## 2 Methods
### CUDA Implementation of Greedy Agglomerative Procedures
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm or Neighborhood**} & \multicolumn{4}{c}{**Achieved Objective Function Values (1) Summarized after 30 Runs**} \\ \cline{2-5}  & **Min (Record)** & **Max (Worst)** & **Average** & **Median** & **Std. Dev** \\ \hline \(\mathrm{AGGL_{7}}\) & \(1.49341\times 10^{9}\) & \(1.59630\times 10^{9}\) & \(1.51531\times 10^{9}\) & \(1.49515\times 10^{9}\) & \(3.58332\times 10^{7}\) \\ \(\mathrm{AGGL_{10}}\) & \(1.49261\times 10^{9}\) & \(1.60585\times 10^{9}\) & \(1.51345\times 10^{9}\) & \(1.50733\times 10^{9}\) & \(2.79785\times 10^{7}\) \\ \(\mathrm{AGGL_{12}}\) & \(1.49358\times 10^{9}\) & \(1.61554\times 10^{9}\) & \(1.51105\times 10^{9}\) & \(1.49754\times 10^{9}\) & \(3.16092\times 10^{7}\) \\ \(\mathrm{AGGL_{15}}\) & \(1.49414\times 10^{9}\) & \(1.57599\times 10^{9}\) & \(1.50791\times 10^{9}\) & \(1.49667\times 10^{9}\) & \(2.39108\times 10^{7}\) \\ \(\mathrm{AGGL_{20}}\) & \(1.49406\times 10^{9}\) & \(1.57432\times 10^{9}\) & \(1.51096\times 10^{9}\) & \(1.50880\times 10^{9}\) & \(1.83795\times 10^{7}\) \\ \(\mathrm{AGGL_{25}}\) & \(1.49386\times 10^{9}\) & \(1.53280\times 10^{9}\) & \(1.51017\times 10^{9}\) & \(1.51159\times 10^{9}\) & \(1.23389\times 10^{7}\) \\ \(\mathrm{AGGL_{30}}\) & \(1.49355\times 10^{9}\) & \(1.53113\times 10^{9}\) & \(1.50656\times 10^{9}\) & \(1.50311\times 10^{9}\) & \(1.12716\times 10^{7}\) \\ \(\mathrm{AGGL_{50}}\) & \(1.49380\times 10^{9}\) & \(1.52818\times 10^{9}\) & \(1.50495\times 10^{9}\) & \(1.50073\times 10^{9}\) & \(1.11666\times 10^{7}\) \\ \(\mathrm{AGGL_{75}}\) & \(1.49415\times 10^{9}\) & \(1.56935\times 10^{9}\) & \(1.51541\times 10^{9}\) & \(1.51203\times 10^{9}\) & \(1.95288\times 10^{7}\) \\ \(\mathrm{AGGL_{100}}\) & \(1.49413\times 10^{9}\) & \(1.56603\times 10^{9}\) & \(1.51133\times 10^{9}\) & \(1.49943\times 10^{9}\) & \(2.01570\times 10^{7}\) \\ \(\mathrm{GH-VNS1}\) & \(1.49362\times 10^{9}\) & \(1.66273\times 10^{9}\) & \(1.54540\times 10^{9}\) & \(1.51469\times 10^{9}\) & \(5.07461\times 10^{7}\) \\ \(\mathrm{GH-VNS2}\) & \(1.49413\times 10^{9}\) & \(1.59073\times 10^{9}\) & \(1.50692\times 10^{9}\) & \(1.50235\times 10^{9}\) & \(1.87495\times 10^{7}\) \\ \(\mathrm{GH-VNS3}\) & \(1.49247\times 10^{9}\) & \(1.55423\times 10^{9}\) & \(1.50561\times 10^{9}\) & \(1.49497\times 10^{9}\) & \(1.84421\times 10^{7}\) \\ \(\mathrm{SWAP_{1}}\) (the best of SWAP) & \(1.70412\times 10^{9}\) & \(2.00689\times 10^{9}\) & \(1.83218\times 10^{9}\) & \(1.83832\times 10^{9}\) & \(6.04418\times 10^{7}\) \\ \(\mathrm{GA-1 POINT}\) & \(1.61068\times 10^{9}\) & \(1.85786\times 10^{9}\) & \(1.69042\times 10^{9}\) & \(1.65346\times 10^{9}\) & \(7.23930\times 10^{7}\) \\ \(\mathrm{GA-UNIFORM}\) & \(1.67990\times 10^{9}\) & \(1.92521\times 10^{9}\) & \(1.77471\times 10^{9}\) & \(1.76783\times 10^{9}\) & \(6.95451\times 10^{7}\) \\ \(\mathrm{Aggl-EA}\) & \(1.49199\times 10^{9}\) & \(1.57449\times 10^{9}\) & \(1.50670\times 10^{9}\) & \(1.49495\times 10^{9}\) & \(2.46374\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative results for BIRCH3 dataset. \(10^{5}\) data vectors in \(\mathbb{R}^{2}\), \(p=300\) centers, time limitation \(10\) s.