From d81ab754e6ff5b06c876a9e8e4185d042cbdce45 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Thu, 17 Feb 2022 15:36:51 -0500 Subject: [PATCH 1/2] updated to also measure using Nx as imput --- notebooks/cugraph_benchmarks/release.ipynb | 342 ++++++++++++++++++--- 1 file changed, 294 insertions(+), 48 deletions(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index 7d1c82468a5..3562337ea1d 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Skip notebook test\n", - "(this notebook is not executed as part of the RAPIDS cuGraph CI process. Execution could take a few hours)\n", + "(this notebook is not executed as part of the RAPIDS cuGraph CI process. Execution will take a few hours)\n", "\n", "---\n", "\n", @@ -52,7 +52,6 @@ "| ------------------------|---------------|------ | ------- |-------------\n", "| Katz | Centrality | X | | \n", "| Betweenness Centrality | Centrality | X | | Estimated, k = 100\n", - "| Betweenness Centrality | Centrality | X | | Estimated, k = 0.1% \n", "| Louvain | Community | X | | Uses python-louvain for comparison\n", "| Triangle Counting | Community | X | |\n", "| WCC | Components | | X | Nx requires directed and returns a generator \n", @@ -76,7 +75,7 @@ "\n", "\n", "### Notes\n", - "* Running Betweenness Centrality on the full graph is prohibited using NetworkX\n" + "* Running Betweenness Centrality on the full graph is prohibited using NetworkX. Anything over k=100 can explode runtime to days\n" ] }, { @@ -86,10 +85,11 @@ "Notebook Credits\n", "\n", " \n", - "| Author | Date | Update | cuGraph Version | Test Hardware |\n", - "| --------------|------------|------------------|-----------------|------------------------|\n", - "| Brad Rees | 10/06/2020 | created | 0.16 | GV100, CUDA 10.2 |\n", - "| Brad Rees | 01/20/2022 | updated | 22.02 | Quadro A6000 CUDA 11.5 |\n", + "| Author | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|---------------------|-----------------|------------------------|\n", + "| Brad Rees | 10/06/2020 | created | 0.16 | GV100, CUDA 10.2 |\n", + "| Brad Rees | 01/20/2022 | updated | 22.02 | Quadro A6000 CUDA 11.5 |\n", + "| Brad Rees | 01/20/2022 | added perf w/Nx obj | 22.02 | Quadro A6000 CUDA 11.5 |\n", "\n", "\n", "\n" @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +164,8 @@ "\n", "# for quick testing\n", "data_quick = {\n", - " 'karate' : './data/karate.mtx',\n", + " 'preferentialAttachment' : './data/preferentialAttachment.mtx', \n", + " #'karate' : './data/karate.mtx',\n", "}\n", "\n", "\n", @@ -174,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -220,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -278,6 +279,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.katz_centrality(_G, alpha)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_katz_nx(_df, alpha):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.katz_centrality(_G, alpha)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -290,11 +298,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def nx_bc(_df, _k):\n", + " print(f\" k = {_k}\", end=' ')\n", " t1 = time.time()\n", " _G = create_nx_ugraph(_df)\n", " _ = nx.betweenness_centrality(_G, k=_k)\n", @@ -306,6 +315,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.betweenness_centrality(_G, k=_k)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_bc_nx(_df, _k):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.betweenness_centrality(_G, k=_k)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -318,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -338,6 +354,13 @@ " _G = create_cu_ugraph(_df)\n", " _,_ = cugraph.louvain(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_louvain_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _,_ = cugraph.louvain(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -350,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -372,6 +395,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.triangles(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_tc_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.triangles(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -384,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -406,6 +436,13 @@ " _G = create_cu_digraph(_df) \n", " _ = cugraph.weakly_connected_components(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_wcc_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_digraph(_df) \n", + " _ = cugraph.weakly_connected_components(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -418,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -439,6 +476,13 @@ " _G = create_cu_ugraph(_df) \n", " _ = cugraph.core_number(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_core_num_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df) \n", + " _ = cugraph.core_number(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -451,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -467,6 +511,13 @@ " _G = create_cu_digraph(_df)\n", " _ = cugraph.pagerank(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_pagerank_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_digraph(_df)\n", + " _ = cugraph.pagerank(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -479,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -495,6 +546,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.jaccard_coefficient(_G)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_jaccard_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.jaccard_coefficient(_G)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -507,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -523,6 +581,13 @@ " _G = create_cu_ugraph(_df)\n", " _ = cugraph.bfs(_G, 1)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_bfs_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df)\n", + " _ = cugraph.bfs(_G, 1)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -535,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -551,6 +616,13 @@ " _G = create_cu_ugraph(_df) \n", " _ = cugraph.sssp(_G, 1)\n", " t2 = time.time() - t1\n", + " return t2\n", + "\n", + "def cu_sssp_nx(_df):\n", + " t1 = time.time()\n", + " _G = create_nx_ugraph(_df) \n", + " _ = cugraph.sssp(_G, 1)\n", + " t2 = time.time() - t1\n", " return t2" ] }, @@ -572,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -582,9 +654,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\tGDF Size 999970\n", + "\tcugraph Size 499985\n", + "\tcugraph Order 100000\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# do a simple pass just to get all the libraries initiallized\n", "# This cell might not be needed\n", @@ -624,26 +717,85 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading ./data/preferentialAttachment.mtx...\n", + "\tdata in gdf 999970 and data in pandas 999970\n", + "\tKatz n.c.cx.\n", + "\tBC k=100 n. k = 100 c.cx. \n", + "\tLouvain n.c.cx. \n", + "\tTC n.c.cx. \n", + "\tWCC n.c.cx. \n", + "\tCore Number n.c.cx. \n", + "\tPageRank n.c.cx. \n", + "\tJaccard n.c.cx. \n", + "\tBFS n.c.cx. \n", + "\tSSSP n.c.cx. \n", + "Reading ./data/dblp-2010.mtx...\n", + "\tdata in gdf 1615400 and data in pandas 1615400\n", + "\tKatz n.c.cx.\n", + "\tBC k=100 n. k = 100 c.cx. \n", + "\tLouvain n.c.cx. \n", + "\tTC n.c.cx. \n", + "\tWCC n.c.cx. \n", + "\tCore Number n.c.cx. \n", + "\tPageRank n.c.cx. \n", + "\tJaccard n.c.cx. \n", + "\tBFS n.c.cx. \n", + "\tSSSP n.c.cx. \n", + "Reading ./data/coPapersCiteseer.mtx...\n", + "\tdata in gdf 32073440 and data in pandas 32073440\n", + "\tKatz n.c.cx.\n", + "\tBC k=100 n. k = 100 c.cx. \n", + "\tLouvain n.c.cx. \n", + "\tTC n.c.cx. \n", + "\tWCC n.c.cx. \n", + "\tCore Number n.c.cx. \n", + "\tPageRank n.c.cx. \n", + "\tJaccard n.c.cx. \n", + "\tBFS n.c.cx. \n", + "\tSSSP n.c.cx. \n", + "Reading ./data/as-Skitter.mtx...\n", + "\tdata in gdf 22190596 and data in pandas 22190596\n", + "\tKatz n.c.cx.