385 lines
14 KiB
Python
Executable File
385 lines
14 KiB
Python
Executable File
#! /bin/python3
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import argparse
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import os
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import re
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import json
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from enum import Enum
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import math
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import numpy as np
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import matplotlib.pyplot as plt
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import itertools
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class Stat(Enum):
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CPU = 'cpu'
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SOLVER = 'solver'
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LIN_ALG = 'linear algebra'
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INPUT_FILE = 'input file'
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MAXWELL_SIZE = 'maxwell size'
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MATRIX_COLS = 'matrix columns'
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POWER_DELTA = 'Δ watt'
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TASK_CLK = 'task clock (msec)'
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PAGE_FAULTS = 'page faults'
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CYCLES = 'cycles'
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INST = 'instructions'
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BR = 'branches'
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BR_MISS = 'branch mispredictions'
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ITLB = 'ITLB accesses'
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ITLB_MISS = 'ITLB misses'
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DTLB = 'DTLB accesses'
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DTLB_MISS = 'DTLB misses'
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L2D_TLB = 'L2D TLB accesses'
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L2D_TLB_MISS = 'L2D TLB misses'
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L1I_CACHE = 'L1I cache accesses'
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L1I_CACHE_MISS = 'L1I cache misses'
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L1D_CACHE = 'L1D cache accesses'
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L1D_CACHE_MISS = 'L1D cache misses'
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L2D_CACHE = 'L2D cache accesses'
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L2D_CACHE_MISS = 'L2D cache misses'
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LL_CACHE = 'LL cache accesses'
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LL_CACHE_MISS = 'LL cache misses'
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BRANCH_MISS_RATE = 'branch miss rate'
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ITLB_MISS_RATE = 'ITLB miss rate'
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DTLB_MISS_RATE = 'DTLB miss rate'
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L1I_CACHE_MISS_RATE = 'L1I cache miss rate'
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L1D_CACHE_MISS_RATE = 'L1D cache miss rate'
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L2D_CACHE_MISS_RATE = 'L2D cache miss rate'
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LL_CACHE_MISS_RATE = 'LL cache miss rate'
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altra_names = {
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Stat.TASK_CLK: 'task-clock:u',
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Stat.PAGE_FAULTS: 'page-faults:u',
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Stat.CYCLES: 'cycles:u',
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Stat.INST: 'instructions:u',
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Stat.BR: 'BR_RETIRED:u',
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Stat.BR_MISS: 'BR_MIS_PRED_RETIRED:u',
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Stat.ITLB: 'L1I_TLB:u',
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Stat.ITLB_MISS: 'ITLB_WALK:u',
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Stat.DTLB: 'L1D_TLB:u',
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Stat.DTLB_MISS: 'DTLB_WALK:u',
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Stat.L2D_TLB: 'L2D_TLB:u',
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Stat.L2D_TLB_MISS: 'L2D_TLB_REFILL:u',
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Stat.L1I_CACHE: 'L1I_CACHE:u',
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Stat.L1I_CACHE_MISS: 'L1I_CACHE_REFILL:u',
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Stat.L1D_CACHE: 'L1D_CACHE:u',
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Stat.L1D_CACHE_MISS: 'L1D_CACHE_REFILL:u',
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Stat.L2D_CACHE: 'L2D_CACHE:u',
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Stat.L2D_CACHE_MISS: 'L2D_CACHE_REFILL:u',
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Stat.LL_CACHE: 'LL_CACHE_RD:u',
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Stat.LL_CACHE_MISS: 'LL_CACHE_MISS_RD:u',
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}
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xeon_names = {
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Stat.TASK_CLK: 'task-clock:u',
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Stat.PAGE_FAULTS: 'page-faults:u',
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Stat.CYCLES: 'cycles:u',
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Stat.INST: 'instructions:u',
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Stat.BR: 'branches:u',
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Stat.BR_MISS: 'branch-misses:u',
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Stat.ITLB: 'iTLB-loads:u',
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Stat.ITLB_MISS: 'iTLB-load-misses:u',
