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Commit f2b3fae2 authored by Matthieu Boileau's avatar Matthieu Boileau
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Use lbm_cl in a notebook

parent e3962aa7
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.gitignore 0 → 100644
View file @ f2b3fae2
.ipynb_checkpoints/
lbm_cl.py
View file @ f2b3fae2
......@@ -57,7 +57,8 @@ def solve_ocl(m = _m, n = _n, nx = _nx, ny = _ny,
Tmax = _Tmax, vmax = _vmax,
dx = _dx, dy = _dy,
dt = _dt,
animate = False):
animate = False,
interactive=True):
ff = "_F"
......@@ -76,7 +77,7 @@ def solve_ocl(m = _m, n = _n, nx = _nx, ny = _ny,
source = source.replace("_lambda_", "("+str(vmax)+ ff + ")")
source = source.replace("_F", "")
print(source)
#print(source)
#exit(0)
......@@ -113,6 +114,11 @@ def solve_ocl(m = _m, n = _n, nx = _nx, ny = _ny,
elapsed = 0.;
fn_cpu = np.empty((4 * m * nx * ny, ), dtype = np.float64)
if animate:
fig = plt.gcf()
fig.show()
fig.canvas.draw()
print("start OpenCL computations...")
while t < Tmax:
t = t + dt
......@@ -124,16 +130,20 @@ def solve_ocl(m = _m, n = _n, nx = _nx, ny = _ny,
elapsed += 1e-9 * (event.profile.end - event.profile.start)
# exchange buffer references for avoiding a copy
fn_gpu, fnp1_gpu = fnp1_gpu, fn_gpu
print("iter=",iter, " t=",t, "elapsed (s)=",elapsed)
if iter % iterplot == 0 and animate:
cl.enqueue_copy(queue, fn_cpu, fn_gpu).wait()
wplot = np.reshape(fn_cpu, (4, m, nx, ny))
plt.clf()
#plt.imshow(np.sum(wplot, axis = 0),vmin=0, vmax=1)
plt.imshow(np.sum(wplot[:, _ivplot, :, :], axis = 0))
plt.gca().invert_yaxis()
plt.colorbar()
plt.pause(0.01)
title = "iter = {}, t = {:f}, elapsed (s) = {:f}".format(iter, t, elapsed)
if animate:
if iter % iterplot == 0:
cl.enqueue_copy(queue, fn_cpu, fn_gpu).wait()
wplot = np.reshape(fn_cpu, (4, m, nx, ny))
plt.clf()
#plt.imshow(np.sum(wplot, axis = 0),vmin=0, vmax=1)
fig.suptitle(title)
plt.imshow(np.sum(wplot[:, _ivplot, :, :], axis = 0))
plt.gca().invert_yaxis()
plt.colorbar()
fig.canvas.draw()
else:
print(title, end='\r')
# copy OpenCL data to CPU and return the results
cl.enqueue_copy(queue, fn_cpu, fn_gpu).wait()
......@@ -141,28 +151,30 @@ def solve_ocl(m = _m, n = _n, nx = _nx, ny = _ny,
wplot_gpu = np.reshape(fn_cpu,(4, m, nx, ny))
return wplot_gpu
# gpu solve
wplot_gpu = solve_ocl(animate = True)
#print(np.sum(wplot_gpu[:, _ivplot, :, :],axis=0))
plt.clf()
#plt.imshow(np.sum(wplot_gpu[:, _ivplot, :, :],axis=0), vmin=0, vmax=1)
plt.imshow(np.sum(wplot_gpu[:, _ivplot, :, :],axis=0))
plt.gca().invert_yaxis()
plt.colorbar()
plt.show()
# for iv in range(4):
# plt.imshow(wplot_gpu[iv,:,:])
# plt.gca().invert_yaxis()
# plt.colorbar()
# plt.show()
# check difference
# plt.clf()
# plt.imshow(wplot_cpu-wplot_gpu)
# plt.gca().invert_yaxis()
# plt.colorbar()
# plt.show()
if __name__ == '__main__':
# gpu solve
wplot_gpu = solve_ocl(animate=False)
#print(np.sum(wplot_gpu[:, _ivplot, :, :],axis=0))
plt.clf()
#plt.imshow(np.sum(wplot_gpu[:, _ivplot, :, :],axis=0), vmin=0, vmax=1)
plt.imshow(np.sum(wplot_gpu[:, _ivplot, :, :],axis=0))
plt.gca().invert_yaxis()
plt.colorbar()
plt.show()
# for iv in range(4):
# plt.imshow(wplot_gpu[iv,:,:])
# plt.gca().invert_yaxis()
# plt.colorbar()
# plt.show()
# check difference
# plt.clf()
# plt.imshow(wplot_cpu-wplot_gpu)
# plt.gca().invert_yaxis()
# plt.colorbar()
# plt.show()
orszag-tang.ipynb 0 → 100644
View file @ f2b3fae2
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# lbm_cl\n",
