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Title Page

Abstract

Contents

1. Introduction 8

2. Basic of Particle-in-cell 9

3. Parallel computing 15

4. Python 16

5. Code structure 17

6. One-Dimensional Case study (PCPA) 20

7. Two-Dimensional Case study (LWFA) 22

8. Benchmark 23

9. Conclusion 26

Reference 27

Appendix. Brief Manual of cplPICg 28

1. Initialization 28

2. Loop 30

List of Tables

Table 1. Pseudocode of PIC loop. 19

Table 2. Pseudocode of particle pusher only for velocity updating part(left) and the meaning of each step in formal equation (right). 20

List of Figures

Fig 1. Leapfrog method for equation of motion. 9

Fig 2. Basic algorithm of PIC simulation 10

Fig 3. Yee lattice for 2-dimensional case(left) and 3-dimensional case(right) 10

Fig 4. Probe data of PCPA simulation. 21

Fig 5. Probe data of reflected pulse by fluctuated plasma with different λf.[이미지참조] 21

Fig 6. Diagnostics of LWFA at t＝0.93ps. 22

Fig 7. Benchmarking result with the change of the number of cells in 1 dimensional simulation. 23

Fig 8. Benchmarking result with the change of the number of super particles per cell in 1 dimensional simulation. 24

Fig 9. Benchmarking result with the change of the number of cells in 2-dimensional simulation. 24

Fig 10. Benchmarking result with the change of the number of super particles per cell in 2-dimensional simulation. 25

초록보기

 Particle-in-Cell (PIC) is a method extensively employed in plasma simulations, particularly in research fields such as astrophysics and laser-plasma, due to its ability to accurately represent the kinematic characteristics of plasmas. Due to our focus on laser-plasma research, our laboratory actively employs PIC simulations. However, the current code we employ faces certain limitations, posing challenges to its effective utilization. In this thesis predominantly focuses on the enhancement of the existing PIC code utilized in the laboratory. The specific objectives for code improvement center on enhancing user-friendliness and reducing simulation time. To achieve these goals, a detailed analysis of PIC is conducted, along with an exploration of High-Performance Computing (HPC) and the implementation of GPU-based calculations to improve temporal efficiency. The finalized code will be evaluated through case studies to ensure its correctness, and benchmarking will be performed against both the original code and open-source alternatives to quantify the extent of the speed improvements.

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