## 4 Discussion  
The results presented in this study highlight the significant performance advantages of GPU-based AES encryption over traditional CPU-based methods, particularly when handling large datasets. As demonstrated in Figures 6 and 7, GPU acceleration substantially reduces encryption times, making it a viable solution for data-intensive applications such as cloud computing and real-time processing. This aligns with previous studies that emphasized the benefits of GPU parallelism for cryptographic operations, but our study extends these findings by integrating a hybrid CPU-GPU approach tailored for optimal workload distribution [25; 26; 27]. The AES-CTR mode's inherent parallelism facilitated efficient GPU utilization, while the CBC mode, though challenging due to its sequential dependency, benefited from our proposed hybrid methods. The bit-slicing parallelization method showed the highest performance improvement (35-40%), consistent with the theoretical advantages of bit-level parallelism highlighted. The adaptive workload distribution method, providing 15-25% improvement, reinforces the importance of dynamic task allocation in heterogeneous computing environments [19; 28]. Our results also illustrate that while hardware advancements (such as increased CUDA cores and memory bandwidth in the RTX 3080) contribute to performance gains, the architectural differences among GPUs have a lesser impact than the employed encryption strategies. This finding is supported by prior research and underscores the importance of algorithmic optimization alongside hardware selection [28; 29; 30]. Although our analysis identifies the number of CUDA cores and memory bandwidth as key contributors to GPU performance improvements, a deeper exploration into the interactions between GPU hardware architecture (such as warp scheduling, shared memory utilization, and cache hierarchies) and AES encryption algorithms would provide further insight. Profiling kernel execution patterns, analyzing memory access behaviors, and assessing cache hit/miss rates would provide valuable insights into optimizing encryption performance. Future studies should conduct in-depth profiling using GPU performance monitoring tools (e.g., NVIDIA Nsight and nvprof) to precisely quantify these interactions and further enhance the optimization of AES implementations on modern GPU architectures. Detailed profiling analysis, such as the examination of kernel execution patterns, shared memory access, and cache hit rates, should be conducted in future work to precisely quantify how these architectural features affect cryptographic performance. Such analyses would enhance understanding of the critical factors that optimize GPU-based encryption workflows and allow further optimization of AES implementations. The hybrid CPU-GPU workflow, detailed in Figure 8, offers a balanced approach by leveraging the strengths of both CPUs (for sequential tasks and control operations) and GPUs (for parallel data processing). This synergy not only enhances performance but also optimizes resource utilization, making it particularly suitable for large-scale data encryption in distributed systems and cloud environments. Furthermore, our study introduces practical techniques to address common bottlenecks in GPU-based encryption, such as I/O data transfer latency. The proposed I/O bottleneck reduction method achieved a 20% improvement by minimizing GPU idle time through CPU pre-encryption, echoing similar strategies in recent works. The implications of these findings are far-reaching. In an era where data security is paramount, our proposed hybrid encryption methods offer scalable solutions that can be integrated into the existing security infrastructures, enhancing both performance and security. Future research could explore adaptive hybrid models that dynamically adjust encryption strategies based on real-time performance metrics, further improving efficiency.