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Rename weak_parallel to weak_threaded
1 parent df1dd2b commit 316a563

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Lines changed: 6 additions & 6 deletions

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‎docs/command-line-options.md‎

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@@ -18,7 +18,7 @@ The Extra-P command line interface has the following options.
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| `--talpas` | Load data from Talpas data format |
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| `--text` | Load data from text input file |
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| `--experiment` | Load Extra-P experiment and generate new models |
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| `--scaling` {`weak`, `weak_parallel`, `strong`} | Set scaling type when loading data from per-thread/per-rank files (CUBE files) (default: weak) |
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| `--scaling` {`weak`, `weak_threaded`, `strong`} | Set scaling type when loading data from per-thread/per-rank files (CUBE files) (default: weak) |
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| --keep-values | Keeps the original values after import |
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| **Modeling options** | |
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| `--median` | Use median values for computation instead of mean values |

‎extrap/entities/scaling_type.py‎

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Original file line numberDiff line numberDiff line change
@@ -10,7 +10,7 @@
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class ScalingType(Enum):
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WEAK = "weak"
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WEAK_PARALLEL = "weak_parallel"
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WEAK_THREADED = "weak_threaded"
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STRONG = "strong"
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def __str__(self):

‎extrap/fileio/file_reader/cube_file_reader2.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -153,7 +153,7 @@ def _aggregate_repetitions_legacy(self, point_group, progress_bar, show_warning_
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cnode_values = metric_values.cnode_values(r_cnode, convert_to_inclusive=True)
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if self.scaling_type == ScalingType.WEAK:
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total_values[callpaths[r_cnode.id]].extend(map(float, cnode_values))
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elif self.scaling_type == ScalingType.WEAK_PARALLEL:
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elif self.scaling_type == ScalingType.WEAK_THREADED:
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values = [v for v in map(float, cnode_values) if v != 0]
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if not values:
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values = map(float, cnode_values)
@@ -175,7 +175,7 @@ def _aggregate_repetitions_legacy(self, point_group, progress_bar, show_warning_
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if self.scaling_type == ScalingType.WEAK:
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# do NOT use generator it is slower
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aggregated_values[(callpath, metric)].extend(map(float, cnode_values))
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elif self.scaling_type == ScalingType.WEAK_PARALLEL:
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elif self.scaling_type == ScalingType.WEAK_THREADED:
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values = [v for v in map(float, cnode_values) if v != 0]
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if not values:
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values = map(float, cnode_values)
@@ -218,7 +218,7 @@ def _aggregate_repetitions(self, point_group, progress_bar, show_warning_skipped
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cnode_values = metric_values.cnode_values(r_cnode, convert_to_inclusive=True)
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if self.scaling_type == ScalingType.WEAK:
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total_values[callpaths[r_cnode.id]].append(cnode_values.astype(float))
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elif self.scaling_type == ScalingType.WEAK_PARALLEL:
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elif self.scaling_type == ScalingType.WEAK_THREADED:
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values = cnode_values.astype(float)
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non_zero_value_mask = values != 0
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masked_array = ma.array(values, mask=non_zero_value_mask)
@@ -239,7 +239,7 @@ def _aggregate_repetitions(self, point_group, progress_bar, show_warning_skipped
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# in case of weak scaling calculate mean and median over all mpi process values
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if self.scaling_type == ScalingType.WEAK:
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aggregated_values[(callpath, metric)].append(cnode_values.astype(float))
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elif self.scaling_type == ScalingType.WEAK_PARALLEL:
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elif self.scaling_type == ScalingType.WEAK_THREADED:
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values = cnode_values.astype(float)
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non_zero_value_mask = values != 0
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aggregated_values[(callpath, metric)].append(ma.array(values, mask=non_zero_value_mask))

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