\n", + "\tBC k=100 n. k = 100 c.cx. \n", + "\tLouvain n.c.cx. \n", + "\tTC n.c.cx. \n", + "\tWCC n.c.cx. \n", + "\tCore Number n.c.cx. \n", + "\tPageRank n.c.cx. \n", + "\tJaccard n.c.cx. \n", + "\tBFS n.c.cx. \n", + "\tSSSP n.c.cx. \n" + ] + } + ], "source": [ "# arrays to capture performance gains\n", "names = []\n", "algos = []\n", "\n", - "# Two dimension data\n", - "time_algo_cu = [] # will be two dimensional\n", - "time_algo_nx = [] # will be two dimensional\n", + "# Two dimension data [file, perf]\n", + "time_algo_nx = [] # NextworkX\n", + "time_algo_cu = [] # cuGraph\n", + "time_algo_cx = [] # cuGraph\n", "perf = []\n", + "perf_cu_nx = []\n", "\n", "algos.append(\" \")\n", "\n", "i = 0\n", "for k,v in data.items():\n", - " time_algo_cu.append([])\n", " time_algo_nx.append([])\n", + " time_algo_cu.append([])\n", + " time_algo_cx.append([])\n", " perf.append([])\n", + " perf_cu_nx.append([])\n", " \n", " # Saved the file Name\n", " names.append(k)\n", @@ -667,7 +819,7 @@ " \n", " #----- Algorithm order is same as defined at top ----\n", " \n", - " # Katz \n", + " #-- Katz \n", " print(\"\\tKatz \", end = '')\n", " if i == 0: \n", " algos.append(\"Katz\")\n", @@ -676,32 +828,44 @@ " tx = nx_katz(pdf, alpha)\n", " print(\"c.\", end='')\n", " tc = cu_katz(gdf, alpha)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_katz_nx(pdf, alpha)\n", " print(\"\")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", " \n", - " # BC\n", + " #-- BC\n", " print(\"\\tBC k=100 \", end='')\n", " if i == 0:\n", " algos.append(\"BC Estimate fixed\")\n", "\n", + " k = 100\n", + " if k > num_nodes:\n", + " k = num_nodes\n", + " \n", " print(\"n.\", end='')\n", - " tx = nx_bc(pdf, 100)\n", + " tx = nx_bc(pdf, k)\n", " print(\"c.\", end='')\n", - " tc = cu_bc(gdf, 100)\n", + " tc = cu_bc(gdf, k)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_bc_nx(pdf, k)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", "\n", - " # Louvain\n", + " #-- Louvain\n", " print(\"\\tLouvain \", end='')\n", " if i == 0:\n", " algos.append(\"Louvain\")\n", @@ -710,14 +874,18 @@ " tx = nx_louvain(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_louvain(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_louvain_nx(pdf)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", - " # TC\n", + " #-- TC\n", " print(\"\\tTC \", end='')\n", " if i == 0:\n", " algos.append(\"TC\")\n", @@ -726,15 +894,19 @@ " tx = nx_tc(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_tc(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_tc_nx(pdf)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", "\n", "\n", - " # WCC\n", + " #-- WCC\n", " print(\"\\tWCC \", end='')\n", " if i == 0:\n", " algos.append(\"WCC\")\n", @@ -743,14 +915,18 @@ " tx = nx_wcc(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_wcc(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_wcc_nx(pdf)\n", " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", - " # Core Number\n", + " #-- Core Number\n", " print(\"\\tCore Number \", end='')\n", " if i == 0:\n", " algos.append(\"Core Number\")\n", @@ -759,15 +935,19 @@ " tx = nx_core_num(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_core_num(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_core_num_nx(pdf)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", - " gc.collect() \n", + " perf_cu_nx[i].append(tx/tcx)\n", + " gc.collect()\n", "\n", " \n", - " # PageRank\n", + " #-- PageRank\n", " print(\"\\tPageRank \", end='')\n", " if i == 0:\n", " algos.append(\"PageRank\")\n", @@ -776,15 +956,19 @@ " tx = nx_pagerank(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_pagerank(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_pagerank_nx(pdf)\n", " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", " \n", - " # Jaccard\n", + " #-- Jaccard\n", " print(\"\\tJaccard \", end='')\n", " if i == 0:\n", " algos.append(\"Jaccard\")\n", @@ -793,15 +977,19 @@ " tx = nx_jaccard(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_jaccard(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_jaccard_nx(pdf)\n", " print(\" \")\n", " \n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", "\n", - " # BFS\n", + " #-- BFS\n", " print(\"\\tBFS \", end='')\n", " if i == 0:\n", " algos.append(\"BFS\")\n", @@ -810,15 +998,19 @@ " tx = nx_bfs(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_bfs(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_bfs_nx(pdf)\n", " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", " \n", " \n", - " # SSSP\n", + " #-- SSSP\n", " print(\"\\tSSSP \", end='')\n", " if i == 0:\n", " algos.append(\"SSP\")\n", @@ -827,11 +1019,15 @@ " tx = nx_sssp(pdf)\n", " print(\"c.