## 4 Experimental Results
### Sensitivity Analysis on Caching Algorithms  
The buffer cache that this article focuses on is equipped at the top of the file system layer, and its algorithm is implemented in the operating system kernel. Since the buffer cache supports not only deep learning workloads but also various kinds of applications, it typically adopts the LRU algorithm that considers temporal locality of data references and the CLOCK algorithm to approximate it. In terms of performance, the LFU (least frequently \begin{table}
\begin{tabular}{c c c c} \hline \hline
**\# of Nodes** & **Node \#** & **Original** & **Proposed** \\ \hline \multirow{2}{*}{2 nodes} & Node 1 & 11.63\% & 36.99\% \\  & Node 2 & 11.64\% & 37.52\% \\ \hline \multirow{4}{*}{4 nodes} & Node 1 & 11.87\% & 37.02\% \\  & Node 2 & 11.90\% & 37.33\% \\  & Node 3 & 11.60\% & 36.93\% \\  & Node 4 & 11.59\% & 36.98\% \\ \hline \multirow{4}{*}{8 nodes} & Node 1 & 11.95\% & 37.58\% \\  & Node 2 & 12.35\% & 36.27\% \\  & Node 3 & 11.43\% & 36.81\% \\  & Node 4 & 11.82\% & 37.55\% \\  & Node 5 & 12.01\% & 36.35\% \\  & Node 6 & 11.82\% & 37.55\% \\  & Node 7 & 11.70\% & 36.14\% \\  & Node 8 & 11.87\% & 37.26\% \\ \hline \multirow{4}{*}{16 nodes} & Node 1 & 13.14\% & 37.04\% \\  & Node 2 & 11.62\% & 37.60\% \\  & Node 3 & 12.70\% & 37.53\% \\  & Node 4 & 12.17\% & 37.75\% \\  & Node 5 & 12.21\% & 36.46\% \\  & Node 6 & 12.59\% & 37.51\% \\  & Node 7 & 12.34\% & 37.40\% \\  & Node 8 & 11.57\% & 37.45\% \\  & Node 9 & 12.49\% & 37.52\% \\  & Node 10 & 11.49\% & 37.11\% \\  & Node 11 & 11.39\% & 37.57\% \\  & Node 12 & 11.80\% & 36.78\% \\  & Node 13 & 11.66\% & 37.49\% \\  & Node 14 & 12.05\% & 37.48\% \\  & Node 15 & 12.20\% & 36.96\% \\  & Node 16 & 12.38\% & 37.39\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Effect of the proposed scheme as the number of nodes increases. used) algorithm is often superior to LRU empirically, but due to implementation efficiency, most operating systems adopt variants of LRU. The MRU (most recently used) algorithm does not show good performance in the buffer cache layer in most workload cases, so few operating systems actually adopt it. However, to further validate the effectiveness of the proposed scheme, we perform additional experiments using LFU and MRU in this subsection. Figure 10 shows the hit rate of the proposed scheme in comparison with the original random shuffling as the caching algorithm is varied. Note that this experiment is performed with the Food101 dataset and similar results can be observed for ImageNet100. As shown in Figure 10b, the performance of the proposed scheme is also improved when the buffer cache is managed by the LFU algorithm although the gap has narrowed. Specifically, the improvement of the proposed scheme is 29.3% on average and up to 66.6% in the buffer cache with LFU. As shown in Figure 10c, the performance result of the proposed scheme is almost the same as that of the original random shuffling when the buffer cache is managed by MRU. MRU evicts the most recently used block from the cache when free space is needed, so blocks cached in the previous epoch remain in the buffer cache until referenced again. For this reason, the number of hits with the MRU cache is equal to the cache size in each epoch regardless of using the proposed scheme. Actually, the philosophy of the MRU algorithm seems similar to the bundle alternating of the proposed scheme. However, replacing the buffer caching algorithm from LRU to MRU is not a simple matter because it requires the modification of the operating system kernel, raising the issue of compatibility and causes  
Figure 10: Performance effect of the proposed scheme with different caching algorithms. significant performance degradation to most other workloads.