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Stat.DTLB: 'dTLB-loads:u',
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Stat.DTLB_MISS: 'dTLB-load-misses:u',
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Stat.L1I_CACHE: 'L1-icache-loads:u',
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Stat.L1I_CACHE_MISS: 'L1-icache-load-misses:u',
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Stat.L1D_CACHE: 'L1-dcache-loads:u',
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Stat.L1D_CACHE_MISS: 'L1-dcache-load-misses:u',
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Stat.LL_CACHE: 'LLC-loads:u',
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Stat.LL_CACHE_MISS: 'LLC-load-misses:u',
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}
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class Workload(Enum):
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MINI_EM = 'mini_em'
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class CPU(Enum):
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ALTRA = altra_names
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XEON = xeon_names
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class Plot(Enum):
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BOX = 'box'
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LINE = 'line'
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def parse_input(filename: str, workload: Workload) -> dict[str, str | int]:
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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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i = 0
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data = {Stat.CPU.value: filename_split[i]}
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i += 1
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if workload == Workload.MINI_EM:
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data[Stat.SOLVER.value] = filename_split[i]
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i += 1
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data[Stat.LIN_ALG.value] = filename_split[i]
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i += 1
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data[Stat.INPUT_FILE.value] = filename_split[i]
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regex = r'^maxwell(\d+)'
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data[Stat.MAXWELL_SIZE.value] = int(re.search(regex, filename_split[i]).group(1))
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x = data[Stat.MAXWELL_SIZE.value]
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data[Stat.MATRIX_COLS.value] = 3 * x ** 3 + 6 * x ** 2 + 3 * x
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i += 1
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return data
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def parse_output(filename: str, data: dict[str, str]) -> dict[str, str | int | float]:
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result: dict[str, int | float] = dict()
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cpu: CPU = CPU[data['cpu'].upper()]
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with open(filename, 'r') as file:
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for line in file:
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for stat in [x for x in Stat if x in cpu.value]:
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regex = r'^\W*([\d+(,|\.)?]+)\W*.*' + cpu.value[stat]
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value = re.search(regex, line)
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if value is None:
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continue
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elif stat == Stat.TASK_CLK:
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result[stat.value] = float(value.group(1).replace(',', ''))
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else:
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result[stat.value] = int(value.group(1).replace(',', ''))
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return result | parse_power(filename, cpu)
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# TODO CHANGE THIS
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def parse_power(filename: str, cpu: CPU) -> dict[str, int]:
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match cpu:
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case CPU.ALTRA:
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class Socket(Enum):
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SOCKET1 = 'Socket1'
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SOCKET2 = 'Socket2'
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data: dict[str, int] = {socket: list() for socket in Socket}
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baseline_data: dict[str, int] = dict()
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filename = os.path.splitext(filename)[0] + "_power" + os.path.splitext(filename)[1]
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with open(filename, 'r') as file:
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for line in file:
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regex = r'Start'
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value = re.search(regex, line)
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if value is not None:
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for socket in Socket:
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baseline_data[socket] = np.average(np.array(data[socket]))
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data[socket] = list()
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continue
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for socket in Socket:
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regex = r'^' + socket.value + r' (\d+\.\d+)'
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value = re.search(regex, line)
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if value is not None:
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data[socket].append(float(value.group(1)))
