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DM-53707: Compute integrated allocated core hours. #452

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DM-53707: Compute integrated allocated core hours. #452

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11 changes: 7 additions & 4 deletions python/lsst/analysis/tools/tasks/gatherResourceUsage.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,7 +127,10 @@ def run(self, **kwargs: Any) -> Struct:
).reset_index()
df["task"] = input_name.replace("_resource_usage", "")
df["quanta"] = len(ru_table)
df["integrated_runtime"] = ru_table["run_time"].sum()
df["integrated_runtime_hrs"] = ru_table["run_time"].sum() / 3600.0
# USDF nodes have 120 CPU and 480GiB RAM for ~4GiB per CPU
integrated = (ru_table["run_time"] * ru_table["memory"]).sum()
df["integrated_allocated_core_hrs"] = integrated / 4 / 1024**3 / 3600.0

quantiles.append(
df[
Expand All @@ -139,7 +142,8 @@ def run(self, **kwargs: Any) -> Struct:
"init_time",
"run_time",
"wall_time",
"integrated_runtime",
"integrated_runtime_hrs",
"integrated_allocated_core_hrs",
]
]
)
Expand All @@ -148,7 +152,6 @@ def run(self, **kwargs: Any) -> Struct:
full_quantiles["percentile"] = (full_quantiles["index"] * 100).astype(int)
full_quantiles["percentile_name"] = "p" + full_quantiles["percentile"].astype(str).str.zfill(3)
full_quantiles["memoryGB"] = full_quantiles["memory"] / 1024 / 1024 / 1024
full_quantiles["integrated_runtime_hrs"] = full_quantiles["integrated_runtime"] / 3600.0
memoryGB = pd.pivot_table(
full_quantiles, values="memoryGB", columns=["percentile_name"], index=["task"]
).add_prefix("mem_GB_")
Expand All @@ -170,7 +173,7 @@ def run(self, **kwargs: Any) -> Struct:
right_on="task",
)
memrun = pd.merge(
full_quantiles[["task", "quanta", "integrated_runtime_hrs"]]
full_quantiles[["task", "quanta", "integrated_runtime_hrs", "integrated_allocated_core_hrs"]]
.drop_duplicates()
.sort_values("task"),
memrun,
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