"\n",
"## Orszag-Tang vortex on `gpu3.math.unistra.fr`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from lbm_cl import solve_ocl\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load OpenCL configuration on `gpu3`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"unset DISPLAY"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%env XAUTHORITY=/tmp/x11-auth-file\n",
"%env GPU_DEVICE_ORDINAL=0,1,2,3\n",
"%env LD_LIBRARY_PATH=/opt/AMDAPPSDK-3.0/lib/x86_64:$LD_LIBRARY_PATH\n",
"%env CPATH=/opt/AMDAPPSDK-3.0/include:$CPATH"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Target a GPU device\n",
"\n",
"The following cell is equivalent to:\n",
"```bash\n",
"\n",
"Choose platform:\n",
"[0] <pyopencl.Platform 'AMD Accelerated Parallel Processing' at 0x7f0b44371430>\n",
"Choice [0]:\n",
"Choose device(s):\n",
"[0] <pyopencl.Device 'Redwood' on 'AMD Accelerated Parallel Processing' at 0x35f8690>\n",
"[1] <pyopencl.Device 'Tahiti' on 'AMD Accelerated Parallel Processing' at 0x3ce9ec0>\n",
"[2] <pyopencl.Device 'Tahiti' on 'AMD Accelerated Parallel Processing' at 0x3a864e0>\n",
"[3] <pyopencl.Device 'Tahiti' on 'AMD Accelerated Parallel Processing' at 0x3f79ed0>\n",
"[4] <pyopencl.Device 'Intel(R) Xeon(R) CPU E5-2630 0 @ 2.30GHz' on 'AMD Accelerated Parallel Processing' at 0x49d2e40>\n",
"Choice, comma-separated [0]:1\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%env PYOPENCL_CTX=0:1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run computation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"%timeit wplot_gpu = solve_ocl(animate=False)\n",
"plt.clf()\n",
"plt.imshow(np.sum(wplot_gpu[:, 0, :, :],axis=0))\n",
"plt.gca().invert_yaxis()\n",
"plt.colorbar()\n",
"plt.show()"
]
}
],
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
%% Cell type:markdown id: tags:
# lbm_cl
## Orszag-Tang vortex on `gpu3.math.unistra.fr`
%% Cell type:code id: tags:
``` python
from lbm_cl import solve_ocl
import matplotlib.pyplot as plt
import numpy as np
```
%% Cell type:markdown id: tags:
### Load OpenCL configuration on `gpu3`
%% Cell type:code id: tags:
``` python
%%bash
unset DISPLAY
```
%% Cell type:code id: tags:
``` python
%env XAUTHORITY=/tmp/x11-auth-file
%env GPU_DEVICE_ORDINAL=0,1,2,3
%env LD_LIBRARY_PATH=/opt/AMDAPPSDK-3.0/lib/x86_64:$LD_LIBRARY_PATH
%env CPATH=/opt/AMDAPPSDK-3.0/include:$CPATH
```
%% Cell type:markdown id: tags:
### Target a GPU device
The following cell is equivalent to:
```bash
Choose platform:
[0] <pyopencl.Platform 'AMD Accelerated Parallel Processing' at 0x7f0b44371430>
Choice [0]:
Choose device(s):
[0] <pyopencl.Device 'Redwood' on 'AMD Accelerated Parallel Processing' at 0x35f8690>
[1] <pyopencl.Device 'Tahiti' on 'AMD Accelerated Parallel Processing' at 0x3ce9ec0>
[2] <pyopencl.Device 'Tahiti' on 'AMD Accelerated Parallel Processing' at 0x3a864e0>
[3] <pyopencl.Device 'Tahiti' on 'AMD Accelerated Parallel Processing' at 0x3f79ed0>
[4] <pyopencl.Device 'Intel(R) Xeon(R) CPU E5-2630 0 @ 2.30GHz' on 'AMD Accelerated Parallel Processing' at 0x49d2e40>
Choice, comma-separated [0]:1
```
%% Cell type:code id: tags:
``` python
%env PYOPENCL_CTX=0:1
```
%% Cell type:markdown id: tags:
### Run computation
%% Cell type:code id: tags:
``` python
%matplotlib notebook
%timeit wplot_gpu = solve_ocl(animate=False)
plt.clf()
plt.imshow(np.sum(wplot_gpu[:, 0, :, :],axis=0))
plt.gca().invert_yaxis()
plt.colorbar()
plt.show()
```
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