Address comments from code review.

Author: petervdonovan

README.md

@@ -117,15 +117,11 @@ The results for a multirun are written to a directory in the scheme `multirun/<d
 
 A second script called `collect_results.py` provides a convenient way for collecting results from a multirun and merging them into a single CSV file. Simply running
 ```
-./collect_results.py multirun/<date>/<time>/ out.csv
+./collect_results.py out.csv multirun/<date>/<time>/
 ```
 collects all results from the particular multirun and stores the merged data structure in out.csv. `collect_results.py` not only merges the results, but it also calculates minimum, maximum and median execution time for each individual run. The resulting CSV does not contain the measured values of individual iterations anymore and only contains a single row per run. This behavior can be disabled with the `--raw` command line flag. With the flag set, the results from all runs are merged as say are and the resulting file contains rows for all individual runs, but no minimum, maximum and median values.
 
-As a shortcut, you may alternatively use
-```
-./collect_results.py latest out.csv
-```
-to write the latest multirun results to `out.csv`.
+As a shortcut, you may omit the multirun directory to write the latest multirun results to `out.csv`.
 
 ## How it works
 

runner/collect_results.py

@@ -19,14 +19,14 @@ def dir_path(string):
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("src_path")
     parser.add_argument("out_file")
+    parser.add_argument("src_path", required=False, type=dir_path)
     parser.add_argument("--raw", dest="raw", action="store_true")
     args = parser.parse_args()
 
     # collect data from all runs
     src_path = (
-        args.src_path if args.src_path != "latest"
+        args.src_path if args.src_path is not None
         else latest_subdirectory(latest_subdirectory("./multirun"))
     )
     data_frames = []
@@ -52,8 +52,13 @@ def main():
     if args.out_file.endswith(".json"):
         with open(args.out_file, "w") as f:
             json.dump((create_json(concat)), f, indent=4)
-    else:
+    elif args.out_file.endswith(".csv"):
         concat.to_csv(args.out_file)
+    else:
+        raise ValueError(
+            f"Expected output file extension to be \".json\" "
+            f"or \".csv\", not \"{args.out_file.split('.')[-1]}\""
+        )
 
 def create_json(all_data: pd.DataFrame) -> str:
     group_by = ["benchmark", "target", "threads", "scheduler"]

runner/conf/benchmark/savina_concurrency_banking.yaml

@@ -2,7 +2,7 @@
 name: "Bank Transaction"
 params:
   accounts: 1000
-  transactions: "${size.banking_transactions}"
+  transactions: "${problem_size.banking_transactions}"
 
 # target specific configuration
 targets:

runner/conf/benchmark/savina_concurrency_bndbuffer.yaml

@@ -4,7 +4,7 @@ params:
   buffer_size: 50
   consumers: 40
   producers: 40
-  items_per_producer: "${size.bndbuffer_items_per_producer}"
+  items_per_producer: "${problem_size.bndbuffer_items_per_producer}"
   produce_cost: 25
   consume_cost: 25
 

runner/conf/benchmark/savina_concurrency_concsll.yaml

@@ -2,7 +2,7 @@
 name: "Concurrent Sorted Linked List"
 params:
   workers: 20
-  messages_per_worker: "${size.concsll_messages_per_worker}"
+  messages_per_worker: "${problem_size.concsll_messages_per_worker}"
   write_percentage: 10
   size_percentage: 1
 

runner/conf/benchmark/savina_micro_big.yaml

@@ -1,7 +1,7 @@
 # @package benchmark
 name: "Big"
 params:
-  messages: ${size.big_messages}
+  messages: ${problem_size.big_messages}
   actors: 120
 
 # target specific configuration

runner/conf/benchmark/savina_micro_pingpong.yaml

@@ -1,7 +1,7 @@
 # @package benchmark
 name: "Ping Pong"
 params:
-  pings: "${size.pingpong_pings}"
+  pings: "${problem_size.pingpong_pings}"
 
 # target specific configuration
 targets:

runner/conf/benchmark/savina_parallelism_apsp.yaml

@@ -1,8 +1,8 @@
 # @package benchmark
 name: "All-Pairs Shortest Path"
 params:
-  num_workers: ${size.apsp_num_workers}
-  block_size: ${size.apsp_block_size}
+  num_workers: ${problem_size.apsp_num_workers}
+  block_size: ${problem_size.apsp_block_size}
   max_edge_weight: 100
 
 # target specific configuration

runner/conf/benchmark/savina_parallelism_filterbank.yaml

@@ -1,7 +1,7 @@
 # @package benchmark
 name: "Filter Bank"
 params:
-  columns: ${size.filterbank_columns}
+  columns: ${problem_size.filterbank_columns}
   simulations: 34816
   channels: 8
 

runner/conf/benchmark/savina_parallelism_piprecise.yaml

@@ -2,7 +2,7 @@
 name: "Precise Pi Computation"
 params:
   workers: 20
-  precision: ${size.piprecise_precision}
+  precision: ${problem_size.piprecise_precision}
 
 # target specific configuration
 targets: