diff --git a/codes/kwant_helper/utils.py b/codes/kwant_helper/utils.py
index 6a67b48eab8dab51493bc333a6efe30000811ad2..6e74a4e9f237cb564c93edd97d54a8dd3dbb3f66 100644
--- a/codes/kwant_helper/utils.py
+++ b/codes/kwant_helper/utils.py
@@ -6,7 +6,7 @@ import inspect
from copy import copy
-def builder2h_0(builder, params={}, return_data=False):
+def builder2tb(builder, params={}, return_data=False):
"""
Constructs a tight-binding model dictionary from a `kwant.Builder`.
diff --git a/codes/test_graphene.py b/codes/test_graphene.py
index 9dbf414adcbf4087e6a0857563b492e26f30a266..59769cc10a4159c28fe9ec9663eb44be0aabcb6d 100644
--- a/codes/test_graphene.py
+++ b/codes/test_graphene.py
@@ -11,7 +11,7 @@ import pytest
repeatNumber = 10
# %%
graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()
-h_0 = utils.builder2h_0(graphene_builder)
+h_0 = utils.builder2tb(graphene_builder)
# %%
@@ -45,7 +45,7 @@ def gap_prediction(U, V):
filling = 2
nK = 20
- h_int = utils.builder2h_0(int_builder, params)
+ h_int = utils.builder2tb(int_builder, params)
guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))
model = Model(h_0, h_int, filling)
diff --git a/examples/graphene_extended_hubbard.ipynb b/examples/graphene_extended_hubbard.ipynb
index d394d1004f18b0a102935280d4ba7268db0d8405..20b9770e677681f124134c24d7f0ba744d4a19bf 100644
--- a/examples/graphene_extended_hubbard.ipynb
+++ b/examples/graphene_extended_hubbard.ipynb
@@ -65,7 +65,7 @@
"source": [
"# Create translationally-invariant `kwant.Builder`\n",
"graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()\n",
- "h_0 = utils.builder2h_0(graphene_builder)"
+ "h_0 = utils.builder2tb(graphene_builder)"
]
},
{
@@ -75,6 +75,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "np.random.seed(5)\n",
"def compute_phase_diagram(Us, Vs, int_builder, h_0): \n",
" gap = []\n",
" ks = np.linspace(-np.pi, np.pi, 300)\n",
@@ -83,12 +84,12 @@
" guess=None\n",
" for V in Vs: \n",
" params = dict(U=U, V=V)\n",
- " h_int = utils.builder2h_0(int_builder, params)\n",
+ " h_int = utils.builder2tb(int_builder, params)\n",
" if guess==None:\n",
" guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))\n",
" model = Model(h_0, h_int, filling=2)\n",
"\n",
- " mf_sol = solverkvector(model, guess, nK=18)\n",
+ " mf_sol = solver(model, guess, nK=18)\n",
" hkfunc = tb2kfunc(addTb(h_0, mf_sol))\n",
" hkarray = np.array([hkfunc((kx, -kx)) for kx in ks])\n",
" vals = np.linalg.eigvalsh(hkarray)\n",
@@ -100,22 +101,33 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"id": "14f332f2",
"metadata": {},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 10/10 [02:25<00:00, 14.56s/it]\n"
+ "ename": "RuntimeError",
+ "evalue": "There is already a profiler running. You cannot run multiple profilers in the same thread or async context, unless you disable async support.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[9], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m Us \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m10\u001b[39m, endpoint\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 4\u001b[0m Vs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1.5\u001b[39m, \u001b[38;5;241m10\u001b[39m, endpoint\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mprofiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m compute_phase_diagram(Us, Vs, int_builder, h_0)\n\u001b[1;32m 7\u001b[0m profiler\u001b[38;5;241m.\u001b[39mstop()\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/python3/lib/python3.11/site-packages/pyinstrument/profiler.py:131\u001b[0m, in \u001b[0;36mProfiler.start\u001b[0;34m(self, caller_frame)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_active_session \u001b[38;5;241m=\u001b[39m ActiveProfilerSession(\n\u001b[1;32m 125\u001b[0m start_time\u001b[38;5;241m=\u001b[39mtime\u001b[38;5;241m.\u001b[39mtime(),\n\u001b[1;32m 126\u001b[0m start_process_time\u001b[38;5;241m=\u001b[39mprocess_time(),\n\u001b[1;32m 127\u001b[0m start_call_stack\u001b[38;5;241m=\u001b[39mbuild_call_stack(caller_frame, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minitial\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m 128\u001b[0m )\n\u001b[1;32m 130\u001b[0m use_async_context \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39masync_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdisabled\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 131\u001b[0m \u001b[43mget_stack_sampler\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubscribe\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 132\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sampler_saw_call_stack\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_async_context\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_active_session \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/python3/lib/python3.11/site-packages/pyinstrument/stack_sampler.py:62\u001b[0m, in \u001b[0;36mStackSampler.subscribe\u001b[0;34m(self, target, desired_interval, use_async_context)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_async_context:\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m active_profiler_context_var\u001b[38;5;241m.\u001b[39mget() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 62\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 63\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThere is already a profiler running. You cannot run multiple profilers in the same thread or async context, unless you disable async support.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 64\u001b[0m )\n\u001b[1;32m 65\u001b[0m active_profiler_context_var\u001b[38;5;241m.\u001b[39mset(target)\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubscribers\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m 68\u001b[0m StackSamplerSubscriber(\n\u001b[1;32m 69\u001b[0m target\u001b[38;5;241m=\u001b[39mtarget,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 73\u001b[0m )\n\u001b[1;32m 74\u001b[0m )\n",
+ "\u001b[0;31mRuntimeError\u001b[0m: There is already a profiler running. You cannot run multiple profilers in the same thread or async context, unless you disable async support."
