In actual operation, there are security risks to the data of the network control system, mainly in the form of possible eavesdropping of signals in the transmission channel and parameters in the controller leading to data leakage. In this paper, we propose a scheme for encrypting linear controllers using fully homomorphic encryption, which effectively removes these security risks and substantially improves the security of networked control systems. Meanwhile, this paper uses precomputation to handle data encryption, which eliminates the encryption time and solves the drawback of fully homomorphic encryption that it is difficult to apply due to the efficiency problem. Compared to previous schemes with precomputation, for the first time, we propose two methods to mitigate the problem of the slight security degradation caused by precomputation, which makes our scheme more secure. Finally, we provide numerical simulation results to support our scheme, and the data show that the encrypted controller achieves normal control and improves safety and efficiency.

LLMs are trained using many thousands of GPUs in well-known conventional models. It is necessary to address numerous issues in the training process, such as manual data collection organization, data parallel, model parallel, evaluation, testing, deployment, transferring large data streams, detecting errors, ongoing maintenance, and project management. A team of dozens of engineers is required to handle system problems in the training process. Therefore, it is time-consuming and expensive to build an efficient and fault-tolerant system based on Kubernetes. This paper develops SimpleScale for building LLMs based on FSDP and Slurm, which is a simple and efficient training system that includes the training agent, the efficient parallel strategy, the optimal step of checkpoint, and so on. Using the proposed system enables us to significantly accelerate the process of building the LLM without incurring substantial time spent on various manual issues. The proposed 1024-GPU cluster was tested on TinyLlama, which has 1.1 billion parameters and 300 billion tokens. For example, utilizing a 16-H100 GPU cluster accelerated the traditional TinyLlama training time costs from 89.05 days to 39.05 days. In the future, the project team plans to integrate Flash-Attention3, aiming for an MFU of more than 60% while maintaining the acceleration achieved in the present work.

With the development of nuclear energy technology, the numerical simulation of magnetic confinement fusion has higher requirements for accuracy and speed. It is an inevitable trend to use high-performance computers to meet the requirements of numerical simulation for accuracy and speed. As an important part of tokamak research, the plasma single particle simulation program describes the motion process of plasma particles in the tokamak and the numerical simulation study of related physical quantities. This paper analyses and summarizes the challenges of porting and parallelization of plasma single particle simulation programs on the Sunway Bluelight II supercomputer. To address these challenges, this paper proposes an efficient, general, and scalable two-level parallelization method, including thread-level parallelization and process-level parallelization. The solution optimises the memory access mode and data transmission mode, which significantly improves the efficiency of program execution. In process-level parallelization, this paper fully integrates the hardware architecture of Sunway Bluelight II supercomputer and proposes an optimization scheme based on spatial region decomposition. The scheme progressively refines the parallel granularity and achieves mass parallelization of program. Two-level parallelization can achieve up to 16.8x speedup compared to the original program. The optimised program has good strong and weak scaling and can run stably on 2028 processes of Sunway Bluelight II supercomputer.