141 lines
3.5 KiB
Python
Executable File
141 lines
3.5 KiB
Python
Executable File
#!/bin/env python
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__author__ = 'dongyun.zdy'
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import math
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import numpy as np
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from scipy.optimize import leastsq
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from scipy.optimize import curve_fit
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import sys
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from lmfit import Model
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import getopt
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def get_row_size(col):
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size = 16
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size += col * (3 + 8 + 4 + 8 + 16 + 32 + 64 + 128)
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size += col
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return size
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def round_wasted_spave(rsize, psize):
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nr = math.floor(float(psize / rsize))
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waste = psize - nr * rsize
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return rsize + waste / nr
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def get_miss_prob(Nrow, Ncol, Turn):
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total_size = Nrow * get_row_size(Ncol)
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TLBcovered = Turn
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if TLBcovered >= 0.9 * total_size:
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hit = 0.9
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else:
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hit = TLBcovered / total_size
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return 1 - hit
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def sort_model_form(args,
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Tmiss,
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Turn
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):
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(
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Nrow,
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Ncol,
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) = args
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total_cost = 0
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total_cost += Nrow * Tmiss * Ncol * get_miss_prob(Nrow, Ncol, Turn)
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return total_cost
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def sort_model_arr(arg_sets,
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Tmiss,
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Turn,
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):
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res = []
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for single_arg_set in arg_sets:
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res.append(sort_model_form(single_arg_set,
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Tmiss,
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Turn,
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))
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return np.array(res)
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sort_model = Model(sort_model_arr)
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sort_model.set_param_hint("Tmiss", min=0.0)
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sort_model.set_param_hint("Turn", min=2097152.0, max=2097153.0)
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# sort_model.set_param_hint("Tmiss_K2", min=0.0)
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def extract_info_from_line(line):
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splited = line.split(",")
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line_info = []
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for item in splited:
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line_info.append(float(item))
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return line_info
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if __name__ == '__main__':
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file_name = "miss.prep.1"
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out_file_name = "miss.model"
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# sys.argv.extend("-i sort.prep.bigint -o sort.model".split(" "))
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output_fit_res = False
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wrong_arg = False
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opts,args = getopt.getopt(sys.argv[1:],"i:o:R:C:")
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for op, value in opts:
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if "-i" == op:
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file_name = value
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elif "-o" == op:
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output_fit_res = True
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out_file_name = value
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elif "-R" == op:
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MATERIAL_ROW_ONCE = float(value)
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elif "-C" == op:
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MATERIAL_ROW_COL = float(value)
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else:
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wrong_arg = True
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if wrong_arg:
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print "wrong arg"
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sys.exit(1)
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file = open(file_name, "r")
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arg_sets = []
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times = []
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case_params = []
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for line in file:
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if line.startswith('#'):
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continue
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case_param = extract_info_from_line(line)
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case_params.append(case_param)
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arg_sets.append((case_param[0], case_param[1]))
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times.append(case_param[3])
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file.close()
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arg_sets_np = np.array(arg_sets)
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times_np = np.array(times)
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#10, 0.20406430879623488, 0.016618100054245379, 14.0, 4.5, 37.0, -0.005, 0.5, -7.0
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result = sort_model.fit(times_np, arg_sets=arg_sets_np,
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Tmiss=1.0,
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Turn=2097152,
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)
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Tmiss = result.best_values["Tmiss"]
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Turn = result.best_values["Turn"]
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res_line = str(Tmiss) + ","
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res_line += str(Turn)
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# res_line += str(result.best_values["Tmiss_K2"])
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print result.fit_report()
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if output_fit_res:
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out_file = open(out_file_name, "w")
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out_file.write(res_line)
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out_file.close()
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for i, args in enumerate(arg_sets):
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cost = sort_model_form(args, Tmiss, Turn)
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time = times[i]
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print "\t".join([str(args), str(time), str(cost)])
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