added chapter06 (everything thus far works on Python 3, btw...)

Chapter06/broken_matrix_ker.py

@@ -0,0 +1,79 @@
+# Note: this code is intentionally broken!!!
+# (This is intended to show a case study of how to debug CUDA code
+# using printf.)
+
+import pycuda.autoinit
+import pycuda.driver as drv
+from pycuda import gpuarray
+from pycuda.compiler import SourceModule
+import numpy as np
+
+
+ker = SourceModule('''
+// row-column dot-product for matrix multiplication
+__device__ float rowcol_dot(float *matrix_a, float *matrix_b, int row, int col, int N)
+{
+
+    //printf("threadIdx.x,y: %d,%d blockIdx.x,y: %d,%d -- row is %d, col is %d, N is %d.\\n", threadIdx.x, threadIdx.y, blockIdx.x, blockIdx.y, row, col, N);
+	float val = 0;
+	
+	for (int k=0; k < N; k++)
+	{
+    
+        // broken version
+        val += matrix_a[ row + k*N ] * matrix_b[ col*N + k];
+
+        //if(threadIdx.x == 0 && threadIdx.y == 0 && blockIdx.x == 0 && blockIdx.y == 0)
+        //    printf("Dot-product loop: k value is %d, matrix_a value is %f, matrix_b is %f.\\n", k, matrix_a[ row + k*N ], matrix_b[ col*N + k]);
+		
+        // fixed version
+        //val += matrix_a[ row*N + k ] * matrix_b[ col + k*N];
+	}
+	
+	return(val);
+
+}
+
+// matrix multiplication kernel that is parallelized over row/column tuples.
+__global__ void matrix_mult_ker(float * matrix_a, float * matrix_b, float * output_matrix, int N)
+{
+
+   // broken version
+   int row = blockIdx.x + threadIdx.x;
+	int col = blockIdx.y + threadIdx.y;
+    
+    // fixed version
+    //int row = blockIdx.x*blockDim.x + threadIdx.x;
+    //int col = blockIdx.y*blockDim.y + threadIdx.y;
+    
+    //printf("threadIdx.x,y: %d,%d blockIdx.x,y: %d,%d -- row is %d, col is %d.\\n", threadIdx.x, threadIdx.y, blockIdx.x, blockIdx.y, row, col);
+
+	
+    // broken version
+    output_matrix[col + row*N] = rowcol_dot(matrix_a, matrix_b, col, row, N);
+    
+    // fixed version
+	//output_matrix[col + row*N] = rowcol_dot(matrix_a, matrix_b, row, col, N);
+
+
+}
+''')
+
+matrix_ker = ker.get_function('matrix_mult_ker')
+
+test_a = np.float32( [range(1,5)] * 4 )
+test_b = np.float32([range(14,10, -1)]*4 )
+
+output_mat = np.matmul(test_a, test_b)
+
+test_a_gpu = gpuarray.to_gpu(test_a)
+test_b_gpu = gpuarray.to_gpu(test_b)
+output_mat_gpu = gpuarray.empty_like(test_a_gpu)
+
+matrix_ker(test_a_gpu, test_b_gpu, output_mat_gpu, np.int32(4), block=(2,2,1), grid=(2,2,1))
+
+assert( np.allclose(output_mat_gpu.get(), output_mat) )
+
+
+
+