Table 5 Strong scaling: d2o’s relative performance to a single process when increasing the number of processes while fixing the global array size to \((4096, 4096)=128~\mathrm {MiB}\)
From: d2o: a distributed data object for parallel high-performance computing in Python
Processes (local size) | 1 (128 MiB) (%) | 2 (64 MiB) (%) | 3 (42.7 MiB) (%) | 4 (32MiB) (%) | 8 (16 MiB) (%) | 16 (8 MiB) (%) | 32 (4 MiB) (%) | 64 (2 MiB) (%) | 128 (1 MiB) (%) | 256 (512 KiB) (%) |
|---|---|---|---|---|---|---|---|---|---|---|
initialization | 100.0 | 40.3 | 23.7 | 17.1o | 6.80 | 2.55 | 0.89 | 0.29 | 0.10, | 0.03 |
copy .empty | 100.0 | 47.8 | 33.5 | 26.3 | 23.2 | 12.1 | 6.16 | 3.09 | 1.55 | 0.79 |
max | 100.0 | 99.0 | 96.7 | 94.0 | 80.1 | 64.3 | 29.6 | 9.10 | 2. 41 | 0.60 |
sum | 100.0 | 100.4 | 95.4 | 91.7 | 74.1 | 60.9 | 24-4 | 7.05 | 1.82 | 0.45 |
sum(axis\(\,=\,\)0) | 100.0 | 98.0 | 94.2 | 89.9 | 62.2 | 45.3 | 19.8 | 6.34 | 1.78: | 0.45 |
sum(axis\(\,=\,\)1) | 100.0 | 100.5 | 92.3 | 92.6 | 79.0 | 77.7 | 47.1 | 20.6 | 4. 27 | 1.25 |
obj[::-2] | 100.0 | 65.4 | 62.4 | 58.5 | 40.7 | 33.1 | 26.6 | 18.3 | 8. 78 | 3.25 |
copy | 100.0 | 103.6 | 105.9 | 98.0 | 145.1 | 156.4 | 155.3 | 152.3 | 157.5 | 306.7 |
obj \(+\) 0 | 100.0 | 105.9 | 109.1 | 100.4 | 59.0 | 97.3 | 75.7 | 79.2 | 79.4 | 149.8 |
obj \(+\) obj | 100.0 | 106.2 | 109.2 | 100.3 | 59.0 | 97.6 | 74.9 | 79.0 | 80.1 | 150.2 |
obj \(+=\) obj | 100.0 | 103.1 | 101.3 | 98.0 | 97.8 | 124.0 | 122.8 | 117.9 | 108.1 | 94.4 |
sqrt | 100.0 | 101.8 | 99.4 | 98.7 | 95.7 | 88.8 | 76.0 | 56.0 | 37.1 | 16.4 |
bincount | 100.0 | 102.3 | 99.5 | 98.0 | 111.1 | 107.3 | 84.2 | 40.5 | 13.2 | 3.57 |