diff --git a/code_to_optimize/code_directories/simple_tracer_e2e/workload.py b/code_to_optimize/code_directories/simple_tracer_e2e/workload.py
index db708a5c0..b929208f6 100644
--- a/code_to_optimize/code_directories/simple_tracer_e2e/workload.py
+++ b/code_to_optimize/code_directories/simple_tracer_e2e/workload.py
@@ -1,16 +1,15 @@
 from concurrent.futures import ThreadPoolExecutor
+from time import sleep
 
 
 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))
-
-    # Use a generator expression directly in join for more efficiency
-    return " ".join(str(i) for i in range(number))
+    number = min(1000, number)
+    # Use arithmetic sum formula instead of for loop for k
+    k = (number * 100) * (number * 100 - 1) // 2
+    # Use arithmetic sum formula for j as well for more efficiency
+    j = number * (number - 1) // 2
+    # Use list comprehension for join for better performance with large 'number'
+    return " ".join([str(i) for i in range(number)])
 
 
 def test_threadpool() -> None:
@@ -21,6 +20,7 @@ def test_threadpool() -> None:
     for r in result:
         print(r)
 
+
 class AlexNet:
     def __init__(self, num_classes=1000):
         self.num_classes = num_classes
@@ -28,7 +28,7 @@ def __init__(self, num_classes=1000):
 
     def forward(self, x):
         features = self._extract_features(x)
-        
+
         output = self._classify(features)
         return output
 
@@ -43,15 +43,24 @@ def _classify(self, features):
         total = sum(features)
         return [total % self.num_classes for _ in features]
 
+
 class SimpleModel:
     @staticmethod
     def predict(data):
-        return [x * 2 for x in data]
-    
+        result = []
+        sleep(10)
+        for i in range(500):
+            for x in data:
+                computation = 0
+                computation += x * i**2
+                result.append(computation)
+        return result
+
     @classmethod
     def create_default(cls):
         return cls()
 
+
 def test_models():
     model = AlexNet(num_classes=10)
     input_data = [1, 2, 3, 4, 5]
@@ -60,6 +69,7 @@ def test_models():
     model2 = SimpleModel.create_default()
     prediction = model2.predict(input_data)
 
+
 if __name__ == "__main__":
     test_threadpool()
     test_models()