## 3 Results
### Performance of Tag Index Partitioning Algorithm  
Before performing tag index block partitioning, it is necessary to pre-calculate the storage space requirements of each index block based on the number distribution of different tag types. This experiment is based on NCBI-nr. To generate a test dataset, we obtain sub-databases with gradient tag quantities through systematic sampling; the 5-mer tag index is generated, and its storage space occupancy is measured. The purpose of this experiment is to verify the effectiveness of estimating index storage cost using the number of tags. Figure 9 shows the actual storage cost and the relative error between the actual and theoretical index space for different tag counts. The error is caused by the fact that the size of the tag index is not only related to the number of tags, but also to the numerical size of the information within the tag index. The results show that there is a significant linear correlation between storage space occupancy and tag quantity, and the trend of change closely matches the theoretical prediction, with the maximum relative error being 0.065%. The experimental results effectively validated the mathematical validity and engineering applicability of estimating tag index  
Figure 8: Ablation experiment on FASTA files from ten species. Storage cost of index generation algorithms after removing tag sequence encoding, index dimensionality reduction, delta encoding, dynamic bit width encoding, and all compression methods from STIP. Figure 9: Performance of tag index storage cost estimations on 10 protein databases of different sizes. storage cost using the number of tags, providing reliable index space prediction results for tag index partitioning algorithms. In addition to the tag index partitioning method based on greedy algorithm used in this article, there is also a method that divides the tag index by evenly dividing the number of tag types. In order to facilitate differentiation in subsequent comparative experiments, we call the former Greedy Load Balancing Partitioning (GLP) and the latter Uniform Lexicographical Partitioning (ULP). To verify indexing block performance of GLP for large-scale databases, we selected the NCBI-nr database as a test database. Since 5-mer tags are the most used in practical applications, this experiment generates a 5-mer tag index and compares it with an equal dictionary sequence partitioning (ULP). ULP partitions the tag index by evenly distributing the types of tag within the index. Block utilization rate refers to the degree of effective usage of a storage block. It measures how much of the available space within a block is actually used to store data. A higher block utilization rate indicates that a larger portion of the block is being used, while a lower rate suggests that the block has a significant amount of unused space, potentially leading to inefficiencies. The formula is as follows:  
\[\text{Block utilization rate }=x/M \tag{10}\]  
where \(x\) is the amount of data actually stored in the block, and \(M\) is the total available capacity of the block. Figure 10 shows the performance of ULP and GLP. The GLP strategy fully utilizes memory resources while adhering to the set memory threshold. A total of 95.83% of the index blocks in the GLP strategy achieve a saturation rate of 99.98% or higher. Additionally, the GLP strategy generates 24 blocks, which is a 54% reduction compared to the equal dictionary sequence partitioning strategy. This results in more concentrated index information, making subsequent retrieval easier. The different partitioning strategies may affect retrieval time, primarily because multiple calls to the tag index blocks are made during retrieval. Figure 11 shows the I/O operations during the retrieval process for the label indices generated using the two strategies. The GLP strategy reduces operations I/O by 54% compared to ULP.