results = [[0 for _ in range(cols_B)] for _ in range(rows_A)]
for i in range(len(A)):
for j in range(len(B[0])):
for k in range(len(B)):
results[i][j] += A[i][k] * B[k][j]Relevance Analysis
The following results were obtained through local experiments.
Configuration
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Processor: Intel® Core™ Ultra 5 135U, 2100 MHz, 12 cores, 14 logical processors
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RAM: 16 GB
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CO2 Emissions Measurement: Using CodeCarbon
Context
This study is divided into 3 parts, comparing a vectorized and an iterative method: measuring the impact on a dot product between two vectors, measuring the impact on an outer product between two vectors, measuring the impact on a matrix calculation.
Impact Analysis
1. dot product:
Non compliant
def iterative_dot_product(x,y):
total = 0
for i in range(len(x)):
total += x[i] * y[i]
return totalCompliant
def vectorized_dot_product(x,y):
return np.dot(x,y)
2. Outer product:
Non compliant
def iterative_outer_product(x, y):
o = np.zeros((len(x), len(y)))
for i in range(len(x)):
for j in range(len(y)):
o[i][j] = x[i] * y[j]
return oCompliant
def vectorized_outer_product(x, y):
return np.outer(x, y)
3. Matrix product:
Non compliant
def iterative_matrix_product(A, B):
for i in range(len(A)):
for j in range(len(B[0])):
for k in range(len(B)):
results[i][j] += A[i][k] * B[k][j]
return resultsCompliant
def vectorized_outer_product(A, B):
return np.dot(A, B)
Conclusion
The results show that the vectorized method is significantly faster than the iterative method. The CO2 emissions are also lower. This is a clear example of how using built-in functions can lead to more efficient code, both in terms of performance and environmental impact.