README.md

20240914 - MacBook Pro (M1) - Benchmark

Machine Specifications

• Model Name: MacBook Pro
• Model Identifier: MacBookPro17,1
• Model Number: Z11D000KXD/A
• Chip: Apple M1
• Total Number of Cores: 8 (4 performance and 4 efficiency)
• Memory: 16 GB
• System Firmware Version: 10151.140.19
• OS Loader Version: 10151.140.19
• Activation Lock Status: Enabled

Results

ArrayStats.json

ArrayStatsNoSuperWord.json

ComplexExpression.json

ComplexExpressionNoSuperWord.json

DotProduct.json

DotProductNoSuperWord.json

ElementWiseMultiplication.json

ElementWiseMultiplicationNoSuperWord.json

MatrixMultiplication.json

MatrixMultiplicationNoSuperWord.json

SimpleSum.json

SimpleSumNoSuperWord.json

Sorting.json

SortingNoSuperWord.json

VectorAddition.json

VectorAdditionNoSuperWord.json

Analysis

The benchmarks were conducted on a MacBook Pro equipped with an Apple M1 chip. The results indicate that vectorized implementations generally provide significant performance improvements over traditional loop-based implementations across a variety of mathematical and array operations. The SuperWord optimization further enhances the performance of vectorized implementations in most scenarios.

Key Observations

1. ArrayStats and ArrayStatsNoSuperWord:

   • Vectorized Implementations: Show a marked improvement in performance.
   • SuperWord Optimization: Provides additional gains, making the operations faster.

2. ComplexExpression and ComplexExpressionNoSuperWord:

   • Vectorized Implementations: Did not exhibit the same level of performance improvement.
   • Reason: Likely due to the complexity and nature of the operations involved.

3. DotProduct and DotProductNoSuperWord:

   • Vectorized Implementations: Significant performance gains were observed.
   • SuperWord Optimization: Further enhances the performance, making the dot product calculations more efficient.

4. ElementWiseMultiplication and ElementWiseMultiplicationNoSuperWord:

   • Vectorized Implementations: Outperformed traditional loops.
   • SuperWord Optimization: Provides additional benefits, improving the speed of element-wise multiplications.

5. MatrixMultiplication and MatrixMultiplicationNoSuperWord:

   • Vectorized Implementations: Showed substantial performance improvements.
   • SuperWord Optimization: Further enhances the performance, making matrix multiplications faster.

6. SimpleSum and SimpleSumNoSuperWord:

   • Vectorized Implementations: Did not show significant performance gains.
   • Reason: Similar to complex expressions, the simple nature of the task might not benefit as much from vectorization.

7. Sorting and SortingNoSuperWord:

   • Vectorized Implementations: Demonstrated improved performance over traditional sorting methods.
   • SuperWord Optimization: Contributes to further gains, making sorting operations more efficient.

8. VectorAddition and VectorAdditionNoSuperWord:

   • Vectorized Implementations: Significantly outperformed traditional loops.
   • SuperWord Optimization: Provides additional performance improvements, making vector additions faster.

Conclusion

Overall, the results highlight the effectiveness of vectorization and the SuperWord optimization in enhancing performance for a wide range of operations. However, some exceptions exist in more complex or simple summation tasks where the benefits of vectorization are less pronounced.