Optimization Engine

Threading uses AI-powered analysis to detect inefficient patterns and apply optimizations automatically.

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How it works

1. Static analysis — Parse code, track data flow, match patterns
2. Dynamic profiling — Measure time, memory, CPU utilization
3. AI optimization — Detect bottlenecks, suggest and implement fixes

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Strategies

Auto-vectorization

Before:

def normalize(data):
    result = np.zeros_like(data)
    for i in range(data.shape[0]):
        row_sum = sum(data[i, :])
        result[i, :] = data[i, :] / row_sum
    return result

After:

def normalize(data):
    return data / data.sum(axis=1, keepdims=True)

Speedup: 150x

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GPU offloading

Operation	CPU	GPU	Speedup
Matrix multiply (10k×10k)	12.4s	0.08s	155x
SVD (5k×5k)	8.7s	0.12s	72x
Sort (100M elements)	5.1s	0.15s	34x

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Kernel fusion

Combines operations to reduce memory bandwidth:

# Before: 3 memory passes
a = data * scale
b = a + offset
c = np.maximum(b, 0)

# After: 1 memory pass
c = np.maximum(data * scale + offset, 0)

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Detection patterns

O(n²) algorithms:

[analyze] Detected O(n²) in pairwise_distance()
  • Suggested: scipy.spatial.distance.cdist
  • Speedup: ~1000x

Redundant computation:

[analyze] np.linalg.inv(A) called 1,000× with same A
  • Suggested: Compute once, reuse

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Hints

# threading: optimize
def critical_function(data):
    ...

# threading: no-gpu
def cpu_only_function(data):
    ...

# threading: skip
def legacy_function(data):
    ...

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Supported libraries

NumPy, Pandas, scikit-learn, SciPy, PyTorch, TensorFlow