Table 3 Overhead costs: d2o’s relative performance to numpy when scaling the array size and being run with one MPI-process
From: d2o: a distributed data object for parallel high-performance computing in Python
Array size | 2\(^{\mathrm {0}}\) (%) | 2\(^{\mathrm {2}}\) (%) | 2\(^{\mathrm {4}}\) (%) | 2\(^{\mathrm {6}}\) (%) | 2\(^{\mathrm {8}}\) (2 KiB) (%) | 2\(^{\mathrm {10}}\) (%) | 2\(^{\mathrm {12}}\) (%) | 2\(^{\mathrm {14}}\) (128 KiB) (%) | 2\(^{\mathrm {16}}\) (%) | 2\(^{\mathrm {18}}\) (%) | 2\(^{\mathrm {20}}\) (8 MiB) (%) | \(2_{\mathrm {22}}\) (%) | 2\(^{\mathrm {23}}\) (%) | 2\(^{\mathrm {24}}\) (128 MiB) (%) | 2\(^{\mathrm {25}}\) (%) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
initialization | 0.65 | 0.64 | 0.69 | 0.69 | 0.71 | 0.71 | 0.74 | 0.72 | 0.75 | 0.74 | 0.75 | 5.41 | 5.58 | 5.83 | 6.00 |
copy .empty | 3.77 | 3.86 | 4.00 | 4.12 | 4.57 | 3.92 | 3.95 | 3.98 | 4.01 | 4.10 | 4.15 | 25.0 | 24.2 | 25.3 | 26.0 |
max | 56.2 | 56.0 | 54.4 | 55.5 | 56.0 | 56.9 | 62.6 | 79.2 | 91.7 | 97.5 | 99.4 | 99.4 | 99.9 | 99.8 | 99.9 |
sum | 59.7 | 59.0 | 57.5 | 60.1 | 58.6 | 59.8 | 62.5 | 74.8 | 88.4 | 95.9 | 99.0 | 99.3 | 99.7 | 99.7 | 100.0 |
obj[::-2] | 1.18 | 1.17 | 1.20 | 1.24 | 1.34 | 1.50 | 2.14 | 4.77 | 15.0 | 22.2 | 28.7 | 24.1 | 45.8 | 47.3 | 47.7 |
copy | 8.16 | 8.86 | 9.30 | 9.72 | 9. 66 | 10.4 | 14.8 | 26.7 | 65.8 | 95.5 | 98.7 | 99.1 | 99.9 | 99.4 | 98.4 |
obj \(+\) 0 | 6.58 | 6.48 | 6.67 | 7.08 | 7.37 | 9.51 | 16.2 | 35.0 | 65.2 | 34.7 | 44.7 | 98.7 | 99.7 | 96.5 | 100.0 |
obj \(+\) obj | 3.07 | 3.10 | 3.24 | 3.59 | 3.98 | 5.70 | 13.3 | 31.9 | 64.0 | 34.6 | 45.0 | 98.7 | 99.9 | 99.5 | 99.8 |
obj \(+=\) obj | 5.17 | 5.35 | 5.41 | 6.02 | 6.66 | 11.3 | 22.8 | 46.4 | 75.3 | 91.9 | 97.8 | 98.0 | 99.8 | 99.3 | 99.7 |
sqrt | 3.25 | 3.17 | 3.26c | 3.50 | 4.42 | 7.49 | 18.7 | 45.6 | 75.8 | 65.2 | 83.2 | 98.8 | 99.4 | 99.6 | 99.8 |
bincount | 3.57 | 3.35 | 3.76 | 4.04 | 4.97 | 7.54 | 16.5 | 35.4 | 58.0 | 75.1 | 78.0 | 76.8 | 78.9 | 82.1 | 83.2 |