# Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations
## 5 Discussion  
In computational simulations, balancing efficiency and power consumption is a paramount challenge, especially with Monte Carlo-based algorithms like the Metropolis algorithm. These methods require several iterations to ensure statistical significance, leading to higher computational complexity and increased power consumption. This paper addresses these challenges by leveraging GPU acceleration to enhance performance while minimizing energy usage, highlighting key findings and comparing them with several works in the field. The simulations presented in the previous section showed that GPU acceleration significantly reduces computation time and energy consumption compared to traditional CPU-based approaches. Substantial reductions in energy consumption were achieved through optimized GPU usage, keeping high computational efficiency without excessive energy costs. In order to contextualize these contributions, the results are compared with existing studies, shown in Table 4, highlighting the differences in focus and outcomes. The study by [PERSON] et al. [7] proposed a parallel implementation for the CPM using software transactional memory, achieving a maximum speedup of 10.76\(\times\) with 12 parallel tasks, but it did not address power consumption. Another study introduced a 3D parallelization scheme for FEM coupling simulations using GPU acceleration, reducing the simulation time by over 80% compared to serial computations, but it did not address energy consumption [18]. [PERSON] et al. [19] proposed a hybrid parallel framework combining CPU and GPU resources for CPM simulations, achieving appreciable reductions in computation time by distributing tasks among CPUs and GPUs. A further study presented a parallel approximate Bayesian computation sequential Monte Carlo algorithm for high-performance computing clusters, significantly improving the computational efficiency of multi-scale biological process simulations [20]. [PERSON] et al. [21] explored GPU acceleration for the CPM to improve simulation times, reporting significant improvements but not addressing energy consumption. [PERSON] et al. [22] investigated various parallel computing techniques to improve the efficiency of biological simulations, achieving enhanced computational efficiency but not extensively treating energy consumption. This paper distinguishes itself by incorporating energy efficiency as a key component, ensuring that the benefits of accelerated computation do not come at the expense of increased power consumption. The optimized use of GPU resources leads to a notable decrease in power usage compared to traditional CPU-based methods, balancing performance and energy efficiency. In conclusion, the proposed work demonstrates that GPU acceleration, through optimized computational strategies, can achieve significant improvements in both computational efficiency and energy consumption. Comparing the findings with the existing literature highlights the unique contributions of this approach, particularly in the context of Monte Carlo-based simulations. The focus on energy efficiency addresses a critical gap in the field, providing a sustainable solution for high-performance computational simulations. This holistic approach ensures that the benefits of accelerated computation are performed without incurring high energy costs, making it a valuable contribution to the development of efficient and sustainable computational methods. The proposed software is very helpful for researchers, enabling large-scale real-time simulations of tumor growth and angiogenesis, reducing the time required to obtain results compared to traditional experiments. This \begin{table}
\begin{tabular}{l c c} \hline \hline
**M** & **Power-GPU\({}_{GS}\)** & **Power-GPU\({}_{CG}\)** \\ \hline
150 & 29.43 & 22.71 \\ \hline
300 & 67.79 & 17.68 \\ \hline
450 & 124.05 & 31.09 \\ \hline
750 & 151.01 & 70.22 \\ \hline
900 & 179.89 & 83.81 \\ \hline \hline \end{tabular}
\end{table}