International high-energy particle physics research centers, like CERN and Fermilab, require excessive studies and simulations to plan for the upcoming upgrades of the world's largest particle accelerators, and the design of future machines given the technological challenges and tight budgetary constraints. The Beam Longitudinal Dynamics (BLonD) simulator suite incorporates the most detailed and complex physics phenomena in the field of longitudinal beam dynamics, required for providing extremely accurate predictions. Modern challenges in beam dynamics dictate for longer, larger and numerous simulation studies to draw meaningful conclusions that will drive the baseline choices for the daily operation of current machines and the design choices of future projects. These studies are extremely time consuming, and would be impractical to perform without a High-Performance Computing oriented simulator framework. In this article, at first, we design and evaluate a highly-optimized distributed version of BLonD. We combine approximate computing techniques, and leverage a dynamic load-balancing scheme to relax synchronization and improve scalability. In addition, we employ GPUs to accelerate the distributed implementation. We evaluate the highly optimized distributed beam longitudinal dynamics simulator in a supercomputing system and demonstrate speedups of more than two orders of magnitude when run on 32 GPU platforms, w.r.t. the previous state-of-art. By driving a wide range of new studies, the proposed high performance beam longitudinal dynamics simulator forms an invaluable tool for accelerator physicists.

Excessive studies and simulations are required to plan for the upcoming upgrades of the world's largest particle accelerators, and the design of future machines, given the technological challenges and tight budgetary constraints. The Beam Longitudinal Dynamics (BLonD) simulator suite incorporates the most detailed and complex physics phenomena in the field of longitudinal beam dynamics, required for providing extremely accurate predictions. These predictions are invaluable to the operation of existing accelerators, upcoming upgrades, and future studies. To undertake this agenda, and enable for the first time scale-out beam longitudinal dynamics simulations, we implement Hybrid-BLond, a distributed version of BLonD, that efficiently combines horizontal and vertical scaling. We propose a series of techniques that minimize the inter-node communication overhead and improve scalability. Firstly, we exploit mixed data and task parallelism opportunities. Secondly, we discuss two traffic optimisation techniques motivated by the properties of the simulated physics phenomena. Finally, we build a dynamic load-balancing scheme that coordinates effectively all the above features. We evaluate experimentally Hybrid-BLonD in an HPC cluster built with cutting-edge Intel servers and Infiniband interconnection network. Our fully-optimised implementation demonstrates an average 25.7X speedup over the previous state-of-the-art simulator when run on 32 computing nodes, across three real-world testcases.