Skip to main content

Simulations, Data Analysis and Algorithms

Table 2 Runtime diagnostics for the parallelization of phew when various numbers of MPI tasks are used. \(\pmb{N_{\mathrm{active}}}\) and \(\pmb{N_{\mathrm{ghost}}}\) are the number of active peaks and ghost peaks respectively and \(\pmb{N_{\mathrm{tot}}=N_{\mathrm{active}}+N_{\mathrm{ghost}}}\) denotes the total number of peaks per MPI task. \(\pmb{N_{\mathrm{sparse}}}\) is the number of entries in the sparse saddle matrix and \(\pmb{N_{\mathrm{collisions}}}\) gives the number of hash table collisions. Sums, maxima and averages are taken over the all MPI tasks

From: PHEW: a parallel segmentation algorithm for three-dimensional AMR datasets

\(\boldsymbol {N}_{\mathbf{tasks}}\) 32 64 128 256 512 1,024 2,048
Load imbalance \((\frac{\max\{ N_{\mathrm{tot}} \}}{ \mathrm{avg} \{ N_{\mathrm{tot}}\}} )\) 1.4 1.5 1.8 2.4 2.8 3.3 3.9
Surface effect \((\frac{\sum N_{\mathrm{ghost}}}{\sum N_{\mathrm{active}}} )\) 0.0087 0.012 0.016 0.021 0.030 0.040 0.055
Connectivity \((\frac{\sum N_{\mathrm{sparse}}}{\sum N_{\mathrm{tot}}} )\) 9.4 9.4 9.4 9.3 9.3 9.3 9.2
\(\max \{ \frac{N_{\mathrm{ghost}}}{N_{\mathrm{active}}} \}\) 0.012 0.017 0.044 0.064 0.10 0.15 0.24
\(\max\{N_{\mathrm{tot}}\}\) 3.0 × 105 1.6 × 105 9.6 × 104 6.4 × 104 3.8 × 104 2.2 × 104 1.3 × 104
\(\max\{N_{\mathrm{sparse}}\}\) 3.3 × 106 1.8 × 106 1.2 × 106 8.7 × 105 6.3 × 105 4.7 × 105 3.0 × 105
\(\max\{N_{\mathrm{collisions}}\}\) 4 3 2 3 16 17 13