\", end='')\n", " tc = cu_sssp(gdf)\n", + " print(\"cx.\", end='')\n", + " tcx = cu_sssp(gdf)\n", " print(\" \")\n", "\n", " time_algo_nx[i].append(tx)\n", " time_algo_cu[i].append(tc)\n", + " time_algo_cx[i].append(tcx)\n", " perf[i].append(tx/tc)\n", + " perf_cu_nx[i].append(tx/tcx)\n", " gc.collect()\n", "\n", " # increament count\n", @@ -841,9 +1037,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", + "preferentialAttachment\n", + "[531.5083672005737, 713.2091362850375, 5404.871910284401, 378.8714209575697, 70.72927362986779, 76.7855488460584, 369.8407050216162, 100.5242813104882, 4.378275208783683, 162.25090274331285]\n", + "dblp\n", + "[263.7293255857766, 1558.1884974132756, 622.282663375948, 142.59215746942343, 88.19197528607465, 116.20873484788576, 460.3603070425029, 121.2753931555862, 38.357311342936576, 186.59598139534884]\n", + "coPapersCiteseer\n", + "[2270.9739290077137, 2436.608230097977, 1057.486186959562, 2300.0750485569533, 233.7511393615408, 246.97401172398136, 853.279646090366, 112.73241487709193, 233.03646005823012, 267.6295226503849]\n", + "as-Skitter\n", + "[1037.8761480097223, 7091.970778835749, 1475.2901788933452, 3950.5098941991014, 228.11245124984197, 245.35607830599636, 547.8739313855568, 98.81747467270701, 222.20389619850272, 326.73959437931654]\n" + ] + } + ], "source": [ "#Print results\n", "print(algos)\n", @@ -853,6 +1065,40 @@ " print(f\"{perf[i]}\")" ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "------------\n", + "\n", + "[' ', 'Katz', 'BC Estimate fixed', 'Louvain', 'TC', 'WCC', 'Core Number', 'PageRank', 'Jaccard', 'BFS', 'SSP']\n", + "preferentialAttachment\n", + "[4.003601795825807, 93.08063351361315, 204.47865340368915, 2.7097492953036797, 0.8666907616338123, 1.2867491927399868, 1.9752328393020735, 0.1240993236960286, 0.7203119096547798, 179.41122927863145]\n", + "dblp\n", + "[4.885862057048338, 122.87364779256156, 31.02199520144101, 2.180625966164683, 1.4324289723528034, 1.3579940932619847, 2.348198022642711, 0.11145559203860335, 0.7265996313793216, 202.49957600312277]\n", + "coPapersCiteseer\n", + "[8.158465907417547, 49.369787236543914, 9.28701234947398, 12.067711445592089, 0.8232363804394461, 1.3752921888176353, 2.8151237928186994, 0.08727548257474192, 0.798480184263566, 322.0841686740297]\n", + "as-Skitter\n", + "[3.66123817526995, 631.2091494372795, 20.617572936160165, 34.49437773532841, 0.9264442180418646, 4.828647431829589, 2.2476285215684615, 0.12267649645919085, 0.8469181921567667, 431.6171964493335]\n" + ] + } + ], + "source": [ + "#Print results\n", + "print(\"\\n------------\\n\")\n", + "print(algos)\n", + "\n", + "for i in range(num_datasets):\n", + " print(f\"{names[i]}\")\n", + " print(f\"{perf_cu_nx[i]}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, From e5d65a70aa500d35b9090ab123b09fcdfb1ae5d9 Mon Sep 17 00:00:00 2001 From: BradReesWork Date: Tue, 1 Mar 2022 16:33:24 -0500 Subject: [PATCH 2/2] fixed typo --- notebooks/cugraph_benchmarks/release.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/cugraph_benchmarks/release.ipynb b/notebooks/cugraph_benchmarks/release.ipynb index 3562337ea1d..e9d887a04a9 100644 --- a/notebooks/cugraph_benchmarks/release.ipynb +++ b/notebooks/cugraph_benchmarks/release.ipynb @@ -781,7 +781,7 @@ "algos = []\n", "\n", "# Two dimension data [file, perf]\n", - "time_algo_nx = [] # NextworkX\n", + "time_algo_nx = [] # NetworkX\n", "time_algo_cu = [] # cuGraph\n", "time_algo_cx = [] # cuGraph\n", "perf = []\n",