benchmarking infrastructure

.gitignore

@@ -127,3 +127,7 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+# asv 
+results/
+html/

benchmarks/README.md

@@ -0,0 +1,7 @@
+## Preview benchmarks locally
+
+1. clone this repo
+2. `cd benchmarks`
+3. `asv run` will run the benchmarks on last commit (or `asv continuous base_commit_hash test_commit_hash` to run the benchmark to compare two commits)
+4. `asv publish` will create a `html` folder with the results
+5. `asv preview` will host the results locally at http://127.0.0.1:8080/

benchmarks/asv.conf.json

@@ -0,0 +1,40 @@
+{
+    "version": 1,
+
+    "project": "nx-parallel",
+
+    "project_url": "https://github.com/networkx/nx-parallel",
+
+    "repo": "..",
+
+    "branches": ["main"],
+
+    "build_command": [
+        "python -m pip install build hatchling",
+        "python -m build .",
+        "PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}"
+    ],
+
+    "install_command": ["in-dir={env_dir} python -mpip install {wheel_file}"],
+
+    "dvcs": "git",
+
+    "environment_type": "virtualenv",
+
+    "show_commit_url": "https://github.com/networkx/nx-parallel/commit/",
+
+    "matrix": {
+        "networkx":[],
+         "nx-parallel": []
+    },
+
+    "benchmark_dir": "benchmarks",
+
+    "env_dir": "env",
+
+    "results_dir": "results",
+
+    "html_dir": "html",
+
+    "build_cache_size": 8
+}

benchmarks/benchmarks/bench_centrality.py

@@ -0,0 +1,22 @@
+import networkx as nx
+import nx_parallel as nxp
+
+
+class BetweennessCentralityBenchmark:
+    params = [("parallel", "normal")]
+    param_names = ["algo_type"]
+
+    def setup(self, algo_type):
+        self.algo_type = algo_type
+
+    def time_betweenness_centrality(self, algo_type):
+        num_nodes, edge_prob = 300, 0.5
+        G = nx.fast_gnp_random_graph(num_nodes, edge_prob, directed=False)
+        if algo_type == "parallel":
+            H = nxp.ParallelGraph(G)            
+            _ = nx.betweenness_centrality(H)
+        elif algo_type == "normal":
+            _ = nx.betweenness_centrality(G)
+        else:
+            raise ValueError("Unknown algo_type") 
+