Weak Scaling documentation

Author: henryleberre

examples/3D_weak_scaling/README.md

@@ -0,0 +1,24 @@
+# 3D Weak Scaling
+
+The [**3D_weak_scaling**](case.py) case depends on two parameters:
+
+- **The number of MPI ranks** (_procs_): As _procs_ increases, the problem
+size per rank remains constant. _procs_ is determined using information provided
+to the case file by `mfc.sh run`.
+
+- **GPU memory usage per rank** (_gbpp_): As _gbpp_ increases, the problem
+size per rank increases and the number of timesteps decreases so that wall times
+consistent. _gbpp_ is a user-defined optional argument to the [case.py](case.py) 
+file. It can be specified right after the case filepath when invoking `mfc.sh run`.
+
+Weak scaling benchmarks can be produced by keeping _gbpp_ constant and varying _procs_.
+
+For example, to run a weak scaling test that uses ~4GB of GPU memory per rank
+on 8 2-rank nodes with case optimization, one could:
+
+```console
+./mfc.sh run examples/3D_weak_scaling/case.py 4 -t pre_process simulation          \
+             -e batch -p mypartition -N 8 -n 2 -w "01:00:00" -# "MFC Weak Scaling" \
+             --case-optimization -j 32
+```
+

examples/3D_weak_scaling/case.py

@@ -11,7 +11,7 @@
     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 
 parser.add_argument("dict", type=str, metavar="DICT", help=argparse.SUPPRESS)
-parser.add_argument("gbpp", type=int, metavar="MEM", default=16, help="Adjusts the problem size per rank to fit into [MEM] GB of GPU memory")
+parser.add_argument("gbpp", type=int, metavar="MEM", default=16, help="Adjusts the problem size per rank to fit into [MEM] GB of GPU memory per GPU.")
 
 ARGS = vars(parser.parse_args())
 DICT = json.loads(ARGS["dict"])