Try out various OpenCL work group sizes using a Jupyter notebook

This is actually quite nice for this kind of experimentation!

2 files changed, 600 insertions, 8 deletions

diff --git a/implosion.py b/implosion.py
index 75b2fee..0851158 100644
--- a/implosion.py
+++ b/implosion.py
@@ -1,6 +1,5 @@
 import pyopencl as cl
 mf = cl.mem_flags
-from pyopencl.tools import get_gl_sharing_context_properties
 
 from string import Template
 
@@ -77,8 +76,9 @@ __kernel void collide_and_stream(__global       float* f_a,
                                  __global       float* moments,
                                  __global const int* material)
 {
-    const unsigned int gid = get_global_id(0);
-    const uint2 cell = cellAtIndex(gid);
+    const unsigned int gid = indexOfCell(get_global_id(0), get_global_id(1));
+
+    const uint2 cell = (uint2)(get_global_id(0), get_global_id(1));
 
     const int m = material[gid];
 
@@ -96,7 +96,7 @@ __kernel void collide_and_stream(__global       float* f_a,
         }
     }
 
-    moments[gid] = d;
+    moments[1*gid] = d;
     moments[2*gid] = v.x;
     moments[3*gid] = v.y;
 }"""
@@ -175,11 +175,11 @@ class D2Q9_BGK_Lattice:
     def evolve(self):
         if self.tick:
             self.tick = False
-            self.program.collide_and_stream(self.queue, (self.nCells,), None, self.cl_pop_a, self.cl_pop_b, self.cl_moments, self.cl_material)
+            self.program.collide_and_stream(self.queue, (self.nX,self.nY), (16,64), self.cl_pop_a, self.cl_pop_b, self.cl_moments, self.cl_material)
             self.queue.finish()
         else:
             self.tick = True
-            self.program.collide_and_stream(self.queue, (self.nCells,), None, self.cl_pop_b, self.cl_pop_a, self.cl_moments, self.cl_material)
+            self.program.collide_and_stream(self.queue, (self.nX,self.nY), (16,64), self.cl_pop_b, self.cl_pop_a, self.cl_moments, self.cl_material)
             self.queue.finish()
 
     def show(self, i):
@@ -197,9 +197,9 @@ class D2Q9_BGK_Lattice:
 def MLUPS(cells, steps, time):
     return ((cells*steps) / time) / 1000000
 
-LBM = D2Q9_BGK_Lattice(2000, 2000)
+LBM = D2Q9_BGK_Lattice(1024, 1024)
 
-nUpdates = 100
+nUpdates = 1000
 
 start = timer()
 
@@ -214,3 +214,5 @@ print("Cells:   " + str(LBM.nCells))
 print("Updates: " + str(nUpdates))
 print("Time:    " + str(runtime))
 print("MLUPS:   " + str(MLUPS(LBM.nCells, nUpdates, end - start)))
+
+LBM.show(nUpdates)
diff --git a/inspect_opencl_layout.ipynb b/inspect_opencl_layout.ipynb
new file mode 100644
index 0000000..ac7c72f
--- /dev/null
+++ b/inspect_opencl_layout.ipynb
@@ -0,0 +1,590 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thread layouting in OpenCL\n",
+    "## Setup PyOpenCL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyopencl as cl\n",
+    "import numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "platform = cl.get_platforms()[0]\n",
+    "context  = cl.Context(properties=[(cl.context_properties.PLATFORM, platform)])\n",
+    "queue = cl.CommandQueue(context)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pyopencl.Platform 'NVIDIA CUDA' at 0x280eab0>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "platform"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize array for storing group and local IDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nX = 16\n",
+    "nY = 16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np_data = numpy.ndarray(shape=(nX*nY, 4), dtype=numpy.int32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np_data[:,:] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0]], dtype=int32)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cl_data = cl.Buffer(context, cl.mem_flags.READ_WRITE | cl.mem_flags.USE_HOST_PTR, hostbuf=np_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pyopencl._cl.Buffer at 0x7fd9da045468>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cl_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define OpenCL kernel\n",
+    "\n",
+    "Simply writes out group and local indices to the given data array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "program = cl.Program(context, \"\"\"\n",
+    "__kernel void test(__global int* data)\n",
+    "{\n",
+    "    const unsigned int gid = 4*(get_global_id(1)*get_global_size(1) + get_global_id(0));\n",
+    "    data[gid + 0] = get_group_id(0);\n",
+    "    data[gid + 1] = get_group_id(1);\n",
+    "    data[gid + 2] = get_local_id(0);\n",
+    "    data[gid + 3] = get_local_id(1);\n",
+    "}\"\"\").build()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test output for a 1x1 work group size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  0,  0,  0],\n",
+       "       [ 1,  0,  0,  0],\n",
+       "       [ 2,  0,  0,  0],\n",
+       "       ...,\n",
+       "       [13, 15,  0,  0],\n",
+       "       [14, 15,  0,  0],\n",
+       "       [15, 15,  0,  0]], dtype=int32)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "program.test(queue, (nX,nY), (1,1), cl_data)\n",
+    "queue.finish()\n",
+    "cl.enqueue_copy(queue, np_data, cl_data).wait();\n",
+    "np_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test various work group sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeWorkgroup(i, j):\n",
+    "    program.test(queue, (nX,nY), (i,j), cl_data)\n",
+    "    queue.finish()\n",
+    "    cl.enqueue_copy(queue, np_data, cl_data).wait();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],\n",
+       "       [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]], dtype=int32)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "computeWorkgroup(4,4)\n",
+    "np_data.reshape((nX, nY, 4))[:,:,0] # x-index of work group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
+       "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
+       "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
+       "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
+       "       [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
+       "       [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
+       "       [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
+       "       [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], dtype=int32)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np_data.reshape((nX, nY, 4))[:,:,1] # y-index of work group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "computeWorkgroup(8,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np_data.reshape((nX, nY, 4))[:,:,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
+       "       [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
+       "       [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
+       "       [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
+       "       [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n",
+       "       [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n",
+       "       [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n",
+       "       [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n",
+       "       [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n",
+       "       [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],\n",
+       "       [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\n",
+       "       [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]], dtype=int32)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np_data.reshape((nX, nY, 4))[:,:,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],\n",
+       "       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7]], dtype=int32)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np_data.reshape((nX, nY, 4))[:,:,2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+       "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np_data.reshape((nX, nY, 4))[:,:,3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate some visuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotWorkgroupConfig(i,j):\n",
+    "    computeWorkgroup(i,j)\n",
+    "    data = np_data.reshape((nX, nY, 4))\n",
+    "    for k in range(0,4):\n",
+    "        plt.subplot(1,4,k+1)\n",
+    "        plt.imshow(np_data.reshape((nX, nY, 4))[:,:,k])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotWorkgroupConfig(1,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotWorkgroupConfig(4,4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotWorkgroupConfig(8,8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotWorkgroupConfig(16,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}