]
}
],
"source": [
+ "from pyinstrument import Profiler\n",
+ "profiler = Profiler()\n",
"Us = np.linspace(0, 3, 10, endpoint=True)\n",
"Vs = np.linspace(0, 1.5, 10, endpoint=True)\n",
- "gap = compute_phase_diagram(Us, Vs, int_builder, h_0)"
+ "profiler.start()\n",
+ "compute_phase_diagram(Us, Vs, int_builder, h_0)\n",
+ "profiler.stop()\n",
+ "profiler.write_html(path=\"timeProfileGrapheneExtendedOld.html\")"
]
},
{
@@ -213,7 +225,7 @@
"params = dict(U=0, V=1)\n",
"filling = 2 \n",
"\n",
- "h_int = utils.builder2h_0(int_builder, params)\n",
+ "h_int = utils.builder2tb(int_builder, params)\n",
"model = Model(h_0, h_int, filling)\n",
"mf_guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))\n",
"mf_sol = solver(model, mf_guess, nK=30)\n",
@@ -228,10 +240,131 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "e183c3cb",
"metadata": {},
"outputs": [],
+ "source": [
+ "\n",
+ "import numpy as np\n",
+ "from codes.model import Model\n",
+ "from codes import kwant_examples\n",
+ "from codes.kwant_helper import utils\n",
+ "import timeit\n",
+ "import memray\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "078dd782",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
+ ]
+ }
+ ],
+ "source": [
+ "graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()\n",
+ "\n",
+ "params = {\"U\": 0.5, \"V\": 1.1}\n",
+ "filling = 2\n",
+ "nK = 300\n",
+ "\n",
+ "h_int = utils.builder2tb(int_builder, params)\n",
+ "h_0 = utils.builder2tb(graphene_builder)\n",
+ "guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))\n",
+ "\n",
+ "model = Model(h_0, h_int, filling)\n",
+ "\n",
+ "\n",
+ "def scf_loop():\n",
+ " model.mfield(guess, nK=nK)\n",
+ "\n",
+ "\n",
+ "# %% Memory profile\n",
+ "with memray.Tracker(\"memoryProfile.bin\"):\n",
+ " scf_loop()\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "3455735e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %% Time profiler\n",
+ "profiler = Profiler()\n",
+ "\n",
+ "profiler.start()\n",
+ "scf_loop()\n",
+ "profiler.stop()\n",
+ "profiler.write_html(path=\"timeProfile.html\")\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "75fe9023",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Single SCF loop takes 7.120591291008168 whereas a single diagonalization of a corresponding system takes 0.024635875000967644\n"
+ ]
+ }
+ ],
+ "source": [
+ "# %%\n",
+ "number = 1\n",
+ "\n",
+ "timeSCF = timeit.timeit(scf_loop, number=number) / number\n",
+ "\n",
+ "H = np.random.rand(nK, nK)\n",
+ "H += H.T.conj()\n",
+ "timeDiag = timeit.timeit(lambda: np.linalg.eigh(H), number=number) / number\n",
+ "\n",
+ "print(\n",
+ " f\"Single SCF loop takes {timeSCF} whereas a single diagonalization of a corresponding system takes {timeDiag}\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f650872f",
+ "metadata": {},
+ "outputs": [],
"source": []
}
],
diff --git a/profiling/graphene.py b/profiling/graphene.py
index 82681de9e152e7ee41fe4390987e6ba401b8062a..a82e382721a2321ff6ba02bc51903c4adf1ccc09 100644
--- a/profiling/graphene.py
+++ b/profiling/graphene.py
@@ -14,8 +14,8 @@ params = {"U": 0.5, "V": 1.1}
filling = 2
nK = 600
-h_int = utils.builder2h_0(int_builder, params)
-h_0 = utils.builder2h_0(graphene_builder)
+h_int = utils.builder2tb(int_builder, params)
+h_0 = utils.builder2tb(graphene_builder)
guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))
model = Model(h_0, h_int, filling)