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 eddf37e0d..2ca8f8728 100644 --- a/code_to_optimize/code_directories/simple_tracer_e2e/workload.py +++ b/code_to_optimize/code_directories/simple_tracer_e2e/workload.py @@ -3,15 +3,12 @@ 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)) + # k and j are unused in return value, so no need to compute them + + # Use map instead of generator expression for slightly faster performance in join + return " ".join(map(str, range(number))) def test_threadpool() -> None: @@ -22,6 +19,7 @@ def test_threadpool() -> None: for r in result: print(r) + class AlexNet: def __init__(self, num_classes=1000): self.num_classes = num_classes @@ -29,7 +27,7 @@ def __init__(self, num_classes=1000): def forward(self, x): features = self._extract_features(x) - + output = self._classify(features) return output @@ -44,6 +42,7 @@ def _classify(self, features): total = sum(features) return [total % self.num_classes for _ in features] + class SimpleModel: @staticmethod def predict(data): @@ -52,10 +51,10 @@ 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 +68,7 @@ def test_models(): model2 = SimpleModel.create_default() prediction = model2.predict(input_data) + if __name__ == "__main__": test_threadpool() test_models()