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+#!/usr/bin/env python3
+# Make a "naive" implementation of a matrix multiplication.
+# - Start from the template from last lecture - all global memory, each work item calculates one value of the result independent of the others, and so on.
+# - Feel free to make assumptions on the size, for instance that the matrices are square, small enough to fit in GPU memory and so on.
+# - Make sure to test your kernel thoroughly enough that you trust it is correct.
+
+# Im asuming that the buffers fit in memory
+import numpy as np
+import pyopencl as cl
+import time
+
+# Source of the kernel itself.
+kernel_source = """
+__kernel void matrixmult(
+             const uint  shared_dim,
+    __global const float *a_device, 
+    __global const float *b_device, 
+    __global       float *result_device)
+{
+    // get the i'th row of matrix a
+    int index_a = get_global_id(0) * shared_dim;
+
+    // get the start of the i'th column of b. 
+    // Remember we should index this by jumping b's row size (bcols, or get_global_size(1)).
+    int index_b = get_global_id(1);
+    int b_jump = get_global_size(1);
+
+    // Do the vector dot
+    float result = 0;
+    for (int i = 0; i < shared_dim; i++) {
+        result += a_device[index_a + i] * b_device[index_b];
+
+        // Remember we need to move b by it's column size to 
+        // skip to the next row
+        index_b += b_jump;
+    }
+
+    // Save the result
+    result_device[get_global_id(0) * get_global_size(1) + get_global_id(1)] = result;
+}
+"""
+
+# matrix a rows
+arows = 500
+bcols = 1000
+# A columns and b rows
+shared = 1000
+
+# Create the context (containing platform and device information) and command queue.
+context = cl.create_some_context()
+cmd_queue = cl.CommandQueue(context)
+
+# Create the host side data and a empty array to hold the result.
+a_host = np.random.random((arows, shared)).astype(np.float32)
+b_host = np.random.random((shared, bcols)).astype(np.float32)
+result_host = np.empty((arows, bcols)).astype(np.float32)
+
+# If you want to keep the kernel in a seperate file uncomment this line and adjust the filename
+#kernel_source = open("kernel.cl").read()
+
+# Create a new program from the kernel and build the source.
+prog = cl.Program(context, kernel_source).build()
+
+start = time.time()
+# Create a device side read-only memory buffer and copy the data from "hostbuf" into it.
+# Create as many 
+# You can find the other possible mem_flags values at
+# https://www.khronos.org/registry/OpenCL/sdk/1.2/docs/man/xhtml/clCreateBuffer.html
+mf = cl.mem_flags
+a_device = cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=a_host)
+b_device = cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=b_host)
+result_device = cl.Buffer(context, mf.WRITE_ONLY, result_host.nbytes)
+
+# Execute the "sum" kernel in the program. Parameters are:
+# 
+#               Command queue         Work group size   Kernel param 1
+#                   ↓   Global grid size   ↓   Kernel param 0  ↓  Kernel param 2
+#                   ↓           ↓          ↓       ↓           ↓        ↓
+prog.matrixmult(cmd_queue, result_host.shape, None, np.uint32(shared), a_device, b_device, result_device)
+
+# Copy the result back from device to host.
+cl.enqueue_copy(cmd_queue, result_host, result_device)
+
+end = time.time()
+
+print(f"GPU time {end - start} sec")
+
+
+start = time.time()
+hostcalc = a_host @ b_host
+end = time.time()
+print(f"CPU time {end - start} sec")
+
+# Check the results in the host array with Numpy.
+print("All elements close?", np.allclose(result_host, hostcalc))