程、可扩展且综合的建模方法。我们展示了百亿亿次计算在将加速器建模软件扩展至满足当代科学驱动力需求方面的突破性进展。具体而言，我们首次在采用美国能源部百亿亿次计算项目WarpX的百亿亿次超级计算机上实现了激光等离子体建模。借助百亿亿次计算的发展，新成立的美国能源部SCIDAC-5先进加速器建模联盟（CAMPA）将协同推进数值算法并加速社区建模代码：涵盖束流源、能量提升、传输、注入、储存、应用及相互作用的全链条。这种从始到终的建模将能够探索包含传统与先进组件的混合加速器，作为先进加速器建模的下一步方向。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衔接的代码开源生态系统。这些工具将覆盖未来混合加速器设计中从超快到超精密的建模需求，甚至能够实现可应用于运行中的加速器虚拟测试台与数字孪生系统。