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break
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power_deltas: dict[str, int] = {
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socket: np.max(np.array(data[socket])) - baseline_data[socket]
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for socket in Socket
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}
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return {Stat.POWER_DELTA.value: max(power_deltas.values())}
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case CPU.XEON:
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return {}
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def derive_stats(data: dict[str, str | int | float]) -> dict[str, int | float]:
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result: dict[str, int | float] = dict()
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result[Stat.BRANCH_MISS_RATE.value] = data[Stat.BR_MISS.value] / data[Stat.BR.value]
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result[Stat.ITLB_MISS_RATE.value] = data[Stat.ITLB_MISS.value] / data[Stat.ITLB.value]
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result[Stat.DTLB_MISS_RATE.value] = data[Stat.DTLB_MISS.value] / data[Stat.DTLB.value]
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result[Stat.L1I_CACHE_MISS_RATE.value] = (
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data[Stat.L1I_CACHE_MISS.value] / data[Stat.L1I_CACHE.value]
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if Stat.L1I_CACHE_MISS.value in data and Stat.L1I_CACHE.value in data
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else None)
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result[Stat.L1D_CACHE_MISS_RATE.value] = (
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data[Stat.L1D_CACHE_MISS.value] / data[Stat.L1D_CACHE.value])
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result[Stat.L2D_CACHE_MISS_RATE.value] = (
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data[Stat.L2D_CACHE_MISS.value] / data[Stat.L2D_CACHE.value]
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if Stat.L2D_CACHE_MISS.value in data and Stat.L2D_CACHE.value in data
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else None)
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result[Stat.LL_CACHE_MISS_RATE.value] = (
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data[Stat.LL_CACHE_MISS.value] / data[Stat.LL_CACHE.value])
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return result
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def accumulate(stats_list: list[dict[str, str | int | float]], category: Stat, value: Stat):
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category_list = np.array([stats[category.value] for stats in stats_list if value.value in stats])
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value_list = np.array([stats[value.value] for stats in stats_list if value.value in stats])
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result: dict[np.ndarray] = dict()
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for category in np.sort(np.unique(category_list)):
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result[category] = value_list[category_list == category]
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return result
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def box_plot(ax, stats_list: list[dict[str, str | int | float]], x: Stat, y: Stat):
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data: dict[str, np.ndarray] = accumulate(stats_list, x, y)
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print("Plotted data: " + str(data))
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ax.boxplot(data.values(), tick_labels=data.keys())
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ax.set_ylabel(y.value)
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def line_plot(
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ax, stats_list: list[dict[str, str | int | float]],
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x: Stat, y: Stat, color: Stat
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):
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x_data: dict[str, np.ndarray] = accumulate(stats_list, color, x)
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y_data: dict[str, np.ndarray] = accumulate(stats_list, color, y)
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for category in x_data.keys():
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sorted_indices = np.argsort(x_data[category])
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x_data[category] = x_data[category][sorted_indices]
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y_data[category] = y_data[category][sorted_indices]
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ax.plot(x_data[category], y_data[category], label=category)
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print("Plotted x data: " + str(x_data[category]))
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print("Plotted y data: " + str(y_data[category]))
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ax.set_ylabel(y.value)
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ax.grid(True)
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def visualize(
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stats_list: list[dict[str, str | int | float]],
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plot: Plot,
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rows: int,
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size_multiplier: int,
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font_size: int,
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x: Stat,
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y: Stat,
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color: Stat,
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filter_list: list[str] = []
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):
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# Remove stats entries containing undesired values (like a specific CPU).
