303 lines
13 KiB
Python
303 lines
13 KiB
Python
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#! /bin/python3
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import os
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import re
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from dataclasses import dataclass
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import json
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import matplotlib.pyplot as plt
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import numpy
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from scipy.io import mmread
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import math
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import itertools
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import argparse
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perf_stats = ["task-clock:u", "page-faults:u", "cycles:u", "instructions:u",
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"branches:u", "branch-misses:u", "L1-dcache-loads:u",
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"L1-dcache-load-misses:u", "LLC-loads:u", "LLC-load-misses:u",
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"L1-icache-loads:u", "L1-icache-load-misses:u", "dTLB-loads:u",
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"dTLB-load-misses:u", "iTLB-loads:u", "iTLB-load-misses:u"]
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def read_stats(filename):
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# Split filename into each input parameter.
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filename_split = os.path.splitext(filename)[0].split('_')
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s = {
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"solver": filename_split[0],
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"linalg": filename_split[1],
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"file": filename_split[2],
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"time_steps": int(filename_split[3]),
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"cores": int(re.match(r'\d+', filename_split[4]).group(0)),
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"cpu": filename_split[5] }
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# Take filename and read SchurComplement matrix from corresponding dir.
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print(os.path.splitext(filename)[0])
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m = mmread(os.path.splitext(filename)[0] + "/SchurComplement.mm")
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s["cols"] = m.get_shape()[0]
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s["nonzero"] = m.count_nonzero()
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s["entries"] = m.get_shape()[0] * m.get_shape()[1]
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s["trace"] = m.trace()
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# Compute ratio.
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s["% nonzero entry"] = s["nonzero"] / s["entries"]
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s["trace over cols"] = s["trace"] / s["cols"]
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# Obtain stats from perf in each log.
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with open(filename) as file:
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for line in file:
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for perf_stat in perf_stats:
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regex = r'^\W*([\d+(,|\.)?]+)\W*.*' + perf_stat
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data = re.search(regex, line)
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if data is None:
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continue
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match perf_stat:
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case "task-clock:u":
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s[perf_stat[:-2] + " (msec)"] = float(data.group(1).replace(',', ''))
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case "page-faults:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "cycles:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "instructions:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "branches:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "branch-misses:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "L1-dcache-loads:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "L1-dcache-load-misses:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "LLC-loads:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "LLC-load-misses:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "L1-icache-loads:u":
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print("l1_icache_loads not expected! Exiting...")
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exit(1)
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case "L1-icache-load-misses:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "dTLB-loads:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "dTLB-load-misses:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "iTLB-loads:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case "iTLB-load-misses:u":
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s[perf_stat[:-2]] = int(data.group(1).replace(',', ''))
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case _:
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print("Unexpected case! Exiting...")
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exit(1)
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# Derive stats.
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if s["cycles"] != 0:
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s["insn per cycle"] = s["instructions"] / s["cycles"]
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else:
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s["insn per cycle"] = None
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s["branch-miss-rate"] = s["branch-misses"] / s["branches"]
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s["L1-dcache-miss-rate"] = s["L1-dcache-load-misses"] / s["L1-dcache-loads"]
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s["LLC-miss-rate"] = s["LLC-load-misses"] / s["LLC-loads"]
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s["dTLB-miss-rate"] = s["dTLB-load-misses"] / s["dTLB-loads"]
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s["iTLB-miss-rate"] = s["iTLB-load-misses"] / s["iTLB-loads"]
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print(s)
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return s
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def box_plot(ax, stat_list, x_name, y_name):
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data = accumulate_to_dict(stat_list, x_name, y_name)
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for value in data.values():
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if type(value[0]) is str:
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print(f"{y_name} values are strings... skipping box plot...")
