feat: Added Pearson correlation coefficient for GPU

1 files changed, 44 insertions, 0 deletions

diff --git a/pyecsca/sca/stacked_traces/correlate.py b/pyecsca/sca/stacked_traces/correlate.py
new file mode 100644
index 0000000..10705ca
--- /dev/null
+++ b/pyecsca/sca/stacked_traces/correlate.py
@@ -0,0 +1,44 @@
+import numpy as np
+import numpy.typing as npt
+from numba import cuda
+from math import sqrt
+
+
+@cuda.jit(device=True, cache=True)
+def gpu_pearson_corr(samples: npt.NDArray[np.number],
+                     intermediate_values: npt.NDArray[np.number],
+                     result: cuda.devicearray.DeviceNDArray):
+    """
+    Calculates the Pearson correlation coefficient between the given samples and intermediate values using GPU acceleration.
+
+    :param samples: A 2D array of shape (n, m) containing the samples.
+    :type samples: npt.NDArray[np.number]
+    :param intermediate_values: A 1D array of shape (n,) containing the intermediate values.
+    :type intermediate_values: npt.NDArray[np.number]
+    :param result: A 1D array of shape (m,) to store the resulting correlation coefficients.
+    :type result: cuda.devicearray.DeviceNDArray
+    """
+    col: int = cuda.grid(1)  # type: ignore
+
+    if col >= samples.shape[1]:  # type: ignore
+        return
+
+    n = samples.shape[0]
+    samples_sum = 0.
+    samples_sq_sum = 0.
+    intermed_sum = 0.
+    intermed_sq_sum = 0.
+    product_sum = 0.
+
+    for row in range(n):
+        samples_sum += samples[row, col]
+        samples_sq_sum += samples[row, col] ** 2
+        intermed_sum += intermediate_values[row]
+        intermed_sq_sum += intermediate_values[row] ** 2
+        product_sum += samples[row, col] * intermediate_values[row]
+
+    numerator = n * product_sum - samples_sum * intermed_sum
+    denominator = (sqrt(n * samples_sq_sum - samples_sum * samples_sum)
+                   * sqrt(n * intermed_sq_sum - intermed_sum * intermed_sum))
+
+    result[col] = numerator / denominator