add primitive workload evaluation for non-PTA models (WiP)

1 files changed, 75 insertions, 0 deletions

diff --git a/bin/workload.py b/bin/workload.py
new file mode 100755
index 0000000..d4dff10
--- /dev/null
+++ b/bin/workload.py
@@ -0,0 +1,75 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+import sys
+import dfatool.cli
+import dfatool.utils
+from dfatool.model import AnalyticModel
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
+    )
+    parser.add_argument("--aggregate", choices=["sum"], default="sum")
+    parser.add_argument(
+        "--aggregate-init",
+        default=0,
+        type=float,
+    )
+    parser.add_argument(
+        "--info",
+        action="store_true",
+        help="Show benchmark information (number of measurements, parameter values, ...)",
+    )
+    parser.add_argument(
+        "--models",
+        nargs="+",
+        type=str,
+        help="Path to model file (.json or .json.xz)",
+    )
+    parser.add_argument("event", nargs="+", type=str)
+    args = parser.parse_args()
+
+    models = list()
+    for model_file in args.models:
+        with open(model_file, "r") as f:
+            models.append(AnalyticModel.from_json(json.load(f)))
+
+    if args.info:
+        for i in range(len(models)):
+            print(f"""{args.models[i]}: {" ".join(models[i].parameters)}""")
+            for name in models[i].names:
+                for attr in models[i].attributes(name):
+                    print(f"    {name}.{attr}")
+
+    aggregate = args.aggregate_init
+    for event in args.event:
+        nn, param = event.split("(")
+        name, action = nn.split(".")
+        param_model = None
+        ref_model = None
+        for model in models:
+            if name in model.names and action in model.attributes(name):
+                ref_model = model
+                param_model, param_info = model.get_fitted()
+                break
+        assert param_model is not None
+        param = param.removesuffix(")")
+        if param == "":
+            param = dict()
+        else:
+            param = dfatool.utils.parse_conf_str(param)
+        if args.aggregate == "sum":
+            aggregate += param_model(
+                name,
+                action,
+                param=dfatool.utils.param_dict_to_list(param, ref_model.parameters),
+            )
+
+    print(aggregate)
+
+
+if __name__ == "__main__":
+    main()