## 6 Conclusions  
The analysis of GPU-based implementations of metaphorless optimization algorithms, conducted within the framework of a standardized parallelization strategy, has yielded insightful conclusions regarding the efficacy, performance characteristics, and scalability of parallel computation across a diverse set of large-scale NESs with varying dimensions, tested on different GPU hardware configurations. The utilization of parallel computational resources on GPUs proved highly effective in enhancing algorithmic performance. The GPU-based versions of the tested metaphorless optimization algorithms consistently demonstrated superior efficiency compared to their sequential counterparts. In the detailed analysis conducted, the achieved speedup ranged from a minimum of 33.9\(\times\) to a maximum of 561.8\(\times\). The observed speedup gains showed a proportional correlation with the augmentation of the problem dimension. Smaller dimensions resulted in more modest speedup gains, while larger dimensions led to more notable improvements. This behavior aligns with the adaptive nature of the parallelization strategy, dynamically adjusting GPU processing capabilities based on problem complexity. The efficiency of GPU-based implementations varied among different optimization algorithms. The Rao-3 algorithm demonstrated exceptional efficiency in leveraging parallel processing capabilities, achieving the highest average speedup across all tested dimensions. Conversely, algorithms with more intricate mathematical models, such as EJAYA, exhibitedmore modest yet still substantial speedup values. This observation underscores the impact of algorithmic complexity on the effectiveness of parallelization. These observations were derived from results obtained with both the RTX 3090 and A100 GPUs, with the latter consistently outperforming the former despite having fewer CUDA cores. Moreover, the RTX 3090 GPU displayed higher sensitivity to variations in problem complexity, resulting in greater variability in computational performance. On the other hand, the A100 GPU showcased more stability and predictability, delivering consistent performance across different problem dimensions. This underscores the nuanced influence of GPU hardware characteristics on algorithmic efficiency. The dynamic adaptability of the parallelization strategy to various GPU hardware configurations was further analyzed. Results revealed that all tested GPUs exhibited positive speedup ratios across all algorithms and dimensions, leading to the emergence of a performance hierarchy. The Tesla T4 demonstrated the lowest overall performance, followed by the RTX 3090, the Tesla V100S, and finally, the A100, which showcased the highest performance. This performance hierarchy is closely aligned with the intrinsic hardware capabilities of each GPU, indicating that the parallelization strategy effectively capitalized on the specific architectural features of each hardware. Furthermore, the results highlighted that while individual factors such as the number of cores and memory bandwidth play a crucial role in determining the computational capacity of a GPU, they only offer a partial view of the overall performance. Such nuances underscored the importance of interpreting performance results within the context of real-world applications. In GPU programming, the pursuit of optimal performance should be harmonized with some degree of flexibility. While maximizing performance remains crucial, overly rigid optimized strategies may restrict algorithmic parameters or compatibility with different GPU hardware. The parallel implementation proposed in this paper showcases the capability of a generic approach to efficiently manage GPU resource allocation while providing scalability across different hardware configurations. This adaptability is achieved automatically, without requiring any source code modification, ensuring that applications remain responsive to evolving computational needs and effectively harness the potential of GPUs for general-purpose computing.