diff --git a/binder/environment.yml b/binder/environment.yml
index 5a58aeafe4d3af7f4fd3e3c35109e92f4671011a..8c72c2cecece2b3efa3f7785a92aa82b805d09ca 100644
--- a/binder/environment.yml
+++ b/binder/environment.yml
@@ -7,7 +7,8 @@
# conda env create -f conda_environment_user.yml
# . activate pystencils
#
-# If you have CUDA installed and want to use your GPU, uncomment the last line to install pycuda
+# If you have CUDA or ROCm installed and want to use your GPU, uncomment the last line to install cupy
+# Be careful to install the correct cupy version depending on your CUDA or ROCm version ...
#
# ----------------------------------------------------------------------------------------------------------------------
@@ -32,4 +33,4 @@ dependencies:
- ipy_table # HTML tables for jupyter notebooks
- pyevtk # VTK output for serial simulations
- blitzdb # file-based No-SQL database to store simulation results
- #- pycuda # add this if you have CUDA installed
+ #- cupy # add this if you have CUDA or ROCm installed
diff --git a/doc/notebooks/01_tutorial_getting_started.ipynb b/doc/notebooks/01_tutorial_getting_started.ipynb
index bceef66c5679fd502dc94337e2794b6b1ef2b20b..b326e17df7059bf16776920d056b74a078643fb4 100644
--- a/doc/notebooks/01_tutorial_getting_started.ipynb
+++ b/doc/notebooks/01_tutorial_getting_started.ipynb
@@ -70,7 +70,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "4.92 ms ± 124 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+ "4.78 ms ± 9.53 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
@@ -120,7 +120,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": "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",
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
@@ -179,7 +179,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "1.01 ms ± 24.6 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+ "987 µs ± 5.12 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
@@ -260,7 +260,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/latex": [
"$\\displaystyle x^{2} \\left(x + y + 5\\right) + x^{2}$"
],
@@ -293,7 +293,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/latex": [
"$\\displaystyle x^{3} + x^{2} y + 6 x^{2}$"
],
@@ -318,7 +318,7 @@
"outputs": [
{
"data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHsAAAAXCAYAAAAr8TBeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAABJ0AAASdAHeZh94AAAFN0lEQVR4nO2ae7CWUxTGf+cUI9VEbg2K3GKmwRCV0aF0ZDSm3P7QiBDDiJJQLj09htRUOhNqQqP4R1SDErlMVIrJJZEwUUMjYrq45ZLyx95fHe95v3Pqu51M55n5Zn2z19rver693r322nt/Zdu3b6cBewca59PZ9jDgEqAd8CfwLjBM0qcF4NaAAqM8z/7nAhOBs4BuwFbgDdst83xuA4qAskKmcdvNgM1Ab0mzU/RPAxcAbSX9VjDHDagB26cD7wP9JU2BPNN4CpoTssWGFOcdgCuBIQ2Bzh22uwCDCNm0JWGsPwGqJM3N2En6wPYLwAO2p0v6Nd80nkQVsIywdicxEvgZmFRgn3sNbN8LLAAqgFeBccBs4EDCkprEQ0Ar4FYoYBq3PYYwc7tIWpXQnQB8Djwp6YaCOKybTz/gKaCrpLdK4bM25MvH9uXAc8AbwCWSfkno95H0d0q/lcD+wDGNE4rXgErgUkmzqrWXRaJXA6MlDU30G0cIdNdkoCOuBcqA6Vl+SE5+iwHbtwNjCcvNuBR9O2A58J6kimLziT7LgdHA70CfZKAB0gId8SwwAuieTON3ANsIeb5RtfaxhAF/IiXQE9gZ6M+yOOwO/EN6es/JbxGxKMpOWfSPAI2AAaWhA4T1uS0wF9hou6ftu2wPtN25jr7vRFn5n2BL+hh4BjgJ6Atg+25gMCGF3Fjd3vZEoB9wBbDBdqv4aVbNpilwKrAyW2G2u36LjA+BLUDHpCKm0krgMUnLS8jpjCh/IPCbA4wi1EiLbb9t+5AsfZdGWZFWoN0L/AGMsD0AeBCYB/SVtC1hexOhAn8TWFftM6SazRGEmbCujh+0O36LhpgOlwKtbR+eaY8v7cPAemB4qfhEHBrljUATQqZsDrQnjFEF8HxaR0mbCePapsbWS9Ja21XAUELKWkwoCP5KsS3bBaIHRbmxNqPd8ZuE7TXAUVnU820n26ZJ6lfLI98hDGAnIFNDDAeOBK6JA1hKPpmlrQy4LGZCgBW2Lwa+BM6x3VnSkpT+G4DDsu2zf6z2/TpJv9dCpC5siXK/XbDN1W8VcECi7VSgFzANWJPQLavjeZl1riMwy/aJwG3Akvi8UvPJTJSvqwUaAElbbM8DrgPOjByTaAJsqRFs21cQCqPvCXu0gYR0nSvWR3lQbUb5+JVUlfK8foTBnZrDVmcxsJ2dRdqjhNl1s6Q696pF4PNFlJuy6DMvQ5MUv+WEF291eUJxIeHNWwGcTNgb949vdq5YR5ix7bIZFMlvzpC0EVgJdLDdBzgPmCzpo/rgQzhI2Qocb3vfFH37KNek6NoR0v+yHcG2fTYwA1gLnC/pR+A+wpHqqFxZxpmwADjY9nFJfbH8FgCLCIcRk4GfCAVkvUDST4QzihYkikPblUAPwp3EqyndM9lpfnnscAqhnN8MVEpaF53MIBym94pnsrliZpQ9EkSL7TcfZNbtZoRr2xrn/SXGYGAVcI/tBbbH2n4eeIVwhnG9pE0p/c6P+hfL42ybR1ijekj6KmE8LMoxeRCdSdgjXpVpKJHffLA6yqXAlHrisAOS1hMKxvFAa8J5dzfgZcIRdY2tl+0WQG9gjqRvC3rFWRviHx1GAqfV49q3y7D9EtAT6CRpaV32eyJs3wJMACokLSz0rVdtGA98A9xfQp85IRZlFwGT/seBbkLIjjMlLYTC32dnhaQ/bPcFutpuuqfdadtuA/QBjiUsNyuAO+uVVH44GngcmJppKFka39Nh+wZC5b0JeB0YJOm7eiVVYDQEey/CvxiiNb+5640IAAAAAElFTkSuQmCC\n",
+ "image/png": "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",
"text/latex": [
"$\\displaystyle x^{2} \\left(x + y + 6\\right)$"
],
@@ -343,7 +343,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/latex": [
"$\\displaystyle x^{2} \\left(x + \\cos{\\left(x \\right)} + 5\\right) + x^{2}$"
],
@@ -375,7 +375,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/latex": [
"$\\displaystyle x^{2} \\left(x + y + 5\\right) + x^{2} = 1$"
],
@@ -401,7 +401,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/latex": [
"$\\displaystyle \\left[ - x - 6 + \\frac{1}{x^{2}}\\right]$"
],
@@ -435,7 +435,7 @@
"outputs": [
{
"data": {
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+ "image/png": "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",
"text/latex": [
"$\\displaystyle x^{2} \\left(x + y + 5\\right) + x^{2}$"
],
@@ -459,11 +459,167 @@
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "graphviz is not installed. Visualizing the AST is not available\n"
- ]
+ "data": {
+ "image/svg+xml": [
+ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+ "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+ " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+ "<!-- Generated by graphviz version 2.43.0 (0)\n",
+ " -->\n",
+ "<!-- Title: %3 Pages: 1 -->\n",
+ "<svg width=\"425pt\" height=\"260pt\"\n",
+ " viewBox=\"0.00 0.00 425.00 260.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+ "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 256)\">\n",
+ "<title>%3</title>\n",
+ "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-256 421,-256 421,4 -4,4\"/>\n",
+ "<!-- Add(Pow(Symbol('x'), Integer(2)), Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y'))))_() -->\n",
+ "<g id=\"node1\" class=\"node\">\n",
+ "<title>Add(Pow(Symbol('x'), Integer(2)), Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y'))))_()</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"136\" cy=\"-234\" rx=\"28.7\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"136\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">Add</text>\n",
+ "</g>\n",
+ "<!-- Pow(Symbol('x'), Integer(2))_(0,) -->\n",
+ "<g id=\"node2\" class=\"node\">\n",
+ "<title>Pow(Symbol('x'), Integer(2))_(0,)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"98\" cy=\"-162\" rx=\"29.8\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"98\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">Pow</text>\n",
+ "</g>\n",
+ "<!-- Add(Pow(Symbol('x'), Integer(2)), Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y'))))_()->Pow(Symbol('x'), Integer(2))_(0,) -->\n",
+ "<g id=\"edge1\" class=\"edge\">\n",
+ "<title>Add(Pow(Symbol('x'), Integer(2)), Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y'))))_()->Pow(Symbol('x'), Integer(2))_(0,)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M127.19,-216.76C122.65,-208.4 117.01,-198.02 111.9,-188.61\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"114.88,-186.75 107.03,-179.63 108.72,-190.09 114.88,-186.75\"/>\n",
+ "</g>\n",
+ "<!-- Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,) -->\n",
