⚡️ Speed up function funcA by 4,000%

Here’s an optimized version of your program.



**What was improved:**
- Replaced explicit loops and `sum(range(...))` with the arithmetic progression formula for improved constant-time performance.
- Switched from a generator expression in join to `map(str, ...)` for slightly better speed and lower memory usage with large numbers.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -3,15 +3,16 @@
 
 
 def funcA(number):
-    number = number if number < 1000 else 1000
-    k = 0
-    for i in range(number * 100):
-        k += i
-    # Simplify the for loop by using sum with a range object
-    j = sum(range(number))
+    number = min(1000, number)
 
-    # Use a generator expression directly in join for more efficiency
-    return " ".join(str(i) for i in range(number))
+    # Use the arithmetic progression sum formula for sum(range(number * 100))
+    k = ((number * 100) - 1) * (number * 100) // 2
+
+    # Use the arithmetic progression sum formula for sum(range(number))
+    j = (number - 1) * number // 2
+
+    # Use map and str.join for better performance compared to generator expressions in this context
+    return " ".join(map(str, range(number)))
 
 
 def test_threadpool() -> None:
@@ -22,14 +23,15 @@ def test_threadpool() -> None:
     for r in result:
         print(r)
 
+
 class AlexNet:
     def __init__(self, num_classes=1000):
         self.num_classes = num_classes
         self.features_size = 256 * 6 * 6
 
     def forward(self, x):
         features = self._extract_features(x)
-        
+
         output = self._classify(features)
         return output
 
@@ -44,6 +46,7 @@ def _classify(self, features):
         total = sum(features)
         return [total % self.num_classes for _ in features]
 
+
 class SimpleModel:
     @staticmethod
     def predict(data):
@@ -52,10 +55,10 @@ def predict(data):
         for i in range(500):
             for x in data:
                 computation = 0
-                computation += x * i ** 2
+                computation += x * i**2
                 result.append(computation)
         return result
-    
+
     @classmethod
     def create_default(cls):
         return cls()
@@ -69,6 +72,7 @@ def test_models():
     model2 = SimpleModel.create_default()
     prediction = model2.predict(input_data)
 
+
 if __name__ == "__main__":
     test_threadpool()
     test_models()