s['median'], s['std_dev'],
s['max_90'], s['min_90'], 'N/A'])
- s = array_stats([float(i["runtime"]) / 1000 for i in op_data])
+ s = array_stats([float(i["runtime"]) for i in op_data])
table.add_row(["duration (s)",
s['min'], s['max'], s['mean'],
s['median'], s['std_dev'],
if len(op_data) else 0)
clat_10 = array_stats([i["clat_ns_10"] for i in op_data])
clat_90 = array_stats([i["clat_ns_90"] for i in op_data])
- table.add_row(["completion latency (ns)",
- clat_min['min'], clat_max['max'], clat_mean['mean'],
- clat_mean['median'], clat_stddev,
- clat_10['mean'], clat_90['mean'], clat_mean['sum']])
+ # For convenience, we'll convert it from ns to seconds.
+ table.add_row(["completion latency (s)",
+ clat_min['min'] / 1e+9,
+ clat_max['max'] / 1e+9,
+ clat_mean['mean'] / 1e+9,
+ clat_mean['median'] / 1e+9,
+ clat_stddev / 1e+9,
+ clat_10['mean'] / 1e+9,
+ clat_90['mean'] / 1e+9,
+ clat_mean['sum'] / 1e+9])
print(table)