论文标题
exascale粒子加速器建模和设计的下一代计算工具
Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale
论文作者
论文摘要
粒子加速器是最大,最复杂的设备之一。为了满足增加能量,强度,准确性,紧凑性,复杂性和效率的挑战,其设计和优化需要越来越复杂的计算工具。当代软件利用计算机硬件和科学软件工程实践的最新进展是关键,为上述挑战提供了速度,可重复性和功能合成性。 LBNL和协作者的束等离子加速器仿真工具包(BLAST)的核心正在开发一个新的开源软件堆栈,并提供了能够利用exascale Supercupter在Exascale Supercupter上的GPU功率的新的粒子中建模代码。结合高级数值技术(例如网状电源)和对机器学习的内在支持,这些代码均可为对未来加速器设计和操作的超脑建模提供超反应。
Particle accelerators are among the largest, most complex devices. To meet the challenges of increasing energy, intensity, accuracy, compactness, complexity and efficiency, increasingly sophisticated computational tools are required for their design and optimization. It is key that contemporary software take advantage of the latest advances in computer hardware and scientific software engineering practices, delivering speed, reproducibility and feature composability for the aforementioned challenges. A new open source software stack is being developed at the heart of the Beam pLasma Accelerator Simulation Toolkit (BLAST) by LBNL and collaborators, providing new particle-in-cell modeling codes capable of exploiting the power of GPUs on Exascale supercomputers. Combined with advanced numerical techniques, such as mesh-refinement, and intrinsic support for machine learning, these codes are primed to provide ultrafast to ultraprecise modeling for future accelerator design and operations.