+ "<g id=\"node5\" class=\"node\">\n",
+ "<title>Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"174\" cy=\"-162\" rx=\"28.7\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"174\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">Mul</text>\n",
+ "</g>\n",
+ "<!-- Add(Pow(Symbol('x'), Integer(2)), Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y'))))_()->Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,) -->\n",
+ "<g id=\"edge2\" class=\"edge\">\n",
+ "<title>Add(Pow(Symbol('x'), Integer(2)), Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y'))))_()->Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M144.81,-216.76C149.42,-208.28 155.16,-197.71 160.32,-188.2\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"163.54,-189.61 165.23,-179.15 157.39,-186.27 163.54,-189.61\"/>\n",
+ "</g>\n",
+ "<!-- Symbol('x')_(0, 0) -->\n",
+ "<g id=\"node3\" class=\"node\">\n",
+ "<title>Symbol('x')_(0, 0)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"27\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
+ "</g>\n",
+ "<!-- Pow(Symbol('x'), Integer(2))_(0,)->Symbol('x')_(0, 0) -->\n",
+ "<g id=\"edge3\" class=\"edge\">\n",
+ "<title>Pow(Symbol('x'), Integer(2))_(0,)->Symbol('x')_(0, 0)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M82.94,-146.15C73.02,-136.37 59.87,-123.4 48.81,-112.5\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"51.13,-109.87 41.55,-105.35 46.21,-114.86 51.13,-109.87\"/>\n",
+ "</g>\n",
+ "<!-- Integer(2)_(0, 1) -->\n",
+ "<g id=\"node4\" class=\"node\">\n",
+ "<title>Integer(2)_(0, 1)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"99\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"99\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">2</text>\n",
+ "</g>\n",
+ "<!-- Pow(Symbol('x'), Integer(2))_(0,)->Integer(2)_(0, 1) -->\n",
+ "<g id=\"edge4\" class=\"edge\">\n",
+ "<title>Pow(Symbol('x'), Integer(2))_(0,)->Integer(2)_(0, 1)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M98.25,-143.7C98.36,-135.98 98.49,-126.71 98.61,-118.11\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"102.11,-118.15 98.76,-108.1 95.11,-118.05 102.11,-118.15\"/>\n",
+ "</g>\n",
+ "<!-- Pow(Symbol('x'), Integer(2))_(1, 0) -->\n",
+ "<g id=\"node6\" class=\"node\">\n",
+ "<title>Pow(Symbol('x'), Integer(2))_(1, 0)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"174\" cy=\"-90\" rx=\"29.8\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"174\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">Pow</text>\n",
+ "</g>\n",
+ "<!-- Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,)->Pow(Symbol('x'), Integer(2))_(1, 0) -->\n",
+ "<g id=\"edge5\" class=\"edge\">\n",
+ "<title>Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,)->Pow(Symbol('x'), Integer(2))_(1, 0)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M174,-143.7C174,-135.98 174,-126.71 174,-118.11\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"177.5,-118.1 174,-108.1 170.5,-118.1 177.5,-118.1\"/>\n",
+ "</g>\n",
+ "<!-- Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1) -->\n",
+ "<g id=\"node9\" class=\"node\">\n",
+ "<title>Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"284\" cy=\"-90\" rx=\"28.7\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"284\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">Add</text>\n",
+ "</g>\n",
+ "<!-- Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,)->Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1) -->\n",
+ "<g id=\"edge6\" class=\"edge\">\n",
+ "<title>Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,)->Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M193.41,-148.65C210.74,-137.62 236.33,-121.33 255.9,-108.88\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"257.85,-111.79 264.41,-103.47 254.1,-105.88 257.85,-111.79\"/>\n",
+ "</g>\n",
+ "<!-- Symbol('x')_(1, 0, 0) -->\n",
+ "<g id=\"node7\" class=\"node\">\n",
+ "<title>Symbol('x')_(1, 0, 0)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"102\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"102\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
+ "</g>\n",
+ "<!-- Pow(Symbol('x'), Integer(2))_(1, 0)->Symbol('x')_(1, 0, 0) -->\n",
+ "<g id=\"edge7\" class=\"edge\">\n",
+ "<title>Pow(Symbol('x'), Integer(2))_(1, 0)->Symbol('x')_(1, 0, 0)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M158.73,-74.15C148.67,-64.37 135.33,-51.4 124.11,-40.5\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"126.36,-37.81 116.75,-33.35 121.49,-42.83 126.36,-37.81\"/>\n",
+ "</g>\n",
+ "<!-- Integer(2)_(1, 0, 1) -->\n",
+ "<g id=\"node8\" class=\"node\">\n",
+ "<title>Integer(2)_(1, 0, 1)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"174\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"174\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">2</text>\n",
+ "</g>\n",
+ "<!-- Pow(Symbol('x'), Integer(2))_(1, 0)->Integer(2)_(1, 0, 1) -->\n",
+ "<g id=\"edge8\" class=\"edge\">\n",
+ "<title>Pow(Symbol('x'), Integer(2))_(1, 0)->Integer(2)_(1, 0, 1)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M174,-71.7C174,-63.98 174,-54.71 174,-46.11\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"177.5,-46.1 174,-36.1 170.5,-46.1 177.5,-46.1\"/>\n",
+ "</g>\n",
+ "<!-- Integer(5)_(1, 1, 0) -->\n",
+ "<g id=\"node10\" class=\"node\">\n",
+ "<title>Integer(5)_(1, 1, 0)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"246\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"246\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">5</text>\n",
+ "</g>\n",
+ "<!-- Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Integer(5)_(1, 1, 0) -->\n",
+ "<g id=\"edge9\" class=\"edge\">\n",
+ "<title>Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Integer(5)_(1, 1, 0)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M275.19,-72.76C270.58,-64.28 264.84,-53.71 259.68,-44.2\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"262.61,-42.27 254.77,-35.15 256.46,-45.61 262.61,-42.27\"/>\n",
+ "</g>\n",
+ "<!-- Symbol('x')_(1, 1, 1) -->\n",
+ "<g id=\"node11\" class=\"node\">\n",
+ "<title>Symbol('x')_(1, 1, 1)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"318\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"318\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
+ "</g>\n",
+ "<!-- Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Symbol('x')_(1, 1, 1) -->\n",
+ "<g id=\"edge10\" class=\"edge\">\n",
+ "<title>Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Symbol('x')_(1, 1, 1)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M292.06,-72.41C296.08,-64.13 301.04,-53.92 305.54,-44.66\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"308.78,-45.99 310,-35.47 302.48,-42.94 308.78,-45.99\"/>\n",
+ "</g>\n",
+ "<!-- Symbol('y')_(1, 1, 2) -->\n",
+ "<g id=\"node12\" class=\"node\">\n",
+ "<title>Symbol('y')_(1, 1, 2)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"390\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"390\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">y</text>\n",
+ "</g>\n",
+ "<!-- Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Symbol('y')_(1, 1, 2) -->\n",
+ "<g id=\"edge11\" class=\"edge\">\n",
+ "<title>Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Symbol('y')_(1, 1, 2)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M302.95,-76.49C319.71,-65.42 344.35,-49.15 363.14,-36.74\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"365.15,-39.6 371.57,-31.17 361.29,-33.76 365.15,-39.6\"/>\n",
+ "</g>\n",
+ "</g>\n",
+ "</svg>\n"
+ ],
+ "text/plain": [
+ "<graphviz.sources.Source at 0x7f41d153f050>"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
@@ -505,7 +661,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/latex": [
"$\\displaystyle \\left( x^{2}, \\ x^{2} \\left(x + y + 5\\right)\\right)$"
],
@@ -562,7 +718,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/latex": [
"$\\displaystyle {f}_{(1,0)}^{1}$"
],
@@ -632,16 +788,13 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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",
"text/latex": [
"$\\displaystyle \\left({img}_{(1,0)}^{2} w_{2} - {img}_{(1,1)}^{2} w_{1} - {img}_{(-1,1)}^{2} w_{1} + {img}_{(1,-1)}^{2} w_{1} - {img}_{(-1,-1)}^{2} w_{1} - {img}_{(-1,0)}^{2} w_{2}\\right)^{2}$"
],
"text/plain": [
- " \n",
- "(img_E__2â‹…wâ‚‚ - img_NE__2â‹…wâ‚ - img_NW__2â‹…wâ‚ + img_SE__2â‹…wâ‚ - img_SW__2â‹…wâ‚ - img\n",
- "\n",
- " 2\n",
- "_W__2â‹…wâ‚‚) "
+ " 2\n",
+ "(img_E__2â‹…wâ‚‚ - img_NE__2â‹…wâ‚ - img_NW__2â‹…wâ‚ + img_SE__2â‹…wâ‚ - img_SW__2â‹…wâ‚ - img_W__2â‹…wâ‚‚) "
]
},
"execution_count": 26,
@@ -671,16 +824,13 @@
"outputs": [
{
"data": {
- "image/png": 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2sqpugnXmBjrpOLnvcYpyNHBmuzuuChH5I7ZSx0ecqJ0xAcK3PM8L/j833EBVB1V1rapOitnvNdia8mdgUu4NvM+R4HXY8OA33fSe3iXBuY58y5je+9YgUuqePHhv+oyyrlkF4X3qMhpSDxWF9y8nTSj/LmovnwMc20mnCeLnOAEgIg+r6pWqupeItL0+fEUocJGqXhKekGDSW+yKJhEmYy8z+0dcpIgsBBampD8KuEtE6ni/kqdehjnXqW8Z03vfmseIuicP3pu+pZRrVqd4n7qWWuuhovD+tU3t5d/09rKqTgNukOEv122LtBEnRORO4M5OD1I2IvJzbDLcFjmTvg84QSIvNMvJGuDTbab1dDFtOud96zE6qHvy4L3pMWq8ZhWB96lhdEk9VBTeP4cuKf+6y+1uEbmuiB0lznHyeDwej8fj8Xg8Ho/xf0JqChoC1iN1AAAAAElFTkSuQmCC\n",
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",
"text/latex": [
"$\\displaystyle \\left({img}_{(1,0)}^{2} w_{2} - 0.5 {img}_{(1,1)}^{2} - 0.5 {img}_{(-1,1)}^{2} + 0.5 {img}_{(1,-1)}^{2} - 0.5 {img}_{(-1,-1)}^{2} - {img}_{(-1,0)}^{2} w_{2}\\right)^{2}$"
],
"text/plain": [
- " \n",
- "(img_E__2â‹…wâ‚‚ - 0.5â‹…img_NE__2 - 0.5â‹…img_NW__2 + 0.5â‹…img_SE__2 - 0.5â‹…img_SW__2 -\n",
- "\n",
- " 2\n",
- " img_W__2â‹…wâ‚‚) "
+ " 2\n",
+ "(img_E__2â‹…wâ‚‚ - 0.5â‹…img_NE__2 - 0.5â‹…img_NW__2 + 0.5â‹…img_SE__2 - 0.5â‹…img_SW__2 - img_W__2â‹…wâ‚‚) "
]
},
"execution_count": 27,
@@ -707,16 +857,13 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/8AAAAaCAYAAADhY+z6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAABJ0AAASdAHeZh94AAAXN0lEQVR4nO2defwVZb3H3yAKXiUXuoJLAUqoZFfFtQT8EYb7dbktppmEt0VxXyrK/Pp1q7ypcM2luiWKWmbFNcuFQHHJNYVSc1dwjcIFwZugyP3j+4y/+c2ZOWfmnDln5pzf8369eM2PeeaZec48n/nOPN/5Pt/ps3r1ajyeTkVV+4vIiqLbUQtVnQocDGwJrADuBaaKyCOFNszTkXi9eQK8Fjz14HXjyROvp96N7//aqOpaIrKyxjapzmPfprXS4ykYVf0yMKLodqSkC7gE+ATwSeBdYI6qblhkozwdSxdebx6jC68FT3a68Lrx5EcXXk+9mS58/9diW1Xds8Y2XaQ4j32yvPlX1Q8BzwOzROTgLC3udFR1GPAccIWITMphf8cBXwOGAwOAE0VkWqP77S2o6nhgoohMjSk7CTgfOExErml541KgqusCS4EDReSGotvjaYyya87rrTWUXQfgtVBWyq4dr5v2oexaAq+nZuL7v31R1fOBGSLycMrtY89jv4zH3cEtH8pYL2jEicAFlFhwSajqY8AKEdmuBcc6BJgOzAem4UI38nYwdCqqug7wPcwDFseObvmnljSoPgZikTmvF92QTkBVNwPOBPYCBgGvAP8LqIikPsequhAYmlC8WESGJJSVXXO9Rm8Fa6HsOoBepIUseBtSE6+bDHg7VBOvp+bh+799ORO4QVW7ROS9FNvHnsesg//RbvlgxnoBgfOg3vpFMguYqqrDReS5Jh9rv2ApIi8HK93g31ObrwO3icg/E8q/iV1AT7WuSZmZDiwA7im4HW2Pqm4B3A1sBFwPPA7sDBwP7KWqu4nIqxl2uRRzykVZXqVO2TXXK/RWAi2UXQfQS7SQhRLoBsqvHa+blJRAT2XXEng9NRPf/22KiCx1L6MPB65IUSX2PLb0zb+rvxx4ss76RTILmAochEUvNJNNAMIDf086VHUgcBywS9I2IvJ861qUHVW9ABgDjBGRVUW3pwO4BHvIOk5ELgpWuvN8InAONsUmLW+IyBlZGlBmzfUyvRWqhTLrADpXC6o6CbgcGC8i8+rYhbchVehU3TQRb4eq4PXUXHz/tz0/BX6lqj+vlgCw2nmsGPyraj9gCnAkMBJYjBmq87A3/y+LyOLQ9mMxY7UtsCnwJrAQmBvMt1bV7wHfCB3mPVUN/v6iiMxM/5sL40/Ai1gWxcyDf1XdBTgV64gNsfN6Ixbi9bLb5gxAQnXCCRk0VHaEqh4RKvuSiMxwdSYB+wPbAxsD7wAPA5eKyFWRNg3DTSMAzgXOAsYDH8QSRSysVh48RKnqZ4FjMA2sBTwNXANcEM607+aevAY8ICK7hdavjYWk9CeiB1U9CtPfkSLys5hTG+XTwGsiEutgcrkAbgV+ICKnxqw/37X9dGAcsIZbf5SI/E1VRwGnAXu49t4JHB01pqo6ADgB+CKwOdbfPwK+D7wBLBSRj8W070LgEOwh9dkUv7cpqOoEYA5wloicHlq/C5Y9FGC4iCwMlV0FHApsLSJPtLC5ibg3LBMxLV8cKRbgK8DhqnqyiLzVpDZUaC5vvbl9ZtZcWfTWCorWgrc97UnRunFt8DakQyhaT94O9W58/+dPq5+XReR+N715P+A3CW2qeh77RTZeC/gd8CksTOAibC7SGVjW9CHADaHtv4V5KJ8HbgGWAIOx+SR7Ym/KwSIFrgCOwEKd/hA67Ly0P7hIRGS1qs4Cpqjq4LADpBaqOhn4MTZ3/7fAC8BHgP8E9lfVXd2FM89VmYTN4dLQbuYB62NhYX/G5oYFLAj9fSnwKHAHNodsELAPMFNVtxSR78Q0cQvgPiwi42pgbcyJU7NcVc/F+nkJZjCWA3tjzoI9VXVi4JkSkeWqej+wi6oOFJFlbv+7YUYEYAIQdgZNcMu5Me2O49NUj0wJpq5EtwnWjwTuAm7CvGt7AQcC/VX1UuDnmCG8AsspsC9wJaH8Au6inAPsik1xmY5dF6cDWwPrxrVRVacDn8Mu1sdr/9SmEswPGhhZH3bibYg9wKCqmwCfBW4oy8DfMd4tZ0fnR4nIMlX9I/YgtivpNdZfVb8AfBh4C/gLcEcVD3Wc5nLTG9SnuZLprRUUrQVve9qTonUD3oZ0EkXryduh3o3v//wp4nl5PjbeqRj8pzmP0Tf/F2MD/9OBs0VktdvRDOB2t81Dbt1gbM7IXcCEaOiBqn4w+FtEfqmq62OD/ytE5MeZfmJ5mAUcCxyADeZroqojgcuwTt9dRF4KlU0AZmMXxkHuTfo8Ve0ChkbDuNQSuxwPLKgS4rWNiDwTqbcWdiF/U1UvC7fBMQb4roh8K1JvWI3yj2MD/xeAnUXkb279VOxc7QecgjkCAm7FBvvjgN+7dROAVZjGgsE+qtoXu1E+KyKLEn4vke3HYJ7FJGoZvp2BXUXkL26fZ2LOrYlYNMWnROQeVxZEOYxT1QEi8rbbx48woxe9jmZiBrHi+Kp6MTaH50DgdVUNkvQsF5Fq80CbRYUxc1o+AHM8HQhsENp+CrAm8F+taV5qtnTLpKlGT2F9O5L0D1pD6OmgAnhOVb8kIrfHbF/twT0PvUFGzZVQb62gaC1429OeFK0b8DakkyhaT94O9W58/+dPEc/LDwGHRVemPY99QxV2xt5E/05Ezgo6C0BE7gAec/8NkvVthYWDPBkd+Ls6SyKrkgTXTtwBvIrN+0/LUVgnHx8ddIvIXCwSYH+1ueoNEx34u3UrMcdOP0KD6xCL6RllkLZ8slueHQz83fHeBU4G3sM0FSa4mYXbMQHT1W+AzdxFA7Ad5i1LewPcHPgAzruWwGji804E+pwUGD0wT7zb3xrAqYHRc2UrgSeAPsA6AKq6K3ZBXh9zHc3FjChUXgdHY4ZjLhaxEfw7pdoPbiJxnsxTgGXYFCCwvgnCu74C3Ccid7WshelYzy2XJpQH69dPub/LMb0Owfr8Y9iNbhhwk6puG1MnTnO56A3q1lzZ9NYKitaCtz3tSdG6AW9DOomi9eTtUO/G93/+FPG8vAjYRFUHRdanOo/hN//HuuU5CQcKMo8GHfYoZqQmq+q/YuHgsyX5EyWj6Z5/3gNV3QDLdvqJuMFrs1DV64B7ReT8NNuLyCpVvQE4VFXXE5Ek4x3m4265u6ruFFO+EXZRjSSHryCo6oexUJMJWAjY2pFNNo2p9mcJzc3PUB4Yi1ujBSLypKq+CAyPnKt7gH+69qGq67n9nBfazwTMMH0yaf8JDHfL2H5xoUgjgbvDBim0/lkRuTmm6lAsV8G1CWXLpDsz7xS3rHYdfYieUzUQkT4J2xfFUsx5MxDej/T5Ihal8oLbZkO3PBzLA3FUowfV6p8tiuNqEflCo8dNi4hEnWCPAF9T1eWYw+sMQs7BOM3lrDeoQ3Ml1FsFnaQFb3taRw3d3KZa4cdu6adzvQ1pL7wdArwdKoS8tef7v2kU8bwcjHOG0z0+T30ew4P/iW4H9yVsuznw9+DttYgsUdUxWIKSfbAkc6tU9Q/At0UkHB7WD/NG/jVhEPkt4MbowF9Vj8aS5G2MORtOEJE70/ywlPXPBG5X1f9JOZAHezs9CZvnck2K7QOvzKlVt7I5MA2hqpsD92PhJXdiUwqWYiH1w7BpF/1jqv4tZl2a8sCD/UpC+SuYA2J91w5EZKWq3gXs4ZxGn8CcH3NF5DFVfQUb/F/qlqtJP/gPwmreTCjfFot2iXocg/VzohXc1IcNgN+IyDuRsnWxXBhh711wHSV9P3Vj4CnpzndQSsRyXCyl25N5PObdnY55M6H7fB8PPENo7pGb+nEwFuK4Akt6MlVEHqlx6GeAt2tsE6bWFzGC63q9hPJg/RsZjhnHZdiD1rjI+jjN5ak36BDNxdBJWvC2p3VMo/Kt6XZYCOYVVEaGLaixP29Deo924vB2yNuhoshbe77/m0BBz8vBOGf9etrczx14APYGen7YGxRq2Gjs83M3hde7hn3GzfsYh4UyfAbYSVU3DQ30RwEDiE/w8C+4xHeR9Z/DTtzRmLCOxkKYRkmKz1SkqS8iD6vqs8AXqMy6msQfsIQqB5Fu8P++oReRpEFpXpyEORvez/4foKqfxwb/cVT0ecry4LcNwcQcZePIdgG3YrklJmCD/7eBP4bK9lbV/sBY4FER+XuN9gUEjo3/SygPIhXmJ6yPi7zYoUrZ9tgFHuTBCK6jBQnX0VbYuZoXU1bG6JfXgYHOwB8FXCPdX6ZYBWyoqhOBjwLHSM/kRV3YVxoewM7RmcAcd/29ltQeEYmbltIIQTKVkQnlH3HLRj8/+g+3XCeyPk5zuegN2k9zWaKtOkwL3vY0QEbdTIupPwkb/M+Q7J/68zYkRjvtoJskvB3ydqgoCtae7/8GKNnzcjDOGVDPbwne/K9y/zZK2O7bbhk7X9/N+5iDNfZOLOnaYLrndmznllHBgUUNrKZ78BdwEnaj/on7/7Gquhd2UqdSm7T1fwt8npSDfxF5W1VvAvbSyoQ5cdyLXTxj6U5wVy+r3HKNhPIRbvnrmLLdGzx2HPMxo9FFZPCvqiOAzYDnROSNSL3wvP+PYyFIb4fKDsP6aR3Sz/cHm04AyVEUtRKdxHkqd6hStn1kf8F1tEHMtgBfTzg+xES/qOo4bJ7ODpjzrcKpU4sGo19ed7/lK9jbiHBykjexMKYTMC/v5eGKIrJnpB2HY06g3Qh9MaQF3OaWE1W1b9jgquXZ2A0zovfGVc7Arm4Z/aRKtURdjeoNctRco3pLWb+eaKu8KFILbWV7suB102ttSFPuWY3i9fQ+3g61WHsp21Gk9nz/V99HOz0vB+OcfyaUV6WvO/A7WIbRTVU1+gb+G1g4AjjPj6pur/atUiLbjgC2wQb9L4aKgtD3uDffY4EHI/NP1sI6cHZk29nYm+KqZKx/P7Cz2rfm0zILO/ETU2z7QyzXwYXanciuR1tVdWzK476OOUo+nFC+0C27IsfYk8rEe3nwM7c8zYXwB8dbA/gBpq+fxtR7CBP2AZgXLDzAD0L8p0b+n4bAQ/aBhPLRWJTBX2PWr8TmykWp5vXsYUjddfQEMFTtSw7vo6pTgC+Ftw+VBdEv0XO1rmvT8dRxgYeiX87FjPTdWPTL+/oRkYexh4O4eWKBMTsBM8rh87YU2AX7DMwlIpIUbREwENNDUk6QpuBuJLOxaS9TIsWKOZhmSuR7yqq6hapupaprhtZtrTY3jsi2w7DrHOCqSHGc5nLRG+SuuYb0lqZ+Db01lXq0EKcDtz6rFtrN9mTB66aX2ZBm3bNyouP05O1QKsqgvZrtKFJ7+P5PpA2fl4NxzqtVtkkkPOf/u9h8uF+r6i+wed5d2Fz9F7AEDUGHHQtMUtV7MW/QciwnwL+78smRkIZANOeo6jZY2PyjInIdliwiOk/lg9jb7cWR9YuBPVL8riz1X8ay8W9CfOh6HL/DLpSDsMiBRETkcVWdjA2UH1XVm7FwrjWxQfxYLDxrq1oHFZHlqnofMFZVr3b7WQX8Viw75yXYxXWdqv7K/bZtMMH9EvvuY26IyN2qeh7mzXvEHfMtYG933LuI+ZSFWOLEedjgH0KDfxFZpKrPAFvQ/fm/tAQe7oq5dGrTCEZh4Ujvxqz/i8R8tQIzfIukZ3KkgNGYkXkstO672Cd3fq+q12LX0RgslO9xbE5P1OsZG/0iIjcCN7p2zog5fi0ajX4JjNkGWJKSMG9gET1v0/2QUY3p2Nzae2ps1wyOxgz5f7sb0mOYIR6PXUPfjqkzF7NNw+l2qn0OOFlV78AyrS7DdLovFnp1I+b0AuI11wS9QU6aa1RvGepnirbKmaxaiNMBZNBCO9qeLHjd9Eob0qx7VsN0qJ68HapBGbSXoR0t157v/5q02/NyMM6JRoqlom/wh4hciXksXsR+2BHYoH83rFNele5vrV+PZfcfhCW/OwULMboG2Fbscw6E9n0H5jB4yy2F7qkAa5MtoUXeBB6i1G/+xebu34p9oi8pBD+8/VXYBXQ18G/AMZjnaATwK+xGkJbDsekDe2Hn8Syc9805AMZjN5V9MdF+AIvcuCzDMVIjIt/A9PIUlt3yOExXp2HfA40zJtA94H+TynCioOzBjGFRi7C3/yNiyrbBHC5RoxOsr/BqqupQTONxZf2BrTGDGUzHCPr6OOAl7Lx8HruId6J7LlTUm1cR/dIoOUW/BO18QCq/FRz0y0ypkZNBVS/AjP9/hM9Vq3BvWnYEZmAPWCdjD0jTse/ZpvWc3oY5/rYADsVuFrtjTq4jgP0ieo/TXK56c7+vFJrLQD3RVrlQkBZ6le1pIr1NN1BeG9JOukmit+nJ26HyUIT2fP8n0KbPyyOAp6XOXHLhN/+IyHTM+EQZGtnueswBkBoR+SHxHo8lVM4PWYK99R0cWT+Y2pnps9YPPr/wD7IxCxuAjwNuE5GFWKKGWFy4yKQ0OxaRriplTxNJjhgpv5vuT+RF6RPZdmF0XZby0Ha/AH5Ra7tInYuAixLKvgp8Ncv+XL3Vqno73aFI4bIHifktSetd2aIqZSswgxlXVvHb1ObwbIh5RaPERb80SsPRL9X6oZpGw6jqhcAhwHgRqctDmQci8gLdIWdpth8Ws+52MkSixGmrGXpz5WXQXFrqibbKjSxaiNOBW59aC73Q9jSLunUjNvdzRiMH9zakB+2kmyS8Haqy3pV5O9QcWq493/9Vacfn5dF05w/JTL/amzSd+UQGxWKfg3sQywh/XajoU8Qns+tBxvrbAC+JSLTTa3E99jm6g2mgAzy580tCYZOtxEWBDIp69lR1D0wrLxAfgbE2lUanlWSOfkmDqk7HwhLHi8jjee7bY7Sp5pqit95M3jpQ1bOJD2UPM16yZ85vBK+bJlCndppuP1qgQa+nnOkUO+S1Vx959n9J70FQgudlVe2LfZ7xjHqPV4bB/y3A91V1UCTU6QJgpqrej80D+RrmaUkbvp62/ljXhkw4Z0HNkH9Py5mF6WkH59FsJaOAB1T1FswjuCY2vWUMFllygIgsj6kXF/3SKK2IfklEVS/GpqgcCLyuqkNc0fKEc+CpjzJpLi25682Tuw6mUZl4LkrNT+7mjNdNc6hHO62wH9Norga9nvKnU+xQs4/bqdrLs/+n0bq+b7fn5U8Cr9DOb/5F5GE3QD+EUAIFEblWVQdhc8c3xrI47iPdeQdQ+3bv5cBwF6JOxvoDsKR9PT6z4GlfRGSFqn4HmEx8dtJmshJLOLILpqn3gOeA84ALqkSXVES/NEqLol+qEeSxmBtZrzTgrfRUUBrNZaAZeuvt5KoDEVmCPRCVCa+b5lCPdppuP1qgQa+n/OkIO+S1Vze59X8r+74Nn5cnA2dLz8T6meizenXx+TVcRsXpwKgaCQ6i9RT4NJZk8N1a28fUn4J5otJ8ss/TJriQmLlYwozXam1fNKr6MSwhykbh6BdVXZfu5IV3A9/DMo2+JiI1PZ5qny6ZiRmWIPrlSOCjESfYDGCViByZx+/xlJ84zeWgt1T1vd7KQ5LtybgPr5teRrPuWTm1zeupzSiDHcqLNO3w2utJGfq/XZ6XVXVT4EpgD2kgQWLf2ps0HxG5GXvrv1nGqvsAU+oZ+Dvewb4+4OkgnDfsq1jUR+kRSwYZRL+E2RHziM7H5hep+/vMYANVnaSqq9W+6xvd77XYFzxOwwzrGJKjX34Sre/pXBI015DeUtb3eisRVWxPFrxuehnNumflhNdTm1ESO5QXVdvhtVdJGfq/jZ6XzwW+3MjAH0ry5t/jaQaqOhZYJiILim5LLXz0i6fV1KM5r7fOo17bk/EYXjcdRlH3rDzweiof7WCHcmqD114M7dD/RfedG9OskUeSw1K8+fd4moGI3An8ueh2pMFHv3haTZ2a83rrMBqwPVnwuukwCrxn5YHXU8loEzuUB157MbRJ/xfdd3flMfAH/+bf4/F4PB6Px+PxeDyejuf/AVp5o7p/vRzVAAAAAElFTkSuQmCC",
"text/latex": [
"$\\displaystyle {dst}_{(0,0)} \\leftarrow \\left({img}_{(1,0)}^{2} w_{2} - 0.5 {img}_{(1,1)}^{2} - 0.5 {img}_{(-1,1)}^{2} + 0.5 {img}_{(1,-1)}^{2} - 0.5 {img}_{(-1,-1)}^{2} - {img}_{(-1,0)}^{2} w_{2}\\right)^{2}$"
],
"text/plain": [
- " \n",
- "dst_C := (img_E__2â‹…wâ‚‚ - 0.5â‹…img_NE__2 - 0.5â‹…img_NW__2 + 0.5â‹…img_SE__2 - 0.5â‹…im\n",
- "\n",
- " 2\n",
- "g_SW__2 - img_W__2â‹…wâ‚‚) "
+ " 2\n",
+ "dst_C := (img_E__2â‹…wâ‚‚ - 0.5â‹…img_NE__2 - 0.5â‹…img_NW__2 + 0.5â‹…img_SE__2 - 0.5â‹…img_SW__2 - img_W__2â‹…wâ‚‚) "
]
},
"execution_count": 28,
@@ -763,7 +910,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -794,7 +941,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "graphviz is not installed. Visualizing the AST is not available\n"
- ]
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- "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_2</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
- "<span class=\"p\">{</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">_stride_img_2</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">])</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]);</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
- "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_2</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"p\">)</span>\n",
+ "<span class=\"p\">{</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">_stride_img_2</span><span class=\"p\">;</span>\n",
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+ "<span class=\"w\"> </span><span class=\"p\">{</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">])</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]);</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span>\n",
+ "<span class=\"p\">}</span>\n",
"</pre></div>\n"
],
"text/plain": [
@@ -1077,24 +1368,24 @@
{
"data": {
"text/html": [
- "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_2</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
- "<span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_2</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"p\">)</span>\n",
+ "<span class=\"p\">{</span>\n",
"<span class=\"w\"> </span><span class=\"cp\">#pragma omp parallel num_threads(2)</span>\n",
- "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">_stride_img_2</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">_stride_img_2</span><span class=\"p\">;</span>\n",
"<span class=\"w\"> </span><span class=\"cp\">#pragma omp for schedule(static)</span>\n",
- "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">])</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span 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"</pre></div>\n"
],
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- "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
- "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">)</span>\n",
+ "<span class=\"p\">{</span>\n",
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+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">202</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">601</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_21_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I_21</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_21_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I_21</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">600</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">];</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span>\n",
+ "<span class=\"p\">}</span>\n",
"</pre></div>\n"
],
"text/plain": [
@@ -1292,7 +1583,7 @@
"source": [
"### Running on GPU\n",
"\n",
- "If you have a CUDA enabled graphics card and [pycuda](https://mathema.tician.de/software/pycuda/) installed, *pystencils* can run your kernel on the GPU as well. You can find more details about this in the GPU tutorial."
+ "If you have a GPU and [cupy](https://cupy.dev/) installed, *pystencils* can run your kernel on the GPU as well. You can find more details about this in the GPU tutorial."
