')
+ print(f"
Hostname: {socket.gethostname()}
")
+ print(
+ f'
Operating System: {platform.system()} {platform.release()}
'
+ )
+ print(f'
Kernel Version : {platform.version()}
')
+ with open("/proc/cpuinfo") as f:
+ print(
+ f'
CPU: {next(line.strip().split(": ")[1] for line in f if "model name" in line)} ({psutil.cpu_count(logical=False)} cores)
'
+ )
+ print(f'
Memory: {get_mem_info()} GB
')
+ print(f"
GPU: {get_first_gpu_info()}
")
+ print(f"
CUDA Version: {get_cuda_version()}
")
+
+
+if __name__ == "__main__":
+ logs_dir = pathlib.Path("logs")
+
+ dataset_patt = re.compile(".*ds=([\w-]+).*")
+ backend_patt = re.compile(".*backend=(\w+).*")
+ k_patt = re.compile(".*k=(10*).*")
+
+ # Organize all benchmark runs by the following hierarchy: algo -> backend -> dataset
+ benchmarks = get_all_benchmark_info()
+
+ # dump HTML table
+ ordered_datasets = [
+ "netscience",
+ "email_Eu_core",
+ "cit-patents",
+ "hollywood",
+ "soc-livejournal1",
+ ]
+ # dataset, # Node, # Edge, Directed info
+ dataset_meta = {
+ "netscience": ["1,461", "5,484", "Yes"],
+ "email_Eu_core": ["1,005", "25,571", "Yes"],
+ "cit-patents": ["3,774,768", "16,518,948", "Yes"],
+ "hollywood": ["1,139,905", "57,515,616", "No"],
+ "soc-livejournal1": ["4,847,571", "68,993,773", "Yes"],
+ }
+
+ print(
+ """
+
+
+
+
+
+
+
+ Dataset
Nodes
Edges
Directed | """
+ )
+ for ds in ordered_datasets:
+ print(
+ f" {ds}
{dataset_meta[ds][0]}
{dataset_meta[ds][1]}
{dataset_meta[ds][2]}
| "
+ )
+ print(
+ """
+
+
+ """
+ )
+ for algo_name in sorted(benchmarks):
+ algo_runs = benchmarks[algo_name]
+ print(" ")
+ print(f" | {algo_name} | ")
+ # Proceed only if any results are present for both cugraph and NX
+ if "cugraph" in algo_runs and "networkx" in algo_runs:
+ cugraph_algo_runs = algo_runs["cugraph"]
+ networkx_algo_runs = algo_runs["networkx"]
+ datasets_in_both = set(cugraph_algo_runs).intersection(networkx_algo_runs)
+
+ # populate the table with speedup results for each dataset in the order
+ # specified in ordered_datasets. If results for a run using a dataset
+ # are not present for both cugraph and NX, output an empty cell.
+ for dataset in ordered_datasets:
+ if dataset in datasets_in_both:
+ cugraph_runtime = cugraph_algo_runs[dataset]
+ networkx_runtime = networkx_algo_runs[dataset]
+ (speedup, runtime_delta) = compute_perf_vals(
+ cugraph_runtime=cugraph_runtime,
+ networkx_runtime=networkx_runtime,
+ )
+ nx_formatted = get_formatted_time_value(networkx_runtime)
+ cg_formatted = get_formatted_time_value(cugraph_runtime)
+ print(
+ f" {nx_formatted} / {cg_formatted}
{speedup}
{runtime_delta} | "
+ )
+ else:
+ print(f" | ")
+
+ # If a comparison between cugraph and NX cannot be made, output empty cells
+ # for each dataset
+ else:
+ for _ in range(len(ordered_datasets)):
+ print(" | ")
+ print("
")
+ print(
+ """
+ \n
+
+
+
Table Format:
+
+ - NetworkX time / nx-cugraph time
+ - Speed-up of using nx-cugraph
+ - Time-delta
+
+
"""
+ )
+ get_system_info()
+ print("""
\n