## 5 Discussion
### Effects of Dimensionality Reduction and Subsequence Selection on Performance  
This subsection analyzes the impact of dimensionality reduction and subsequence selection on training efficiency, computational requirements, and classification performance in both Q3/Q8 tasks across different hardware configurations. **High-dimensional embeddings** (e.g., 1024D) frequently exceeded GPU memory capacity, necessitating CPU-based training. This substantially increased training times--especially for larger datasets--highlighting a critical scalability issue. In contrast, dimensionality reduction using autoencoders proved effective in maintaining performance while enabling GPU-based acceleration. A key trade-off was observed between subsequence length and computational load. Longer subsequences preserved broader contextual dependencies essential for accurate prediction but incurred higher memory usage and slower convergence. Conversely, shorter subsequences reduced computational burden but risked losing crucial long-range interactions. As shown in Table 8, compressing the embedding dimension from 1024D to 256D reduced Q8 training time by 92.3% (from 13,217 s to 1057 s) for 25-length subsequences. However, extreme reduction to 32D increased training time relative to 64D due to more training epochs being required to compensate for information loss. Notably, the 64D-100L configuration achieved the lowest training time (582 s), likely due to efficient batch fitting in GPU memory. For Q3 classification (Table 9), reducing from 1024D to 256D led to a 94.4% decrease in training time (from 16,804 s to 937 s). Interestingly, the 32D-75L configuration yielded the fastest training (524 s), despite the representation's sparsity--indicating potential convergence instability in extremely compressed embeddings. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Dimension**} & \multicolumn{5}{c}{**Time (s)**} & \multicolumn{5}{c}{**Platform**} \\ \cline{2-9}  & **25** & **50** & **75** & **100** & **25** & **50** & **75** & **100** \\ \hline
1024 & 13217 & 16868 & 16844 & 21187 & CPU & CPU & CPU & CPU \\
512 & 25262 & 25893 & 40629 & 41112 & CPU & CPU & CPU & CPU \\
256 & 1057 & 1145 & 1373 & 1150 & GPU & GPU & GPU & GPU \\
128 & 1035 & 1242 & 1370 & 1653 & GPU & GPU & GPU & GPU \\
64 & 1051 & 1321 & 1576 & **582** & GPU & GPU & GPU & GPU \\
32 & 1109 & 1296 & 1720 & 1733 & GPU & GPU & GPU & GPU \\ \hline \end{tabular}
\end{table} 
Table 8: Training times (seconds) and platforms for Q8 classification. Across all configurations, Q3 classification consistently required 37% less training time than Q8, which is attributable to the reduced class complexity. Furthermore, 50-residue subsequences achieved a favorable balance between computational efficiency and contextual integrity, aligning with structural studies on local folding windows [20]. GPU acceleration was only feasible below 512 dimensions due to Video Random-Access Memory (VRAM) constraints. While 1024D models suffered from memory swapping, the 256D models ran up to 8.9\(\times\) faster, with manageable VRAM consumption (10.1 GB vs. 31.1 GB). Thus, the 512D setting emerges as a practical upper limit for GPU viability in current hardware environments. Key Observations:  
1. **Memory Optimization:** Reducing from 1024D to 256D decreased VRAM usage by 67.4% (31.1 GB to 10.13 GB). 2. **Speed-Accuracy Trade-off:** The 50-residue subsequences maintained 99% accuracy while reducing training time by 43%. 3. **Hardware-Aware Design:** GPU acceleration is viable only for models below 512D, emphasizing the value of dimensionality control. 4. **Model Complexity:** Q8 classification required 2.7\(\times\) more epochs than Q3 to reach equivalent convergence. These findings confirm that the combination of dimensionality reduction and adaptive subsequence generation enables computationally efficient protein structure prediction without compromising accuracy.