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stats_list = [stats for stats in stats_list
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if len([stats[key] for key in stats.keys()
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if stats[key] in filter_list]) == 0]
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#x = Stat.MAXWELL_SIZE
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#y = Stat.DTLB_MISS_RATE
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#color = Stat.SOLVER
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if y is None:
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ys = [stat for stat in Stat if stat.value in stats_list[0].keys()
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and stat is not x
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#and y != color
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#and y != marker
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and stat.value not in filter_list]
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fig, axes = plt.subplots(rows, int(math.ceil(len(ys) / rows)),
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figsize = (16 * size_multiplier, 9 * size_multiplier))
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match plot:
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case Plot.BOX:
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for i, y in enumerate(ys):
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box_plot(axes[i % rows][int(i / rows)], stats_list, x, y)
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case Plot.LINE:
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for i, y in enumerate(ys):
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line_plot(axes[i % rows][int(i / rows)], stats_list, x, y, color)
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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 Plot.BOX:
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box_plot(ax, stats_list, x, y)
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case Plot.LINE:
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line_plot(ax, stats_list, x, y, color)
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handles, labels = ax.get_legend_handles_labels()
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#box_plot(ax, stats, x, y)
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#line_plot(ax, stats, x, y, color)
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match plot:
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case Plot.BOX:
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title = f"{plot.value}_plot_of_{y.value.replace(' ', '_')}_vs_{x.value.replace(' ', '_')}_excluding_{filter_list}"
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case Plot.LINE:
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title = f"{plot.value}_plot_of_{y.value.replace(' ', '_')}_vs_{x.value.replace(' ', '_')}_by_{color.value.replace(' ', '_')}_excluding_{filter_list}"
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fig.suptitle(title, fontsize = font_size)
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fig.legend(handles, labels, fontsize = font_size)
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fig.supxlabel(x.value, fontsize = font_size)
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plt.savefig(title + ".png", dpi = 100)
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plt.show()
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def main():
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class Command(Enum):
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PARSE = 'parse'
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VISUALIZE = 'visualize'
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parser = argparse.ArgumentParser()
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parser.add_argument('command', choices=[x.value for x in Command])
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parser.add_argument('workload',
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choices=[x.name.lower() for x in Workload],
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help='the workload to analyze')
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parser.add_argument('filepath',
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help='the output for the ' + Command.PARSE.value + ' command or the input for the ' + Command.VISUALIZE.value + ' command')
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parser.add_argument('-i', '--input',
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help='the input directory for the parse command')
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parser.add_argument('-p', '--plot',
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choices=[x.name.lower() for x in Plot],
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help = 'the type of plot')
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parser.add_argument('-r', '--rows', type=int,
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help = 'the number of rows to display when -y is not specified',
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default = 5)
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parser.add_argument('-s', '--size', type=int,
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help = 'figure size multiplier',
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default = 4)
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parser.add_argument('-fs', '--font_size', type=int,
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help = 'font size',
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default = 40)
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parser.add_argument('-x',
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choices=[x.name.lower() for x in Stat],
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help = 'the name of the x axis')
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parser.add_argument('-y',
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choices=[x.name.lower() for x in Stat],
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help = 'the name of the y axis')
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parser.add_argument('-c', '--color',
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choices=[x.name.lower() for x in Stat],
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help = 'the name of the color')
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parser.add_argument('-f', '--filter', nargs = '+',
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help = 'a comma-separated string of names and values to filter out.',
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default = [])
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args = parser.parse_args()
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stats_list: list[dict] = list()
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if args.command == Command.PARSE.value:
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if (args.input) is None:
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print("An input directory is required with -i")
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exit(-1)
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original_dir = os.getcwd()
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os.chdir(args.input)
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for filename in os.listdir(os.getcwd()):
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if "output" not in filename:
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continue
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if "power" in filename:
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continue
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stats = parse_input(filename, Workload[args.workload.upper()])
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stats = stats | parse_output(filename, stats)
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stats = stats | derive_stats(stats)
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stats_list.append(stats)
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print(filename + " parsed.")
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os.chdir(original_dir)
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with open(args.filepath, 'w') as file:
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json.dump(stats_list, file, indent = 2)
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elif args.command == Command.VISUALIZE.value:
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with open(args.filepath, 'r') as file:
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stats_list = json.load(file)
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x = Stat[args.x.upper()] if args.x is not None else None
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y = Stat[args.y.upper()] if args.y is not None else None
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color = Stat[args.color.upper()] if args.color is not None else None
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visualize(stats_list, Plot[args.plot.upper()], args.rows, args.size, args.font_size, x, y, color, args.filter)
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if __name__ == '__main__':
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main()
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