reworked partitioned_iter.py example

README.md

@@ -174,6 +174,24 @@ The usage of `AtomicInt` provides correctness, regardless of the hashmap impleme
 But using `AtomicDict` instead of `dict` improves performance, even without using handles: writes to distinct keys do
 not generate contention.
 
+### Pre-sized dictionary, with partitioned iterations
+
+`AtomicDict` provides some more features compared to Python's `dict`, in
+the [partitioned iteration example](./examples/atomic_dict/partitioned_iter.py) two of them are shown:
+
+1. pre-sizing, which allows for the expensive dynamic resizing of a hash table to be avoided, and
+2. partitioned iterations, which allows to split number of elements among threads.
+
+```text
+Insertion into builtin dict took 36.81s
+Builtin dict iter took 17.56s with 1 thread.
+----------
+Insertion took 17.17s
+Partitioned iter took 8.80s with 1 threads.
+Partitioned iter took 5.03s with 2 threads.
+Partitioned iter took 3.92s with 3 threads.
+```
+
 ## AtomicRef
 
 You can use an `AtomicRef` when you have a shared variable that points to an object, and you need to change the

examples/atomic_dict/partitioned_iter.py

@@ -1,19 +1,33 @@
-import multiprocessing
 import threading
 import time
 
 import cereggii
 
 
 keys = 2**26
+py_d = {}
+
+started = time.time()
+for _ in range(keys):
+    py_d[_] = _
+print(f"Insertion into builtin dict took {time.time() - started:.2f}s")
+
+started = time.time()
+current = 0
+for k, v in py_d.items():
+    current = k + v
+print(f"Builtin dict iter took {time.time() - started:.2f}s with 1 thread.")
+print("----------")
+del py_d
+
 d = cereggii.AtomicDict(min_size=keys * 2)
 
 started = time.time()
 for _ in range(keys):
     d[_] = _
 print(f"Insertion took {time.time() - started:.2f}s")
 
-for n in range(1, multiprocessing.cpu_count() + 1):
+for n in range(1, 4):
 
     def iterator(i):
         current = 0
@@ -27,4 +41,4 @@ def iterator(i):
         t.start()
     for t in threads:
         t.join()
-    print(f"Fast iter took {time.time() - started:.2f}s with {n} threads.")
+    print(f"Partitioned iter took {time.time() - started:.2f}s with {n} threads.")