]
},
{
@@ -1388,19 +1679,19 @@
{
"data": {
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- "<span class=\"p\">{</span><span class=\"w\"></span>\n",
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- "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_10</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_11_21</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">5</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_1m1_21</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">3</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_10_21</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"n\">_data_dst_10</span><span class=\"p\">[</span><span class=\"mi\">601</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_11_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_11_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_1m1_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_10_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_10_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_I_1m1_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"nf\">__launch_bounds__</span><span class=\"p\">(</span><span class=\"mi\">256</span><span class=\"p\">)</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">)</span>\n",
+ "<span class=\"p\">{</span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">if</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">202</span><span class=\"w\"> </span><span class=\"o\">&&</span><span class=\"w\"> </span><span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">600</span><span class=\"p\">)</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_10</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_11_21</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">5</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_1m1_21</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">3</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_10_21</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_10</span><span class=\"p\">[</span><span class=\"mi\">601</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_11_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_11_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_1m1_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_10_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_10_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_I_1m1_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2404</span><span class=\"p\">];</span>\n",
"<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"> </span>\n",
- "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">}</span>\n",
"</pre></div>\n"
],
"text/plain": [
@@ -1425,8 +1716,8 @@
],
"source": [
"try:\n",
- " import pycuda\n",
- " from pystencils.gpucuda import BlockIndexing\n",
+ " import cupy\n",
+ " from pystencils.gpu import BlockIndexing\n",
"\n",
" gpu_ast = create_kernel(update_rule, target=ps.Target.GPU,\n",
" gpu_indexing=BlockIndexing,\n",
@@ -1434,7 +1725,7 @@
"\n",
" ps.show_code(gpu_ast)\n",
"except ImportError:\n",
- " print(\"Please install pycuda for GPU support\")"
+ " print(\"Please install cupy for GPU support\")"
]
}
],
@@ -1455,7 +1746,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.6"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
diff --git a/doc/notebooks/03_tutorial_datahandling.ipynb b/doc/notebooks/03_tutorial_datahandling.ipynb
index 3b406b3ec756fb0cd717fd8f89f9645686ebb5c3..05b489133ba87e8a74091ef1121ac456498f0e5e 100644
--- a/doc/notebooks/03_tutorial_datahandling.ipynb
+++ b/doc/notebooks/03_tutorial_datahandling.ipynb
@@ -21,7 +21,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Test to see if pycuda is installed which is needed to run calculations on the GPU"
+ "Test to see if cupy is installed which is needed to run calculations on the GPU"
]
},
{
@@ -471,7 +471,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -505,7 +505,7 @@
"\n",
"- symbolic pystencils field\n",
"- numpy array on CPU \n",
- "- for GPU run also a pycuda.gpuarray to mirror the data on the GPU\n",
+ "- for GPU use also cupy to mirror the data on the GPU\n",
"\n",
"Managing these three objects manually is tedious and error-prone. We'll see in the next section how the data handling object takes care of this problem."
]
@@ -523,7 +523,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -553,7 +553,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -570,7 +570,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -586,7 +586,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -604,7 +604,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -623,7 +623,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -644,7 +644,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -675,7 +675,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -697,7 +697,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -724,7 +724,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -752,7 +752,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -806,19 +806,68 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
- "ename": "ValueError",
- "evalue": "Cannot create parallel data handling because walberla module is not available",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dh \u001b[38;5;241m=\u001b[39m \u001b[43mps\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_data_handling\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdomain_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m field \u001b[38;5;241m=\u001b[39m dh\u001b[38;5;241m.\u001b[39madd_array(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfield\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m block \u001b[38;5;129;01min\u001b[39;00m dh\u001b[38;5;241m.\u001b[39miterate():\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# offset is in global coordinates, where first inner cell has coordiante (0,0) \u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# and ghost layers have negative coordinates\u001b[39;00m\n",
- "File \u001b[0;32m~/pystencils/pystencils/pystencils/datahandling/__init__.py:46\u001b[0m, in \u001b[0;36mcreate_data_handling\u001b[0;34m(domain_size, periodicity, default_layout, default_target, parallel, default_ghost_layers)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m parallel:\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wlb \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot create parallel data handling because walberla module is not available\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m periodicity \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m periodicity \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 49\u001b[0m periodicity \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m)\n",
- "\u001b[0;31mValueError\u001b[0m: Cannot create parallel data handling because walberla module is not available"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(-1, -1) (32, 32)\n",
+ "[0][PROGRESS]------(0.000 sec) Initializing SetupBlockForest:\n",
+ "[0] - AABB: [ <0,0,0>, <30,30,1> ]\n",
+ "[0] - forest size (root blocks / blocks on the initial grid): 1 x 1 x 1\n",
+ "[0] - periodicity: false x false x false\n",
+ "[0][PROGRESS]------(0.000 sec) Initializing SetupBlockForest: Allocating root blocks ...\n",
+ "[0][PROGRESS]------(0.000 sec) Initializing SetupBlockForest: Setting up neighborhood information for each block (with respect to periodicity) ...\n",
+ "[0][PROGRESS]------(0.000 sec) Initializing SetupBlockForest: Assigning workload, memory requirements, and SUIDs to blocks ...\n",
+ "[0][PROGRESS]------(0.000 sec) Initializing SetupBlockForest: finished!\n",
+ "[0] The following block structure has been created:\n",
+ "[0] - AABB: [ <0,0,0>, <30,30,1> ]\n",
+ "[0] - initial decomposition: 1 x 1 x 1 (= forest size)\n",
+ "[0] - periodicity: false x false x false\n",
+ "[0] - number of blocks discarded from the initial grid: 0 (= 0 %)\n",
+ "[0] - number of levels: 1\n",
+ "[0] - tree ID digits: 1 (-> block ID bytes = 1)\n",
+ "[0] - total number of blocks: 1\n",
+ "[0] - blocks have not yet been distributed to processes\n",
+ "[0][PROGRESS]------(0.000 sec) Balancing SetupBlockForest: Creating a process distribution for 1 process(es) ...\n",
+ "[0][PROGRESS]------(0.000 sec) Balancing SetupBlockForest: process distribution to 1 process(es) finished!\n",
+ "[0] - number of worker processes: 1\n",
+ "[0] - number of empty buffer processes: 0\n",
+ "[0] - buffer processes are inserted into the process network: no\n",
+ "[0] The resulting block structure looks like as follows:\n",
+ "[0] - AABB: [ <0,0,0>, <30,30,1> ]\n",
+ "[0] - initial decomposition: 1 x 1 x 1 (= forest size)\n",
+ "[0] - periodicity: false x false x false\n",
+ "[0] - number of blocks discarded from the initial grid: 0 (= 0 %)\n",
+ "[0] - number of levels: 1\n",
+ "[0] - tree ID digits: 1 (-> block ID bytes = 1)\n",
+ "[0] - total number of blocks: 1\n",
+ "[0] - number of processes: 1 (1 worker process(es) / 0 empty buffer process(es))\n",
