Added hpp assignments

4 files changed, 156 insertions, 0 deletions

diff --git a/sem4/hpp/m9/opgave3.py b/sem4/hpp/m9/opgave3.py
new file mode 100644
index 0000000..15505eb
--- /dev/null
+++ b/sem4/hpp/m9/opgave3.py
@@ -0,0 +1,31 @@
+import numpy as np
+
+def matrixmult(a, b):
+    res = np.empty((a.shape[0], b.shape[1]))
+    for ic, c in enumerate(b.T):
+        for ir, r in enumerate(a):
+            res[ir][ic] = np.dot(c, r)
+
+    return res
+
+a = np.random.random((100, 300))
+b = np.random.random((300, 100))
+
+print("a")
+print(a)
+print("b")
+print(b)
+
+custom = matrixmult(a, b)
+
+ref = a @ b
+
+print("custom")
+print(custom)
+print("ref")
+print(ref)
+
+if np.array_equal(custom, ref):
+    print("Yay they are the same, well done")
+else:
+    print("Not the same, bummer")
diff --git a/sem4/hpp/m9/opgave5.py b/sem4/hpp/m9/opgave5.py
new file mode 100644
index 0000000..0824ef0
--- /dev/null
+++ b/sem4/hpp/m9/opgave5.py
@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# A short template to test small kernels.
+# 
+
+import numpy as np
+import pyopencl as cl
+
+VEC_SIZE = 50000
+
+# 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.rand(VEC_SIZE).astype(np.float32)
+b_host = np.random.rand(VEC_SIZE).astype(np.float32)
+result_host = np.empty_like(a_host)
+
+# 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, a_host.nbytes)
+
+# Source of the kernel itself.
+kernel_source = """
+__kernel void sum(
+    __global const float *a_device, 
+    __global const float *b_device, 
+    __global       float *result_device)
+{
+  int gid = get_global_id(0);
+  result_device[gid] = a_device[gid] * b_device[gid];
+}
+"""
+
+# 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()
+
+# 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.sum(cmd_queue, a_host.shape, None, a_device, b_device, result_device)
+
+# Copy the result back from device to host.
+cl.enqueue_copy(cmd_queue, result_host, result_device)
+
+# Check the results in the host array with Numpy.
+print("All elements close?", np.allclose(result_host, (a_host * b_host)))
diff --git a/sem4/hpp/m9/opgave6.py b/sem4/hpp/m9/opgave6.py
new file mode 100644
index 0000000..3921551
--- /dev/null
+++ b/sem4/hpp/m9/opgave6.py
@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# A short template to test small kernels.
+# 
+
+import numpy as np
+import pyopencl as cl
+
+VEC_SIZE = 50000
+
+# 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.rand(VEC_SIZE).astype(np.float32)
+b_host = np.random.rand(VEC_SIZE).astype(np.float32)
+result_host = np.empty_like(a_host)
+
+# 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, a_host.nbytes)
+
+# Source of the kernel itself.
+kernel_source = """
+__kernel void sum(
+    __global const float *a_device, 
+    __global const float *b_device, 
+    __global       float *result_device)
+{
+  int gid = get_global_id(0);
+  result_device[gid] = a_device[gid] * b_device[gid];
+}
+"""
+
+# 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()
+
+# 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.sum(cmd_queue, a_host.shape, None, a_device, b_device, result_device)
+
+# Copy the result back from device to host.
+cl.enqueue_copy(cmd_queue, result_host, result_device)
+
+# Check the results in the host array with Numpy.
+print("All elements close?", np.allclose(np.sum(result_host), np.dot(a_host, b_host)))
diff --git a/sem4/hpp/m9/opgaver.md b/sem4/hpp/m9/opgaver.md
new file mode 100644
index 0000000..15ccf1e
--- /dev/null
+++ b/sem4/hpp/m9/opgaver.md
@@ -0,0 +1,7 @@
+## Opgave 1, 2
+
+> How many operations are involved in the multiplication?
+> Assume that all three matricies are of the data type float (IEEE754, aka Binary32, 4 bytes floating point). How much storage is needed to perform the operation?
+
+
+Løste denne i notesbog