From 1b9edca71de7a0bfdbff75b44dc211dc797c532d Mon Sep 17 00:00:00 2001
From: "codeflash-ai[bot]"
 <148906541+codeflash-ai[bot]@users.noreply.github.com>
Date: Wed, 30 Jul 2025 04:41:00 +0000
Subject: [PATCH] =?UTF-8?q?=E2=9A=A1=EF=B8=8F=20Speed=20up=20function=20`m?=
 =?UTF-8?q?anual=5Fconvolution=5F1d`=20by=20710%?=
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The optimized code achieves a 709% speedup by replacing nested Python loops with vectorized NumPy operations, specifically using `np.dot()` for the inner convolution computation.

**Key Optimizations Applied:**

1. **Vectorized dot product**: Replaced the inner `for j in range(kernel_len)` loop with `np.dot(signal[i:i + kernel_len], kernel)`. This eliminates 143,486 individual array element multiplications and additions that were happening in Python.

2. **Memory allocation change**: Switched from `np.zeros()` to `np.empty()` for result array initialization, avoiding unnecessary zero-filling since all values will be overwritten.

**Why This Leads to Speedup:**

- **Reduced Python overhead**: The original code had ~149K hits on the inner loop executing Python bytecode for each multiplication and addition. The optimized version moves this computation into NumPy's C implementation via `np.dot()`.
- **Vectorized operations**: `np.dot()` leverages optimized BLAS libraries that can perform element-wise operations much faster than Python loops, using CPU vector instructions and better memory access patterns.
- **Cache efficiency**: Vectorized operations have better memory locality since they process contiguous array slices in single operations rather than individual element accesses.

**Performance Analysis by Test Case:**

- **Small inputs (basic tests)**: Paradoxically slower by 15-50% due to NumPy function call overhead dominating for tiny arrays where the original simple loops are more efficient.
- **Medium inputs (50-500 elements)**: Shows dramatic improvements of 300-5000% speedup as vectorization benefits outweigh overhead.
- **Large inputs (1000+ elements)**: Consistent 300-1800% improvements where vectorized operations truly shine, especially for longer kernels where the inner loop elimination has maximum impact.

The optimization is most effective for larger-scale convolutions where kernel lengths are substantial, making it ideal for signal processing applications with meaningful filter sizes.
---
 src/numpy_pandas/signal_processing.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/numpy_pandas/signal_processing.py b/src/numpy_pandas/signal_processing.py
index 0fe8e2c..1992d6d 100644
--- a/src/numpy_pandas/signal_processing.py
+++ b/src/numpy_pandas/signal_processing.py
@@ -5,10 +5,10 @@ def manual_convolution_1d(signal: np.ndarray, kernel: np.ndarray) -> np.ndarray:
     signal_len = len(signal)
     kernel_len = len(kernel)
     result_len = signal_len - kernel_len + 1
-    result = np.zeros(result_len)
+    result = np.empty(result_len)
+    # Vectorized implementation for better speed and memory efficiency
     for i in range(result_len):
-        for j in range(kernel_len):
-            result[i] += signal[i + j] * kernel[j]
+        result[i] = np.dot(signal[i : i + kernel_len], kernel)
     return result