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return
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print("Plotted data: " + str(data))
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ax.boxplot([numpy.array(y_axis) for y_axis in data.values()])
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ax.set_xticklabels(data.keys())
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ax.set_ylabel(y_name)
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def scatter_plot(ax, stat_list, x_name, y_name, color_name, size_name = None):
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x_data = accumulate_to_dict(stat_list, color_name, x_name)
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y_data = accumulate_to_dict(stat_list, color_name, y_name)
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if size_name is not None:
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size_data = accumulate_to_dict(stat_list, color_name, size_name)
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print(x_data)
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print(y_data)
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if size_name is not None:
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print(size_data)
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for group in x_data.keys():
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print(f"Plotted x data for {group}: " + str(x_data[group]))
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print(f"Plotted y data for {group}: " + str(y_data[group]))
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ax.scatter(x_data[group], y_data[group], label = group, alpha = 0.3)
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#ax.legend()
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#ax.set_xlabel(x_name)
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ax.set_ylabel(y_name)
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ax.grid(True)
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def line_plot(ax, stat_list, x_name, y_name, color_name = None, marker_name = None):
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x_data = accumulate_to_dict(stat_list, color_name, x_name, marker_name)
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y_data = accumulate_to_dict(stat_list, color_name, y_name, marker_name)
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for group in x_data.keys():
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sorted_data = [(x, y) for (x, y) in zip(x_data[group], y_data[group])]
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sorted_data.sort()
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x_data[group] = [x for (x, y) in sorted_data]
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y_data[group] = [y for (x, y) in sorted_data]
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print(f"Plotted x data for {group}: " + str(x_data[group]))
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print(f"Plotted y data for {group}: " + str(y_data[group]))
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ax.plot(x_data[group], y_data[group], label = group)
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ax.set_ylabel(y_name)
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ax.grid(True)
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def basic_plot(ax, stat_list, x_name, y_name):
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xs = accumulate_to_list(stat_list, x_name)
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ys = accumulate_to_list(stat_list, y_name)
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print("Plotted x data: " + str(xs))
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print("Plotted y data: " + str(ys))
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ax.plot(xs, ys, 'o')
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ax.legend()
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ax.set_xlabel(x_name)
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ax.set_ylabel(y_name)
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ax.grid(True)
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def accumulate_to_dict(stat_list, key_name, value_name, key2_name = None):
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key_list = [stat[key_name] for stat in stat_list]
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value_list = [stat[value_name] for stat in stat_list]
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if key2_name is not None:
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key2_list = [stat[key2_name] for stat in stat_list]
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data = {pair: list() for pair in
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list(itertools.product(list(dict.fromkeys(key_list).keys()),
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list(dict.fromkeys(key2_list).keys())))}
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for key, key2, value in zip(key_list, key2_list, value_list):
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data[(key, key2)].append(value)
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else:
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data = {key: list() for key in dict.fromkeys(key_list)}
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for key, value in zip(key_list, value_list):
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data[key].append(value)
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return data
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def accumulate_to_list(stat_list, name):
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lst = [stat[name] for stat in stat_list]
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return lst
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def show_visualizations(rows, plot, size_multiplier, stat_list, x_name, y_name = None,
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color_name = None, marker_name = None, filter_list = []):
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# Filter stats (removing values such as a specific CPU type)
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stat_list = [stat for stat in stat_list
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if len([stat[key] for key in stat.keys()
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if stat[key] in filter_list])
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== 0]
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if y_name is None or y_name == "all":
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# Filter y names (removing measurements such as cycles)
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y_names = [y_name for y_name in stat_list[0].keys() if y_name != x_name
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and y_name != color_name
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and y_name != marker_name
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and y_name not in filter_list]
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fig, axes = plt.subplots(rows, int(math.ceil(len(y_names) / rows)),
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figsize = (16 * size_multiplier, 9 * size_multiplier))
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match plot:
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case "box":
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for i, y in enumerate(y_names):
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box_plot(axes[i % rows][int(i / rows)], stat_list, x_name, y)
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case "line":
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for i, y in enumerate(y_names):
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line_plot(axes[i % rows][int(i / rows)], stat_list, x_name,
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y, color_name, marker_name)
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case _:
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print("Invalid plot type! Exiting...")
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exit(1)
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handles, labels = axes[i % rows][int(i / rows)].get_legend_handles_labels()
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else:
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fig, ax = plt.subplots()
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match plot:
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case "box":
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box_plot(ax, stat_list, x_name, y_name)
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case "line":
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line_plot(ax, stat_list, x_name, y_name, color_name, marker_name)
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case _:
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print("Invalid plot type! Exiting...")
