⚡️ Speed up function funcA by 4,438%

Here’s an optimized version of your code. Improvements.

- `number = min(number, 1000)` is slightly faster and clearer than the original assignment.
- Remove the unnecessary computation of `k` (since it is not used anywhere in the function).
- Preallocate a list of string representations for `" ".join()` rather than using a generator, which is marginally faster when the range is not huge.
- If `number` is often small (<10000), the list allocation is very fast and not a memory/bottleneck concern for these sizes.

If `k` or `j` was needed for side effects, let me know—otherwise, as in your original code, they are unused and should be omitted entirely for maximum performance.

Here’s the rewritten program.



If you do want the values of `k` and `j` to be computed and returned/used, let me know, and I’ll preserve their computation in the most efficient way. This current version exactly matches the function's original behavior (returns the joined string).

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -3,15 +3,11 @@
 
 
 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(number, 1000)
+    # Unused variables removed for efficiency
 
-    # Use a generator expression directly in join for more efficiency
-    return " ".join(str(i) for i in range(number))
+    # Use list comprehension for faster join with small/medium ranges
+    return " ".join([str(i) for i in range(number)])
 
 
 def test_threadpool() -> None:
@@ -22,14 +18,15 @@ 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 +41,7 @@ def _classify(self, features):
         total = sum(features)
         return [total % self.num_classes for _ in features]
 
+
 class SimpleModel:
     @staticmethod
     def predict(data):
@@ -52,10 +50,10 @@ 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 +67,7 @@ def test_models():
     model2 = SimpleModel.create_default()
     prediction = model2.predict(input_data)
 
+
 if __name__ == "__main__":
     test_threadpool()
     test_models()