Table 4 Weak scaling: d2o’s relative performance to the single-process case when increasing both, the number of processes and the global array size proportionally
From: d2o: a distributed data object for parallel high-performance computing in Python
Process count | 1 (%) | 2 (%) | 3 (%) | 4 (%) | S (%) | 16 (%) | 32 (%) | 64 (%) | 128 (%) | 256 (%) |
|---|---|---|---|---|---|---|---|---|---|---|
initialization | 100.0 | 90.9 | 87.9 | 87.8 | 74.6 | 67.6 | 54.9 | 45.7 | 34.6 | 19.9 |
copy .empty | 100.0 | 97.5 | 96.2 | 97.5 | 97.6 | 103.6 | 97.8 | 97.7 | 97.6 | 95.1 |
max | 100.0 | 97.5 | 96.6 | 95.6 | 90.9 | 84.0 | 72.1 | 56.2 | 39.1 | 24.3 |
sum | 100.0 | 98.0 | 95.3 | 93.5 | 87.3 | 79.2 | 65.1 | 48.3 | 32.2 | 19.2 |
sum(axis\(\,=\,\)0) | 100.0 | 100.2 | 96.7 | 96.5 | 90.9 | 78.1 | 74.6 | 58.0 | 42.7 | 28.2 |
sum(axis\(\,=\,\)1) | 100.0 | 105.2 | 103.2 | 102.2 | 100.6 | 100.0 | 98.3 | 95.8 | 93.2 | 88.6 |
obj[::-2] | 100.0 | 70.4 | 65.9 | 64.0 | 46.2 | 46.6 | 42.8 | 33.6 | 31.1 | 25.3 |
copy | 100.0 | 104.7 | 103.1 | 101.3 | 101.3 | 105.3 | 101.4 | 101.2 | 101.3 | 101.5 |
obj \(+\) 0 | 100.0 | 105.1 | 102.6 | 100.6 | 99.9 | 103.5 | 100.2 | 100.0 | 99.7 | 100.1 |
obj \(+\) obj | 100.0 | 105.2 | 102.5 | 100.1 | 100.0 | 103.7 | 100.1 | 100.1 | 99.8 | 100.2 |
obj \(+\) = obj | 100.0 | 102.3 | 99.3 | 98.6 | 98.2 | 101.8 | 98.2 | 98.2 | 98.2 | 98.4 |
sqrt | 100.0 | 102.0 | 100.6 | 100.1 | 99.6 | 99.1 | 99.2 | 99.2 | 8.6 | 98.0 |
bincount | 100.0 | 103.0 | 101.2 | 99.9 | 98.8 | 97.6 | 94.1 | 88.3 | 79.4 | 65.8 |