## 3 Materials and Methods
### Feature Extraction  
After collection of the sparse matrices and performing SpMV computations with random block sizes, we extracted some of the most relevant features that affect the performance of SpMV computation.  
These features of the sparse matrix are listed in Table 3, along with their computation complexity, feature description, and formulae. The feature set is divided into two subsets, that is, basic features, which have low computational complexity, and other features, which require a full scan of the matrix and have certain associated computational complexity. The feature column shows the name of the addressed feature, along with their description and formula. The computational complexity of these features is listed in the last column of Table 3.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**No.** & **Name** & **Non-Zero** & **Rows** & **Columns** & **Domains** \\ \hline
10 & rbsa480 & 17,088 & 480 & 480 & Robotics Problem \\
11 & Hardesty2 & 4,020,731 & 929,901 & 303,645 & Computer Graphics/Vision Problem \\
12 & nv2 & 377,475,646 & 1,453,908 & 1,453,908 & Semiconductor Device Problem \\
13 & hangGilder\_3 & 149,532 & 15,561 & 15,561 & Optimal Control Problem \\
14 & vsp\_model1\_crew1\_crew1\_c42\_south31 & 379,952 & 45,101 & 45,101 & Random Unweighted Graph \\
15 & NLR & 24,975,952 & 4,163,763 & 4,163,763 & Undirected Graph \\
16 & FX\_March2010 & 301,899 & 1319 & 9498 & Term/Document Graph \\
17 & Curl\_Curl\_0 & 113,434 & 11,083 & 11,083 & Model Reduction Problem \\
18 & preferential Attachment & 999,970 & 100,000 & 100,000 & Random Undirected Graph \\
19 & kron\_g500-logn19 & 43,562,265 & 524,288 & 524,288 & Undirected Multigraph \\
20 & coc & 4,194,298 & 1,048,56 & 1,048,576 & Undirected Graph Sequence \\
21 & dielFilterV2 ck & 25,309,272 & 607,232 & 607,232 & Electromagnetics Problem \\
22 & S20 PL\_n & 2881 & 1182 & 1182 & Eigenvalue/Model Reduction Problem \\
23 & bp\_1200 & 4726 & 822 & 822 & Optimization Problem Sequence \\
24 & lp\_bnl2 & 14,996 & 2324 & 4486 & Linear Programming Problem \\
25 & onetone2 & 222,596 & 36,057 & 36,057 & Frequency Domain Circuit Simulation Problem \\
26 & nemeth01 & 725,054 & 9506 & 9506 & Quantum Chemistry Problem Sequence \\
27 & nemeth02 & 394,808 & 9506 & 9506 & Theoretical/Quantum Chemistry Problem \\
28 & young1c & 4089 & 841 & 841 & Acoustics Problem \\
29 & shl\_200 & 1726 & 663 & 663 & Subsequent Optimization Problem \\
30 & gemat12 & 33,044 & 4929 & 4929 & Subsequent Power Network Problem \\
31 & fs\_760\_2 & 5739 & 760 & 760 & Subsequent 2D/3D Problem \\
32 & impcol\_a & 572 & 207 & 207 & Chemical Process Simulation Problem \\
33 & beacx & 50,409 & 497 & 506 & Economic Problem \\
34 & gyro\_k & 1,021,159 & 17,361 & 17,361 & Duplicate Model Reduction Problem \\
35 & flowmeter0 & 67,391 & 9669 & 9669 & Model Reduction Problem \\
36 & Pd & 13036 & 8081 & 8081 & Counter Example Problem \\
37 & GD00\_c & 1041 & 638 & 638 & Directed Multigraph \\
38 & thermal2 & 8,580,313 & 1,228,045 & 1,228,045 & Thermal Problem \\
39 & bibl\_18\_9 & 1,750,320 & 153 & 48,620 & Combinatorial Problem \\
40 & mawi\_201512012 & 38,040,320 & 18,571,154 & 18,571,154 & Undirected Weighted Graph \\
41 & circuit\_1 & 35,823 & 2624 & 2624 & Circuit Simulation Problem \\
42 & fs\_541\_1 & 4282 & 541 & 541 & 2D/3D Problem Sequence \\
43 & Cities & 1342 & 55 & 46 & Weighted Bipartite Graph \\ \hline \end{tabular}
\end{table}
Table 3: Sparse matrices features with definition and complexity.