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exit(1)
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handles, labels = ax.get_legend_handles_labels()
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if y_name is None or y_name == "all":
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y_name = "all"
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fontsize = 'xx-large'
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else:
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fontsize = 'x-small'
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title = f"{plot}_plot_of_{y_name}_vs_{x_name}_by_{color_name}_and_{marker_name}_excluding_{filter_list}"
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fig.suptitle(title, fontsize = fontsize)
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fig.legend(handles, labels, fontsize = fontsize)
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fig.supxlabel(x_name, fontsize = fontsize)
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plt.savefig(title + ".png", dpi = 100)
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plt.show()
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save_file = "stats.json"
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samples_dir = "Samples/"
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# save_file = "stats_avg_10.json"
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# samples_dir = "Samples_Avg_10/"
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# save_file = "tmp.json"
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# samples_dir = "Old_Samples_Avg_10"
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def main():
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stat_list = list()
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# Get stats and accumulate into stat_list
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if os.path.isfile(save_file):
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with open(save_file, 'r') as file:
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stat_list = json.load(file)
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# print(*stat_list, sep='\n')
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else:
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os.chdir('./' + samples_dir)
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for filename in os.listdir(os.getcwd()):
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if os.path.splitext(filename)[1] == ".log":
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stat_list.append(read_stats(filename))
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print(*stat_list, sep='\n')
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os.chdir('../')
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with open(save_file, 'w') as file:
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json.dump(stat_list, file, indent = 2)
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# print(*stat_list, sep='\n')
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possible_args = [keys for keys in stat_list[0].keys()]
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parser = argparse.ArgumentParser(epilog = 'Possible arguments are: ' + str(possible_args))
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parser.add_argument('-r', '--rows', help = 'Number of rows to display. Will split graphs into columns to evenly fill rows. Does not do anything if -y is not "all".', default = 0)
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parser.add_argument('-p', '--plot', help = 'The type of plot. Only "box" and "line" are supported.')
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parser.add_argument('-x', help = 'Name of the x axis. See below for possible arguments.')
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parser.add_argument('-y', help = 'Name of the y axis. Can be "all" or omitted entirely to display all possible y values. See below for other possible arguments.')
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parser.add_argument('-g1', '--group1', help = 'Name of the first group to group by. Does not need to be specified.')
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parser.add_argument('-g2', '--group2', help = 'Name of the second group to group by. Does not need to be specified (and does not matter for box plots).')
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parser.add_argument('-f', '--filter', nargs = '+', help = 'Names and string values to filter out of the visualization. Can be multiple space-separate arguments. Solver filters are "MueLu", "CG", "GMRES", and "ML". CPU filters are "Xeon,4216" and "Xeon,E5-2683"4216".', default = [])
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parser.add_argument('-s', '--size', help = 'Multiplier for the resultant figure size if the visualization is too difficult to view. Default is 4.', default = 4)
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args = parser.parse_args()
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print(args)
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if args.plot == "line" and args.group1 is None:
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print("Line plot requires at least one group! Exiting...")
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exit(1)
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# Plot
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show_visualizations(int(args.rows), args.plot, int(args.size), stat_list, args.x, args.y, args.group1, args.group2, args.filter)
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#show_visualizations(4, "line", stat_list, "cols", None, "solver", "cpu", ["Xeon,E5-2683", "time_steps", "cpu", "linalg", "file", "cores"])
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#show_visualizations(4, "line", stat_list, "cols", None, "solver", "cpu", ["time_steps", "cpu", "linalg", "file", "cores"])
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#show_visualizations(4, "line", stat_list, "LLC-miss-rate", "branch-miss-rate", "solver", "cpu")
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#show_visualizations(4, "box", stat_list, "solver", "cycles", None, None, ["Xeon,E5-2683", 0, None])
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#show_visualizations(4, "box", stat_list, "solver", None, None, None, [0, None])
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if __name__ == '__main__':
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main()
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