refactor: use public priority aggregate fields

Author: mental2008

cluster-trace-gpu-v2026/scripts/build_aggregates.py

@@ -428,8 +428,8 @@ def write_job_type_outputs(
     rows = [
         {
             "job_type": job_type,
-            "used_card_hour_sum_p9300": values[0],
-            "used_card_hour_sum_p9200_9300": values[1],
+            "hp_used_gpu_hours": values[0],
+            "lp_used_gpu_hours": values[1],
         }
         for job_type, values in sorted(job_type_priority.items())
     ]
@@ -460,9 +460,9 @@ def write_gpu_hourly_req(gpu_hourly_req: dict[tuple[str, str], list[float]], out
         {
             "server_gpu_spec": spec,
             "day": day,
-            "used_card_hour_sum_p9300": vals[0],
-            "used_card_hour_sum_p9200_9300": vals[1],
-            "used_card_hour_sum": vals[2],
+            "hp_used_gpu_hours": vals[0],
+            "lp_used_gpu_hours": vals[1],
+            "total_used_gpu_hours": vals[2],
         }
         for (spec, day), vals in sorted(gpu_hourly_req.items())
     ]

cluster-trace-gpu-v2026/scripts/plotters/gpu_spec_hourly_req_trend.py

@@ -43,7 +43,7 @@ def plot() -> None:
     for spec in plot_order:
         group = df[df["display_gpu_spec"].eq(spec)].sort_values("day")
         plot_x = [plot_x_by_day[day] for day in group["day"]]
-        plot_y = group["used_card_hour_sum_p9300"].to_numpy(dtype=float) / 100.0
+        plot_y = group["hp_used_gpu_hours"].to_numpy(dtype=float) / 100.0
         ax.plot(
             plot_x,
             plot_y,

cluster-trace-gpu-v2026/scripts/plotters/job_type_priority_distribution.py

@@ -17,8 +17,8 @@ def plot() -> None:
     rows = []
     for job_type in c.PAPER_JOB_ORDER:
         cur = df[df["job_type"].eq(job_type)]
-        hp = float(cur["used_card_hour_sum_p9300"].sum())
-        lp = float(cur["used_card_hour_sum_p9200_9300"].sum())
+        hp = float(cur["hp_used_gpu_hours"].sum())
+        lp = float(cur["lp_used_gpu_hours"].sum())
         total = hp + lp
         rows.append(
             {