Update experiments.md

doc.zih.tu-dresden.de/docs/software/misc/dask_test.py

+#use of dask-jobqueue for the estimation of Pi by Monte-Carlo method
+
+import time
+from time import time, sleep
+
+from dask.distributed import Client
+from dask_jobqueue import SLURMCluster
+import subprocess as sp
+
+import dask.array as da
+import numpy as np
+
+#setting up the dashboard
+
+uid = int( sp.check_output('id -u', shell=True).decode('utf-8').replace('\n','') )
+portdash = 10001 + uid
+
+#create a slurm cluster, please specify your project 
+
+cluster = SLURMCluster(queue='alpha', cores=2, project='p_scads', memory="8GB", walltime="00:30:00", extra=['--resources gpu=1'], scheduler_options={"dashboard_address": f":{portdash}"})
+
+#submit the job to the scheduler with the number of nodes (here 2) requested:
+
+cluster.scale(2)
+
+#wait for SLURM to allocate a resources
+
+sleep(120)
+
+#check resources
+
+client = Client(cluster)
+client
+
+#real calculations with a Monte Carlo method
+
+def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):
+    """Calculate PI using a Monte Carlo estimate."""
+    
+    size = int(size_in_bytes / 8)
+    chunksize = int(chunksize_in_bytes / 8)
+    
+    xy = da.random.uniform(0, 1,
+                           size=(size / 2, 2),
+                           chunks=(chunksize / 2, 2))
+    
+    in_circle = ((xy ** 2).sum(axis=-1) < 1)
+    pi = 4 * in_circle.mean()
+
+    return pi
+
+def print_pi_stats(size, pi, time_delta, num_workers):
+    """Print pi, calculate offset from true value, and print some stats."""
+    print(f"{size / 1e9} GB\n"
+          f"\tMC pi: {pi : 13.11f}"
+          f"\tErr: {abs(pi - np.pi) : 10.3e}\n"
+          f"\tWorkers: {num_workers}"
+          f"\t\tTime: {time_delta : 7.3f}s")
+          
+#let's loop over different volumes of double-precision random numbers and estimate it
+
+for size in (1e9 * n for n in (1, 10, 100)):
+    
+    start = time()
+    pi = calc_pi_mc(size).compute()
+    elaps = time() - start
+
+    print_pi_stats(size, pi, time_delta=elaps,
+                   num_workers=len(cluster.scheduler.workers))
+
+#Scaling the Cluster to twice its size and re-run the experiments
+                   
+new_num_workers = 2 * len(cluster.scheduler.workers)
+
+print(f"Scaling from {len(cluster.scheduler.workers)} to {new_num_workers} workers.")
+
+cluster.scale(new_num_workers)
+
+sleep(120)
+
+client
+                   
+#Re-run same experiments with doubled cluster
+
+for size in (1e9 * n for n in (1, 10, 100)):    
+        
+    start = time()
+    pi = calc_pi_mc(size).compute()
+    elaps = time() - start
+
+    print_pi_stats(size, pi,
+                   time_delta=elaps,
+                   num_workers=len(cluster.scheduler.workers))