+ "[0] - buffer processes are inserted into the process network: no\n",
+ "[0] - process ID bytes: 0\n",
+ "[0] - blocks/memory/workload per process:\n",
+ "[0] + blocks:\n",
+ "[0] - min = 1\n",
+ "[0] - max = 1\n",
+ "[0] - avg = 1\n",
+ "[0] - stdDev = 0\n",
+ "[0] - relStdDev = 0\n",
+ "[0] + memory:\n",
+ "[0] - min = 1\n",
+ "[0] - max = 1\n",
+ "[0] - avg = 1\n",
+ "[0] - stdDev = 0\n",
+ "[0] - relStdDev = 0\n",
+ "[0] + workload:\n",
+ "[0] - min = 1\n",
+ "[0] - max = 1\n",
+ "[0] - avg = 1\n",
+ "[0] - stdDev = 0\n",
+ "[0] - relStdDev = 0\n",
+ "[0][PROGRESS]------(0.000 sec) Adding block data (\"cell bounding box\")\n",
+ "[0][PROGRESS]------(0.000 sec) Adding block data (\"field\")\n"
]
}
],
@@ -843,9 +892,74 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(-1, -1, -1)[0][PROGRESS]------(0.025 sec) Initializing SetupBlockForest:\n",
+ "[0] - AABB: [ <0,0,0>, <40,10,40> ]\n",
+ "[0] - forest size (root blocks / blocks on the initial grid): 2 x 1 x 2\n",
+ "[0] - periodicity: true x false x false\n",
+ " (22, 12, 22)\n",
+ "(19, -1, -1) (22, 12, 22)\n",
+ "(-1, -1, 19) (22, 12, 22)\n",
+ "(19, -1, 19) (22, 12, 22)\n",
+ "[0][PROGRESS]------(0.025 sec) Initializing SetupBlockForest: Allocating root blocks ...\n",
+ "[0][PROGRESS]------(0.025 sec) Initializing SetupBlockForest: Setting up neighborhood information for each block (with respect to periodicity) ...\n",
+ "[0][PROGRESS]------(0.025 sec) Initializing SetupBlockForest: Assigning workload, memory requirements, and SUIDs to blocks ...\n",
+ "[0][PROGRESS]------(0.025 sec) Initializing SetupBlockForest: finished!\n",
+ "[0] The following block structure has been created:\n",
+ "[0] - AABB: [ <0,0,0>, <40,10,40> ]\n",
+ "[0] - initial decomposition: 2 x 1 x 2 (= forest size)\n",
+ "[0] - periodicity: true x false x false\n",
+ "[0] - number of blocks discarded from the initial grid: 0 (= 0 %)\n",
+ "[0] - number of levels: 1\n",
+ "[0] - tree ID digits: 3 (-> block ID bytes = 1)\n",
+ "[0] - total number of blocks: 4\n",
+ "[0] - blocks have not yet been distributed to processes\n",
+ "[0][PROGRESS]------(0.025 sec) Balancing SetupBlockForest: Creating a process distribution for 1 process(es) ...\n",
+ "[0][PROGRESS]------(0.025 sec) Balancing SetupBlockForest: process distribution to 1 process(es) finished!\n",
+ "[0] - number of worker processes: 1\n",
+ "[0] - number of empty buffer processes: 0\n",
+ "[0] - buffer processes are inserted into the process network: no\n",
+ "[0] The resulting block structure looks like as follows:\n",
+ "[0] - AABB: [ <0,0,0>, <40,10,40> ]\n",
+ "[0] - initial decomposition: 2 x 1 x 2 (= forest size)\n",
+ "[0] - periodicity: true x false x false\n",
+ "[0] - number of blocks discarded from the initial grid: 0 (= 0 %)\n",
+ "[0] - number of levels: 1\n",
+ "[0] - tree ID digits: 3 (-> block ID bytes = 1)\n",
+ "[0] - total number of blocks: 4\n",
+ "[0] - number of processes: 1 (1 worker process(es) / 0 empty buffer process(es))\n",
+ "[0] - buffer processes are inserted into the process network: no\n",
+ "[0] - process ID bytes: 0\n",
+ "[0] - blocks/memory/workload per process:\n",
+ "[0] + blocks:\n",
+ "[0] - min = 4\n",
+ "[0] - max = 4\n",
+ "[0] - avg = 4\n",
+ "[0] - stdDev = 0\n",
+ "[0] - relStdDev = 0\n",
+ "[0] + memory:\n",
+ "[0] - min = 4\n",
+ "[0] - max = 4\n",
+ "[0] - avg = 4\n",
+ "[0] - stdDev = 0\n",
+ "[0] - relStdDev = 0\n",
+ "[0] + workload:\n",
+ "[0] - min = 4\n",
+ "[0] - max = 4\n",
+ "[0] - avg = 4\n",
+ "[0] - stdDev = 0\n",
+ "[0] - relStdDev = 0\n",
+ "[0][PROGRESS]------(0.025 sec) Adding block data (\"cell bounding box\")\n",
+ "[0][PROGRESS]------(0.026 sec) Adding block data (\"field\")\n"
+ ]
+ }
+ ],
"source": [
"from waLBerla import createUniformBlockGrid\n",
"from pystencils.datahandling import ParallelDataHandling\n",
@@ -868,9 +982,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAH4AAAAUCAYAAABLTIm0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAABJ0AAASdAHeZh94AAAEGUlEQVR4nO2aW4hVVRjHf2M+aGYzYITQBdIc8UGaytAIjcys3gYUfDFNUSSrsUgiL/nvE7o8pKLdsXAshAoGDerFF0E0Lxn2pnmnCKGyMu+UTg9rrZk9e/Y+5+x9Zs526PzhsNjf3mut3/7W9Vv7NHR2dlLX/0+DigaoqxgNTjKa2WbgSeAuSRdqi1RXX8nM7gcOAAslfRy91xCf6s3sAWAfsFTS2hKFzgY+85e9Co48dzuwGngCGAGcBrYBJunPPC9USmY2E3gYaAHuAYYDWyTNLpOvppwpDGV9mpXTzLYCk4Axks4He9JU/zrwN/BBCcA7gHeB82nP+OdGA98D84D9wDrgBLAE2GNmI0rlz6mVwHO4hv+lkgwFccYZyvo0J+ebwEigLWrs0fBm1gxMA76UdCml8gZgE3AG+LDM+7wP3Aq0SWqV9IqkqR54LK6T9bVeBJqBm4FnKsxTBGeXMvg0M6ek/cBhYJGZdbV3fMTPBxqAL0pU3gZMxfW61PXf987pwCngvTiPz/uUmQ0rUVdmSdoh6aikisKVojhjKuvTKjk/B+4EHguGeMNPA64Ce1MqHwe8BayXtLP0u/CIT7dLutaDUjoH7AZuxK0/RapQzgw+rYZzt097N7zvKS3AoaSdvJkNxm08fgKWl3kfcFMPwJGU+0d92lxBWf2pwjgz+rQazu98OiUYoiP+NuAG3C4xSauAe4Gn09b/mBp9ejblfrA3VVBWf6pIziw+zc0p6SxwGTfdAz0bPuwIk0KCibgeuUbSnjKAdVWgAnz6B3BLuIg2fOhxQ6JP++noU9wU82qGikIPbEy5H+x/ZSizP1Rzzpw+rZZzKN1t3OPk7lefxmPBm+heNy6bWVKhG81sI26D8oK3/ejTtLVxjE/T1qxaqQjOPD7NzenDuCbgZLBFR/xp4De6NxFBV4BPUn4H/TO7/HV0ytrh0+nR+NGDDAceAi6SEkHUUEVw5vFpNZxjcWH6D8HQNeIldZrZTmCGmd0t6Zi3XwIWJNGb2Wu4zcnm+PGipONmth0Xez4LvBPNCgwDPopHEGbWDswF5klqT6q3L1UEZx6f5uX0CiFe6Dy9PtJ0ADOAx4FjWV4mRYuBb4ENZvYocAiYiItJjwArEvKE3vxvngrNrBVo9ZcjffqgbyiA3yUtLZozp/JwgussV4GvgiF+gNOBW+vn9AWlpOPABKDdA74EjAbWA5MknUnINh44B3yTs9oW3Eici+vAAKMitpnXCWdm5eE0s0bcQPha0s/BnvR1bhnwBnCfpIPUUGbWhDuvXiPp5VrWnUUDhRPAzJ4HNgCTJe0K9qSvc+twJ0mra8QW1WTgHyD1c/B1ogHBaWZDgWVAR7TRIWHE+wxTcOvG2/U/Ygxc+e8As4B2Saei9/4DBmP8sRNok9wAAAAASUVORK5CYII=",
+ "text/latex": [
+ "$\\displaystyle \\left( 40, \\ 10, \\ 40\\right)$"
+ ],
+ "text/plain": [
+ "(40, 10, 40)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"dh.gather_array('field').shape"
]
@@ -886,9 +1015,71 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0][PROGRESS]------(0.119 sec) Initializing SetupBlockForest:\n",
+ "[0] - AABB: [ <0,0,0>, <40,40,1> ]\n",
+ "[0] - forest size (root blocks / blocks on the initial grid): 2 x 4 x 1\n",
+ "[0] - periodicity: true x true x false\n",
+ "[0][PROGRESS]------(0.119 sec) Initializing SetupBlockForest: Allocating root blocks ...\n",
+ "[0][PROGRESS]------(0.119 sec) Initializing SetupBlockForest: Setting up neighborhood information for each block (with respect to periodicity) ...\n",
+ "[0][PROGRESS]------(0.119 sec) Initializing SetupBlockForest: Assigning workload, memory requirements, and SUIDs to blocks ...\n",
+ "[0][PROGRESS]------(0.119 sec) Initializing SetupBlockForest: finished!\n",
+ "[0] The following block structure has been created:\n",
+ "[0] - AABB: [ <0,0,0>, <40,40,1> ]\n",
+ "[0] - initial decomposition: 2 x 4 x 1 (= forest size)\n",
+ "[0] - periodicity: true x true x false\n",
+ "[0] - number of blocks discarded from the initial grid: 0 (= 0 %)\n",
+ "[0] - number of levels: 1\n",
+ "[0] - tree ID digits: 4 (-> block ID bytes = 1)\n",
+ "[0] - total number of blocks: 8\n",
+ "[0] - blocks have not yet been distributed to processes\n",
+ "[0][PROGRESS]------(0.119 sec) Balancing SetupBlockForest: Creating a process distribution for 1 process(es) ...\n",
+ "[0][PROGRESS]------(0.119 sec) Balancing SetupBlockForest: process distribution to 1 process(es) finished!\n",
+ "[0] - number of worker processes: 1\n",
+ "[0] - number of empty buffer processes: 0\n",
+ "[0] - buffer processes are inserted into the process network: no\n",
+ "[0] The resulting block structure looks like as follows:\n",
+ "[0] - AABB: [ <0,0,0>, <40,40,1> ]\n",
+ "[0] - initial decomposition: 2 x 4 x 1 (= forest size)\n",
+ "[0] - periodicity: true x true x false\n",
+ "[0] - number of blocks discarded from the initial grid: 0 (= 0 %)\n",
+ "[0] - number of levels: 1\n",
+ "[0] - tree ID digits: 4 (-> block ID bytes = 1)\n",
+ "[0] - total number of blocks: 8\n",
+ "[0] - number of processes: 1 (1 worker process(es) / 0 empty buffer process(es))\n",
+ "[0] - buffer processes are inserted into the process network: no\n",
+ "[0] - process ID bytes: 0\n",
+ "[0] - blocks/memory/workload per process:\n",
+ "[0] + blocks:\n",
+ "[0] - min = 8\n",
+ "[0] - max = 8\n",
+ "[0] - avg = 8\n",
+ "[0] - stdDev = 0\n",
+ "[0] - relStdDev = 0\n",
+ "[0] + memory:\n",
+ "[0] - min = 8\n",
+ "[0] - max = 8\n",
+ "[0] - avg = 8\n",
+ "[0] - stdDev = 0\n",
+ "[0] - relStdDev = 0\n",
+ "[0] + workload:\n",
+ "[0] - min = 8\n",
+ "[0] - max = 8\n",
+ "[0] - avg = 8\n",
+ "[0] - stdDev = 0\n",
+ "[0] - relStdDev = 0\n",
+ "[0][PROGRESS]------(0.119 sec) Adding block data (\"cell bounding box\")\n",
+ "[0][PROGRESS]------(0.119 sec) Adding block data (\"src\")\n",
+ "[0][PROGRESS]------(0.120 sec) Adding block data (\"dst\")\n"
+ ]
+ }
+ ],
"source": [
"blocks = createUniformBlockGrid(blocks=(2,4,1), cellsPerBlock=(20, 10, 1), \n",
" oneBlockPerProcess=False, periodic=(1, 1, 0))\n",
@@ -913,9 +1104,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"dh.fill('src', 0.0)\n",
"for block in dh.iterate(ghost_layers=False, inner_ghost_layers=False):\n",
@@ -934,7 +1136,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -947,9 +1149,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.scalar_field( dh.gather_array('src') );"
