initial commit

README.md

@@ -0,0 +1 @@
+Utilizing CUDA + Numba to calculate entropy.

entro.py

@@ -0,0 +1,68 @@
+import numpy as np
+from numba import cuda
+import math
+
+@cuda.jit
+def count_values(arr, counts):
+    idx = cuda.grid(1)
+    if idx < 256:
+        counts[idx] = 0
+
+    cuda.syncthreads()
+
+    for i in range(arr.shape[0]):
+        if arr[i] == idx:
+            cuda.atomic.add(counts, idx, 1)
+
+def count_values_with_cuda(arr):
+    counts = np.zeros(256, dtype=np.int32)
+    threadsperblock = 256
+    blockspergrid = (threadsperblock + len(counts) - 1) // threadsperblock
+    count_values[blockspergrid, threadsperblock](arr, counts)
+    return counts
+
+
+
+@cuda.jit
+def calculate_histogram(data, hist_out):
+    # Calculate histogram using CUDA
+    x = cuda.grid(1)
+    if x < data.size:
+        cuda.atomic.add(hist_out, data[x], 1)
+
+@cuda.jit
+def calculate_entropy(hist, total_pixels, entropy_out):
+    # Calculate entropy using CUDA
+    x = cuda.grid(1)
+    if x < hist.size:
+        prob = hist[x] / total_pixels
+        if prob != 0:
+            entropy_out[x] = -prob * math.log2(prob)
+
+def entropy_with_cuda(data):
+    # Convert input data to numpy array
+    data_np = np.array(data)
+
+    counts = count_values_with_cuda(data)
+    # Determine unique values and their counts
+    #unique_values, counts = np.unique(data_np, return_counts=True)
+
+    # Total number of pixels
+    total_pixels = data_np.size
+
+    # Compute histogram on GPU
+    hist_out = np.zeros_like(range(0,255))
+    threadsperblock = 256
+    blockspergrid = (data_np.size + (threadsperblock - 1)) // threadsperblock
+    calculate_histogram[blockspergrid, threadsperblock](data_np, hist_out)
+
+    # Compute entropy on GPU
+    entropy_out = np.zeros_like(hist_out, dtype=np.float32)
+    threadsperblock = 256
+    blockspergrid = (hist_out.size + (threadsperblock - 1)) // threadsperblock
+    calculate_entropy[blockspergrid, threadsperblock](hist_out, total_pixels, entropy_out)
+
+    # Sum the entropy values to get the total entropy
+    entropy = np.sum(entropy_out)
+
+    return entropy