diff options
| -rw-r--r-- | implosion.py | 18 | ||||
| -rw-r--r-- | inspect_opencl_layout.ipynb | 590 |
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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+ "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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+ "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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+ "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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