]
diff --git a/pystencils/datahandling/blockiteration.py b/pystencils/datahandling/blockiteration.py
index bd981dc318cd855c496b70eef5e6bb847c24f2b5..24c4282126912e712769f3cbc74036d790bada21 100644
--- a/pystencils/datahandling/blockiteration.py
+++ b/pystencils/datahandling/blockiteration.py
@@ -115,7 +115,7 @@ class ParallelBlock(Block):
result = wlb.field.toArray(result, with_ghost_layers=self._gls)
result = self._normalize_array_shape(result)
elif 'GpuField' in type_name:
- result = wlb.cuda.toGpuArray(result, with_ghost_layers=self._gls)
+ result = wlb.gpu.toGpuArray(result, with_ghost_layers=self._gls)
result = self._normalize_array_shape(result)
return result
diff --git a/pystencils/datahandling/parallel_datahandling.py b/pystencils/datahandling/parallel_datahandling.py
index 9d1e898d7368c22faf6c1699a619587cb1c613a1..cf9a68b58e5961854ede40111c4bc8d9471ad74c 100644
--- a/pystencils/datahandling/parallel_datahandling.py
+++ b/pystencils/datahandling/parallel_datahandling.py
@@ -151,8 +151,8 @@ class ParallelDataHandling(DataHandling):
if gpu:
if alignment != 0:
raise ValueError("Alignment for walberla GPU fields not yet supported")
- wlb.cuda.addGpuFieldToStorage(self.blocks, self.GPU_DATA_PREFIX + name, dtype, fSize=values_per_cell,
- usePitchedMem=False, ghostLayers=ghost_layers, layout=layout_map[layout])
+ wlb.gpu.addGpuFieldToStorage(self.blocks, self.GPU_DATA_PREFIX + name, dtype, fSize=values_per_cell,
+ usePitchedMem=False, ghostLayers=ghost_layers, layout=layout_map[layout])
if cpu and gpu:
self._cpu_gpu_pairs.append((name, self.GPU_DATA_PREFIX + name))
@@ -255,7 +255,7 @@ class ParallelDataHandling(DataHandling):
def get_kernel_kwargs(self, kernel_function, **kwargs):
if kernel_function.ast.backend == Backend.CUDA:
name_map = self._field_name_to_gpu_data_name
- to_array = wlb.cuda.toGpuArray
+ to_array = wlb.gpu.toGpuArray
else:
name_map = self._field_name_to_cpu_data_name
to_array = wlb.field.toArray
@@ -280,7 +280,7 @@ class ParallelDataHandling(DataHandling):
for block in self.blocks:
transfer_func(block[self.GPU_DATA_PREFIX + name], block[name])
else:
- wlb.cuda.copyFieldToCpu(self.blocks, self.GPU_DATA_PREFIX + name, name)
+ wlb.gpu.copyFieldToCpu(self.blocks, self.GPU_DATA_PREFIX + name, name)
def to_gpu(self, name):
if name in self._custom_data_transfer_functions:
@@ -288,20 +288,20 @@ class ParallelDataHandling(DataHandling):
for block in self.blocks:
transfer_func(block[self.GPU_DATA_PREFIX + name], block[name])
else:
- wlb.cuda.copyFieldToGpu(self.blocks, self.GPU_DATA_PREFIX + name, name)
+ wlb.gpu.copyFieldToGpu(self.blocks, self.GPU_DATA_PREFIX + name, name)
def is_on_gpu(self, name):
return (name, self.GPU_DATA_PREFIX + name) in self._cpu_gpu_pairs
def all_to_cpu(self):
for cpu_name, gpu_name in self._cpu_gpu_pairs:
- wlb.cuda.copyFieldToCpu(self.blocks, gpu_name, cpu_name)
+ wlb.gpu.copyFieldToCpu(self.blocks, gpu_name, cpu_name)
for name in self._custom_data_transfer_functions.keys():
self.to_cpu(name)
def all_to_gpu(self):
for cpu_name, gpu_name in self._cpu_gpu_pairs:
- wlb.cuda.copyFieldToGpu(self.blocks, gpu_name, cpu_name)
+ wlb.gpu.copyFieldToGpu(self.blocks, gpu_name, cpu_name)
for name in self._custom_data_transfer_functions.keys():
self.to_gpu(name)
@@ -328,7 +328,7 @@ class ParallelDataHandling(DataHandling):
create_packing = wlb.field.createStencilRestrictedPackInfo
else:
assert target == Target.GPU
- create_packing = wlb.cuda.createPackInfo if buffered else wlb.cuda.createMPIDatatypeInfo
+ create_packing = wlb.gpu.createPackInfo if buffered else wlb.gpu.createMPIDatatypeInfo
names = [self.GPU_DATA_PREFIX + name for name in names]
sync_function = create_scheme(self.blocks, stencil)
diff --git a/pystencils/datahandling/serial_datahandling.py b/pystencils/datahandling/serial_datahandling.py
index 77614fd09422470783aae59d619274d493334bfa..7cc53725ab6e94ac91c03c7c2c3a3c5a08430d21 100644
--- a/pystencils/datahandling/serial_datahandling.py
+++ b/pystencils/datahandling/serial_datahandling.py
@@ -126,7 +126,7 @@ class SerialDataHandling(DataHandling):
else:
layout_tuple = spatial_layout_string_to_tuple(layout, self.dim)
- # cpu_arr is always created - since there is no create_pycuda_array_with_layout()
+ # cpu_arr is always created - since there is no create_gpu_array_with_layout()
byte_offset = ghost_layers * np.dtype(dtype).itemsize
cpu_arr = create_numpy_array_with_layout(layout=layout_tuple, alignment=alignment,
byte_offset=byte_offset, **kwargs)
diff --git a/pystencils/gpu/cudajit.py b/pystencils/gpu/cudajit.py
index 9123085cfeac15eb737afffca5f5d733b2457eb1..50a048b8e87094dade5e55bcdb73d87649bdc646 100644
--- a/pystencils/gpu/cudajit.py
+++ b/pystencils/gpu/cudajit.py
@@ -91,7 +91,10 @@ def make_python_function(kernel_function_node, argument_dict=None, custom_backen
cache_values.append(kwargs) # keep objects alive such that ids remain unique
with cp.cuda.Device(pystencils.GPU_DEVICE):
func(block_and_thread_numbers['grid'], block_and_thread_numbers['block'], args)
- # import pycuda.driver as cuda
+ # useful for debugging:
+ # with cp.cuda.Device(pystencils.GPU_DEVICE):
+ # cp.cuda.runtime.deviceSynchronize()
+
# cuda.Context.synchronize() # useful for debugging, to get errors right after kernel was called
ast = kernel_function_node
parameters = kernel_function_node.get_parameters()
diff --git a/pystencils/gpu/indexing.py b/pystencils/gpu/indexing.py
index 02ff969a7f9aada02ef1a151bbe25ea39697b743..ac77102c45d83e53d471e19459360a86f200ab3c 100644
--- a/pystencils/gpu/indexing.py
+++ b/pystencils/gpu/indexing.py
@@ -34,7 +34,7 @@ GRID_DIM = [ThreadIndexingSymbol("gridDim." + coord, create_type("int32")) for c
class AbstractIndexing(abc.ABC):
"""
Abstract base class for all Indexing classes. An Indexing class defines how a multidimensional
- field is mapped to CUDA's block and grid system. It calculates indices based on CUDA's thread and block indices
+ field is mapped to GPU's block and grid system. It calculates indices based on GPU's thread and block indices
and computes the number of blocks and threads a kernel is started with. The Indexing class is created with
a pystencils field, a slice to iterate over, and further optional parameters that must have default values.
"""
@@ -42,12 +42,12 @@ class AbstractIndexing(abc.ABC):
@property
@abc.abstractmethod
def coordinates(self):
- """Returns a sequence of coordinate expressions for (x,y,z) depending on symbolic CUDA block and thread indices.
+ """Returns a sequence of coordinate expressions for (x,y,z) depending on symbolic GPU block and thread indices.
These symbolic indices can be obtained with the method `index_variables` """
@property
def index_variables(self):
- """Sympy symbols for CUDA's block and thread indices, and block and grid dimensions. """
+ """Sympy symbols for GPU's block and thread indices, and block and grid dimensions. """
return BLOCK_IDX + THREAD_IDX + BLOCK_DIM + GRID_DIM
@abc.abstractmethod
@@ -89,14 +89,14 @@ class AbstractIndexing(abc.ABC):
class BlockIndexing(AbstractIndexing):
- """Generic indexing scheme that maps sub-blocks of an array to CUDA blocks.
+ """Generic indexing scheme that maps sub-blocks of an array to GPU blocks.
Args:
field: pystencils field (common to all Indexing classes)
iteration_slice: slice that defines rectangular subarea which is iterated over
permute_block_size_dependent_on_layout: if True the block_size is permuted such that the fastest coordinate
gets the largest amount of threads
- compile_time_block_size: compile in concrete block size, otherwise the cuda variable 'blockDim' is used
+ compile_time_block_size: compile in concrete block size, otherwise the gpu variable 'blockDim' is used
"""
def __init__(self, field, iteration_slice,
@@ -182,7 +182,7 @@ class BlockIndexing(AbstractIndexing):
def limit_block_size_by_register_restriction(block_size, required_registers_per_thread, device=None):
"""Shrinks the block_size if there are too many registers used per multiprocessor.
This is not done automatically, since the required_registers_per_thread are not known before compilation.
- They can be obtained by ``func.num_regs`` from a pycuda function.
+ They can be obtained by ``func.num_regs`` from a cupy function.
:returns smaller block_size if too many registers are used.
"""
import cupy as cp
@@ -232,10 +232,10 @@ class BlockIndexing(AbstractIndexing):
class LineIndexing(AbstractIndexing):
"""
- Indexing scheme that assigns the innermost 'line' i.e. the elements which are adjacent in memory to a 1D CUDA block.
+ Indexing scheme that assigns the innermost 'line' i.e. the elements which are adjacent in memory to a 1D GPU block.
The fastest coordinate is indexed with thread_idx.x, the remaining coordinates are mapped to block_idx.{x,y,z}
This indexing scheme supports up to 4 spatial dimensions, where the innermost dimensions is not larger than the
- maximum amount of threads allowed in a CUDA block (which depends on device).
+ maximum amount of threads allowed in a GPU block (which depends on device).
