Leveraging Neural Networks with Attention Mechanism for High-Order Accuracy in Charge Density in Particle-in-Cell Simulation

In this research, we introduce an innovative three-network architecture that comprises an encoder-decoder framework with an attention mechanism. The architecture comprises a 1st-order-pre-trainer, a 2nd-order-improver, and a discriminator network, designed to boost the order accuracy of charge density in Particle-In-Cell (PIC) simulations. We acquire our training data from our self-developed 3-D PIC code, JefiPIC. The training procedure starts with the 1st-order-pre-trainer, which is trained on a large dataset to predict charge densities based on the provided article positions. Subsequently, we fine-tune the 1st-order-pre-trainer, whose predictions then serve as inputs to the 2nd-order-improver. Meanwhile, we train the 2nd-order-improver and discriminator network using a smaller volume of 2nd-order data, thereby achieving to generate charge density with 2nd-order accuracy. In the concluding phase, we replace JefiPIC's conventional particle interpolation process with our trained neural network. Our results demonstrate that the neural network-enhanced PIC simulation can effectively simulate plasmas with 2 nd-order accuracy. This highlights the advantage of our proposed neural network: it can achieve higher-accuracy data with fewer real labels.Comment: 8 pages, 10 figure

Modern gyrokinetic particle-in-cell simulation of fusion plasmas on top supercomputers

The gyrokinetic toroidal code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5-D Vlasov–Poisson equation featuring efficient utilization of modern parallel computer architectures at the petascale and beyond. Motivated by the goal of developing a modern code capable of dealing with the physics challenge of increasing problem size with sufficient resolution, new thread-level optimizations have been introduced as well as a key additional domain decomposition. GTC-P’s multiple levels of parallelism, including internode 2-D domain decomposition and particle decomposition, as well as intranode shared memory partition and vectorization, have enabled pushing the scalability of the PIC method to extreme computational scales. In this article, we describe the methods developed to build a highly parallelized PIC code across a broad range of supercomputer designs. This particularly includes implementations on heterogeneous systems using NVIDIA GPU accelerators and Intel Xeon Phi (MIC) coprocessors and performance comparisons with state-of-the-art homogeneous HPC systems such as Blue Gene/Q. New discovery science capabilities in the magnetic fusion energy application domain are enabled, including investigations of ion–temperature–gradient driven turbulence simulations with unprecedented spatial resolution and long temporal duration. Performance studies with realistic fusion experimental parameters are carried out on multiple supercomputing systems spanning a wide range of cache capacities, cache-sharing configurations, memory bandwidth, interconnects, and network topologies. These performance comparisons using a realistic discovery-science-capable domain application code provide valuable insights on optimization techniques across one of the broadest sets of current high-end computing platforms worldwide

Smart Distributed Processing Technologies For Hedge Fund Management

Distributed processing cluster design using commodity hardware and software has proven to be a technological breakthrough in the field of parallel and distributed computing. The research presented herein is the original investigation on distributed processing using hybrid processing clusters to improve the calculation efficiency of the compute-intensive applications. This has opened a new frontier in affordable supercomputing that can be utilised by businesses and industries at various levels. Distributed processing that uses commodity computer clusters has become extremely popular over recent years, particularly among university research groups and research organisations. The research work discussed herein addresses a bespoke-oriented design and implementation of highly specific and different types of distributed processing clusters with applied load balancing techniques that are well suited for particular business requirements. The research was performed in four phases, which are cohesively interconnected, to find a suitable solution using a new type of distributed processing approaches. The first phase is an implementation of a bespoke-type distributed processing cluster using an existing network of workstations as a calculation cluster based on a loosely coupled distributed process system design that has improved calculation efficiency of certain legacy applications. This approach has demonstrated how to design an innovative, cost-effective, and efficient way to utilise a workstation cluster for distributed processing. The second phase is to improve the calculation efficiency of the distributed processing system; a new type of load balancing system is designed to incorporate multiple processing devices. The load balancing system incorporates hardware, software and application related parameters to assigned calculation tasks to each processing devices accordingly. Three types of load balancing methods are tested, static, dynamic and hybrid, which each of them has their own advantages, and all three of them have further improved the calculation efficiency of the distributed processing system.   The third phase is to facilitate the company to improve the batch processing application calculation time, and two separate dedicated calculation clusters are built using small form factor (SFF) computers and PCs as separate peer-to-peer (P2P) network based calculation clusters. Multiple batch processing applications were tested on theses clusters, and the results have shown consistent calculation time improvement across all the applications tested. In addition, dedicated clusters are built using SFF computers with reduced power consumption, small cluster size, and comparatively low cost to suit particular business needs. The fourth phase incorporates all the processing devices available in the company as a hybrid calculation cluster utilises various type of servers, workstations, and SFF computers to form a high-throughput distributed processing system that consolidates multiple calculations clusters. These clusters can be utilised as multiple mutually exclusive multiple clusters or combined as a single cluster depending on the applications used. The test results show considerable calculation time improvements by using consolidated calculation cluster in conjunction with rule-based load balancing techniques. The main design concept of the system is based on the original design that uses first principle methods and utilises existing LAN and separate P2P network infrastructures, hardware, and software. Tests and investigations conducted show promising results where the company’s legacy applications can be modified and implemented with different types of distributed processing clusters to achieve calculation and processing efficiency for various applications within the company. The test results have confirmed the expected calculation time improvements in controlled environments and show that it is feasible to design and develop a bespoke-type dedicated distributed processing cluster using existing hardware, software, and low-cost SFF computers. Furthermore, a combination of bespoke distributed processing system with appropriate load balancing algorithms has shown considerable calculation time improvements for various legacy and bespoke applications. Hence, the bespoke design is better suited to provide a solution for the calculation of time improvements for critical problems currently faced by the sponsoring company