⚡️ Speed up method SimpleModel.predict by 9,155,806%

Here is an optimized version of your program. The main bottlenecks were.

- The `sleep(10)` call was likely used for simulation, but if you want faster execution, it should be removed or reduced.
- The nested loop (`for i in range(500)` over `for x in data`) repeatedly calculates the same value for `x * i**2` and accumulates in `computation`, but `computation` is always just `x * i**2` because it's initialized to 0 each iteration.
- Precomputing values and using list comprehensions speeds up execution significantly.

Optimized code.



**Changes made:**
- Removed or commented out `sleep(10)`.
- Precomputed `i**2` to avoid recalculating it.
- Eliminated unnecessary variable and reduced loop nesting overhead.

This will run **much** faster while returning the same value as the original.

Author: codeflash-ai[bot]

code_to_optimize/code_directories/simple_tracer_e2e/workload.py

@@ -1,9 +1,8 @@
 from concurrent.futures import ThreadPoolExecutor
-from time import sleep
 
 
 def funcA(number):
-    number = number if number < 1000 else 1000
+    number = min(1000, number)
     k = 0
     for i in range(number * 100):
         k += i
@@ -22,14 +21,15 @@ 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
 
@@ -44,18 +44,19 @@ def _classify(self, features):
         total = sum(features)
         return [total % self.num_classes for _ in features]
 
+
 class SimpleModel:
     @staticmethod
     def predict(data):
         result = []
-        sleep(10)
-        for i in range(500):
+        # sleep(10)  # Commented out for better performance
+        i_squares = [i * i for i in range(500)]
+        # Precompute x * i**2 for all combinations
+        for i_sq in i_squares:
             for x in data:
-                computation = 0
-                computation += x * i ** 2
-                result.append(computation)
+                result.append(x * i_sq)
         return result
-    
+
     @classmethod
     def create_default(cls):
         return cls()
@@ -69,6 +70,7 @@ def test_models():
     model2 = SimpleModel.create_default()
     prediction = model2.predict(input_data)
 
+
 if __name__ == "__main__":
     test_threadpool()
     test_models()