"""
def __init__(self, field, iteration_slice):
diff --git a/pystencils_tests/test_datahandling_parallel.py b/pystencils_tests/test_datahandling_parallel.py
index dd18e4fb18a78ad96d3685e4400799852e2fa9d9..b5aa9832470b8e19d712f73697ab3af4549eaf7e 100644
--- a/pystencils_tests/test_datahandling_parallel.py
+++ b/pystencils_tests/test_datahandling_parallel.py
@@ -33,12 +33,12 @@ def test_access_and_gather():
dh = ParallelDataHandling(blocks, default_ghost_layers=2)
access_and_gather(dh, cells)
synchronization(dh, test_gpu=False)
- if hasattr(wlb, 'cuda'):
+ if hasattr(wlb, 'gpu'):
synchronization(dh, test_gpu=True)
def test_gpu():
- pytest.importorskip('waLBerla.cuda')
+ pytest.importorskip('waLBerla.gpu')
block_size = (4, 7, 1)
num_blocks = (3, 2, 1)
@@ -59,7 +59,7 @@ def test_gpu():
@pytest.mark.parametrize('target', (pystencils.Target.CPU, pystencils.Target.GPU))
def test_kernel(target):
if target == pystencils.Target.GPU:
- pytest.importorskip('waLBerla.cuda')
+ pytest.importorskip('waLBerla.gpu')
# 3D
blocks = wlb.createUniformBlockGrid(blocks=(3, 2, 4), cellsPerBlock=(3, 2, 5), oneBlockPerProcess=False)
@@ -108,7 +108,7 @@ def test_block_iteration():
def test_getter_setter():
- pytest.importorskip('waLBerla.cuda')
+ pytest.importorskip('waLBerla.gpu')
block_size = (2, 2, 2)
num_blocks = (2, 2, 2)
@@ -131,7 +131,7 @@ def test_getter_setter():
def test_parallel_datahandling_boundary_conditions():
- pytest.importorskip('waLBerla.cuda')
+ pytest.importorskip('waLBerla.gpu')
dh = create_data_handling(domain_size=(7, 7), periodicity=True, parallel=True,
default_target=pystencils.Target.GPU)
diff --git a/pystencils_tests/test_modulo.py b/pystencils_tests/test_modulo.py
index 7f81ab6448fe8d326d618561b1e147e60e5faea5..959daddb9d8cbc01d6db0fed65c9a411746fd5a9 100644
--- a/pystencils_tests/test_modulo.py
+++ b/pystencils_tests/test_modulo.py
@@ -9,7 +9,7 @@ from pystencils.astnodes import LoopOverCoordinate, Conditional, Block, SympyAss
@pytest.mark.parametrize('iteration_slice', [False, True])
def test_mod(target, iteration_slice):
if target == ps.Target.GPU:
- pytest.importorskip("pycuda")
+ pytest.importorskip("cupy")
dh = ps.create_data_handling(domain_size=(5, 5), periodicity=True, default_target=ps.Target.CPU)
loop_ctrs = [LoopOverCoordinate.get_loop_counter_symbol(i) for i in range(dh.dim)]
diff --git a/pystencils_tests/test_phasefield_dentritic_3D.ipynb b/pystencils_tests/test_phasefield_dentritic_3D.ipynb
index a9a51773e9e1dbbd487bea43b8d59005773c8f1b..d23f7b600624803acca301eebf82a95d6521dc92 100644
--- a/pystencils_tests/test_phasefield_dentritic_3D.ipynb
+++ b/pystencils_tests/test_phasefield_dentritic_3D.ipynb
@@ -2,17 +2,28 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<module 'cupy' from '/home/markus/.local/lib/python3.11/site-packages/cupy/__init__.py'>"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"import pytest\n",
- "pytest.importorskip('pycuda')"
+ "pytest.importorskip('cupy')"
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -32,7 +43,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -48,7 +59,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -78,38 +89,24 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
- "$\\displaystyle \\bar{\\epsilon} \\left(δ \\left(\\frac{{\\partial_{0} {{φ}_{(0,0,0)}}}^{4}}{\\left({\\partial_{0} {{φ}_{(0,0,0)}}}^{2} + {\\partial_{1} {{φ}_{(0,0,0)}}}^{2} + {\\partial_{2} {{φ}_{(0,0,0)}}}^{2}\\right)^{2}} + \\frac{{\\partial_{1} {{φ}_{(0,0,0)}}}^{4}}{\\left({\\partial_{0} {{φ}_{(0,0,0)}}}^{2} + {\\partial_{1} {{φ}_{(0,0,0)}}}^{2} + {\\partial_{2} {{φ}_{(0,0,0)}}}^{2}\\right)^{2}} + \\frac{{\\partial_{2} {{φ}_{(0,0,0)}}}^{4}}{\\left({\\partial_{0} {{φ}_{(0,0,0)}}}^{2} + {\\partial_{1} {{φ}_{(0,0,0)}}}^{2} + {\\partial_{2} {{φ}_{(0,0,0)}}}^{2}\\right)^{2}}\\right) + 1\\right)$"
+ "$\\displaystyle \\bar{\\epsilon} \\left(δ \\left(\\frac{{\\partial_{0} {φ}_{(0,0,0)}}^{4}}{\\left({\\partial_{0} {φ}_{(0,0,0)}}^{2} + {\\partial_{1} {φ}_{(0,0,0)}}^{2} + {\\partial_{2} {φ}_{(0,0,0)}}^{2}\\right)^{2}} + \\frac{{\\partial_{1} {φ}_{(0,0,0)}}^{4}}{\\left({\\partial_{0} {φ}_{(0,0,0)}}^{2} + {\\partial_{1} {φ}_{(0,0,0)}}^{2} + {\\partial_{2} {φ}_{(0,0,0)}}^{2}\\right)^{2}} + \\frac{{\\partial_{2} {φ}_{(0,0,0)}}^{4}}{\\left({\\partial_{0} {φ}_{(0,0,0)}}^{2} + {\\partial_{1} {φ}_{(0,0,0)}}^{2} + {\\partial_{2} {φ}_{(0,0,0)}}^{2}\\right)^{2}}\\right) + 1\\right)$"
],
"text/plain": [
- " ⎛ ⎛ 4 \n",
- " ⎜ ⎜ D(φ[0,0,0]) \n",
- "\\bar{\\epsilon}⋅⎜δ⋅⎜───────────────────────────────────────────── + ───────────\n",
- " ⎜ ⎜ 2 \n",
- " ⎜ ⎜⎛ 2 2 2⎞ ⎛ \n",
- " ⎠âŽâŽD(φ[0,0,0]) + D(φ[0,0,0]) + D(φ[0,0,0]) ⎠âŽD(φ[0,0,0]\n",
- "\n",
- " 4 4 \n",
- " D(φ[0,0,0]) D(φ[0,0,0]) \n",
- "────────────────────────────────── + ─────────────────────────────────────────\n",
- " 2 \n",
- " 2 2 2⎞ ⎛ 2 2 \n",
- ") + D(φ[0,0,0]) + D(φ[0,0,0]) ⎠âŽD(φ[0,0,0]) + D(φ[0,0,0]) + D(φ[0,0,0]\n",
- "\n",
- " ⎞ ⎞\n",
- " ⎟ ⎟\n",
- "────⎟ + 1⎟\n",
- " 2⎟ ⎟\n",
- " 2⎞ ⎟ ⎟\n",
- ") ⎠⎠⎠"
+ " ⎛ ⎛ 4 4 4 ⎞ ⎞\n",
+ " ⎜ ⎜ D(φ[0,0,0]) D(φ[0,0,0]) D(φ[0,0,0]) ⎟ ⎟\n",
+ "\\bar{\\epsilon}⋅⎜δ⋅⎜───────────────────────────────────────────── + ───────────────────────────────────────────── + ─────────────────────────────────────────────⎟ + 1⎟\n",
+ " ⎜ ⎜ 2 2 2⎟ ⎟\n",
+ " ⎜ ⎜⎛ 2 2 2⎞ ⎛ 2 2 2⎞ ⎛ 2 2 2⎞ ⎟ ⎟\n",
+ " ⎠âŽâŽD(φ[0,0,0]) + D(φ[0,0,0]) + D(φ[0,0,0]) ⎠âŽD(φ[0,0,0]) + D(φ[0,0,0]) + D(φ[0,0,0]) ⎠âŽD(φ[0,0,0]) + D(φ[0,0,0]) + D(φ[0,0,0]) ⎠⎠⎠"
]
},
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -127,7 +124,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -137,7 +134,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -153,7 +150,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -162,21 +159,20 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/latex": [
"$\\displaystyle \\left\\{ \\pi : 3.14159265358979, \\ T_{eq} : 1.0, \\ \\bar{\\epsilon} : 0.01, \\ j : 6, \\ α : 0.9, \\ γ : 10, \\ δ : 0.3, \\ θ_{0} : 0.2, \\ κ : 1.8, \\ τ : 0.0003\\right\\}$"
],
"text/plain": [
- "{π: 3.14159265358979, T_eq: 1.0, \\bar{\\epsilon}: 0.01, j: 6, α: 0.9, γ: 10, δ:\n",
- " 0.3, θ₀: 0.2, κ: 1.8, τ: 0.0003}"
+ "{π: 3.14159265358979, T_eq: 1.0, \\bar{\\epsilon}: 0.01, j: 6, α: 0.9, γ: 10, δ: 0.3, θ₀: 0.2, κ: 1.8, τ: 0.0003}"
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -199,7 +195,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -219,21 +215,20 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
- "$\\displaystyle \\left[ {{T}_{(0,0,0)}} \\leftarrow 0.0111111111111111 {{T}_{(-1,0,0)}} + 0.0111111111111111 {{T}_{(0,-1,0)}} + 0.0111111111111111 {{T}_{(0,0,-1)}} + 0.933333333333333 {{T}_{(0,0,0)}} + 0.0111111111111111 {{T}_{(0,0,1)}} + 0.0111111111111111 {{T}_{(0,1,0)}} + 0.0111111111111111 {{T}_{(1,0,0)}} + 1.8 \\cdot 10^{-5} {{φ_D}_{(0,0,0)}}\\right]$"
+ "$\\displaystyle \\left[ {T}_{(0,0,0)} \\leftarrow 0.0111111111111111 {T}_{(0,0,-1)} + 0.933333333333333 {T}_{(0,0,0)} + 0.0111111111111111 {T}_{(1,0,0)} + 0.0111111111111111 {T}_{(0,1,0)} + 0.0111111111111111 {T}_{(0,-1,0)} + 0.0111111111111111 {T}_{(0,0,1)} + 0.0111111111111111 {T}_{(-1,0,0)} + 1.8 \\cdot 10^{-5} {φ_D}_{(0,0,0)}\\right]$"
],
"text/plain": [
- "[T_C := 0.0111111111111111â‹…T_W + 0.0111111111111111â‹…T_S + 0.0111111111111111â‹…T\n",
- "_B + 0.933333333333333â‹…T_C + 0.0111111111111111â‹…T_T + 0.0111111111111111â‹…T_N +\n",
- " 0.0111111111111111â‹…T_E + 1.8e-5â‹…phidelta_C]"
+ "[T_C := 0.0111111111111111â‹…T_B + 0.933333333333333â‹…T_C + 0.0111111111111111â‹…T_E + 0.0111111111111111â‹…T_N + 0.0111111111111111â‹…T_S + 0.0111111111111111â‹…T_T + 0.0111111111111111â‹…T_\n",
+ "W + 1.8e-5â‹…phidelta_C]"
]
},
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -244,7 +239,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -254,7 +249,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -296,7 +291,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -313,14 +308,12 @@
},
{
"data": {
- "image/png": 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",
"text/plain": [
- "<Figure size 1152x432 with 6 Axes>"
+ "<Figure size 1600x600 with 6 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -334,7 +327,18 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Step 0 25.45903586700115 0.9934522628963286\n",
+ "Step 1 17.733385490000728 0.993928549434581\n",
+ "Step 2 17.385464679000506 0.9969817492516411\n",
+ "Step 3 17.73187294499985 0.9987018046573852\n"
+ ]
+ }
+ ],
"source": [
"if 'is_test_run' in globals():\n",
" time_loop(2)\n",
@@ -355,7 +359,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -369,9 +373,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.11.0rc1"
}
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/pystencils_tests/test_simplifications.py b/pystencils_tests/test_simplifications.py
index ef8ae7ce61a07c992d09807933061c73e61484a1..61d009d03bd341032896076b65fabcb3630859d5 100644
--- a/pystencils_tests/test_simplifications.py
+++ b/pystencils_tests/test_simplifications.py
@@ -148,7 +148,7 @@ def test_add_subexpressions_for_field_reads():
@pytest.mark.skipif((vs.major, vs.minor, vs.micro) == (3, 8, 2), reason="does not work on python 3.8.2 for some reason")
def test_sympy_optimizations(target, dtype):
if target == ps.Target.GPU:
- pytest.importorskip("pycuda")
+ pytest.importorskip("cupy")
src, dst = ps.fields(f'src, dst: {dtype}[2d]')
assignments = ps.AssignmentCollection({
@@ -172,7 +172,7 @@ def test_sympy_optimizations(target, dtype):
@pytest.mark.skipif((vs.major, vs.minor, vs.micro) == (3, 8, 2), reason="does not work on python 3.8.2 for some reason")
def test_evaluate_constant_terms(target, simplification):
if target == ps.Target.GPU:
- pytest.importorskip("pycuda")
+ pytest.importorskip("cupy")
src, dst = ps.fields('src, dst: float32[2d]')
# cos of a number will always be simplified