Remove show_models.py
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"""
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Try some models and see what they look like
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Following this die could help https://en.wikichip.org/w/images/4/48/E5_v4_LCC.png
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Using the following naming convention:
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------
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0 7
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1 6
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2 5
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3 4
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------
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"""
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import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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import numpy as np
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import itertools
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import os
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nb_cores = 8
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nb_slices = 8
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num_core = nb_cores
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cores = list(range(nb_cores))
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slices = list(range(nb_slices))
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img_dir = os.getenv("PWD")+"/"
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def plot(filename, g=None):
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if g is not None:
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g.savefig(img_dir + filename)
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else:
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plt.savefig(img_dir + filename)
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# tikzplotlib.save(
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# img_dir+filename+".tex",
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# axis_width=r'0.175\textwidth',
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# axis_height=r'0.25\textwidth'
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# )
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print(img_dir + filename, "saved")
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plt.close()
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def ring_distance(x0, x1):
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"""
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return (a, b) where `a` is the core distance and `b` the larger "ring step"
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it is possible that going from 0->7 costs one more than 3->4
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"""
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dist = abs(x0-x1)
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if x0 // (num_core/2) != x1 // (num_core/2):
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# côté du coeur différent
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return min((num_core-1-dist, 2), (dist-1, 1))
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else:
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return dist, 0
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def slice_msg_distance(source, dest):
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"""
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Si l'expéditeur est à l'extrémité d'une des lignes, il envoie toujours dans le même sens
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(vers toute sa ligne d'abord), sinon, il prend le chemin le plus court
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le bonus correspond au fait que 0->7 puisse coûter 1 de plus que 3->4
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"""
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dist = abs(source-dest)
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if source // (num_core/2) == dest // (num_core/2):
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return (dist, 0)
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# Pour aller de l'autre côté
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up, down = (num_core-1-dist, 2), (dist-1, 1)
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if source in [0, 7]:
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return down
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if source in [3, 4] or source in [2, 5]:
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return up
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if source in [1, 6]:
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return min(up, down)
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raise IndexError
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def ha_dist(core, is_QPI):
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"""
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distance to Home Agent
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"""
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if is_QPI:
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if core < 4:
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return core, 0
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return 7-core, 1 # +1 for PCI
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if core < 4:
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return 3-core, 0
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return core-4, 0
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def cclockwise_dist(source, dest):
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base = (dest+8-source)%8
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side_jump = 0
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if source < 4 and dest >= 4:
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side_jump = 1
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elif source >= 4 and dest < 4:
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side_jump = 2
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return base, side_jump
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def cclockwise_ha_dist(core, is_QPI):
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"""
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counter-clockwise distance to Home Agent
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"""
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if is_QPI:
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return cclockwise_dist(core, 7)
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return cclockwise_dist(core, 3)
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def no_QPI_dist(source, dest):
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"""
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Path not using QPI hop
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"""
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return abs(source-dest), 1 if source // 4 != dest //4 else 0
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def miss():
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"""
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- ini : initial cost
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- core_step : cost to go from one core to the following (eg 0 to 1)
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- ring_step : cost to go from one line to the other (eg 0 to 7)
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Issue: on a same socket, we observe always the first 4 or last 4 patterns, but not mixed
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"""
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def miss_topo(main_core, slice, i, c, l, k):
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x, y, z = slice_msg_distance(main_core, slice)
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return i+c*x+l*y+k*z
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ini, core_step, ring_step, other_ring_step = 4, 1, 5, 2
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fig, axs = plt.subplots(nrows=1, ncols=8, figsize=(15, 5))
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for i, slice in enumerate(range(8)):
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axs[i].plot(cores, [miss_topo(main_core, slice, ini, core_step, ring_step, other_ring_step) for main_core in cores], "ro")
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axs[i].set_title(f"slice_group = {slice}")
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axs[i].set_ylabel("clflush_miss_n")
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axs[i].set_xlabel("main_core_fixed")
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axs[i].set_ylim([0, 25])
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plt.tight_layout()
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plt.show()
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def hit():
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"""
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- ini : initial cost
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- core_step : cost to go from one core to the following (eg 0 to 1)
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- ring_step : cost to go from one line to the other (eg 0 to 7)
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Issue: on a same socket, we observe always the first 4 or last 4 patterns, but not mixed
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"""
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def hit_topo(main, helper, slice_g, i, c, l):
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helper = helper%8
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main_slice_local = slice_msg_distance(slice_g, main)
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slice_QPI = cclockwise_dist(0, slice_g) # clockwise
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QPI_slice_r = cclockwise_dist(0, slice_g)
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slice_r_helper = slice_msg_distance(slice_g, helper)
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costs = (main_slice_local[0]+slice_QPI[0]+QPI_slice_r[0]+slice_r_helper[0], main_slice_local[1]+slice_QPI[1]+QPI_slice_r[1]+slice_r_helper[1])
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return ini+costs[0]*c+costs[1]*l
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ini, core_step, ring_step = 12, 1, 1.5
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fig, axs = plt.subplots(nrows=1, ncols=8, figsize=(15, 5))
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# Define the ranges for x, y, z
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main = range(8)
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helper = range(8, 16)
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slice_g = range(8)
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# Create a DataFrame with all combinations of x, y, and z
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data = pd.DataFrame([
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(x, y, z) for z in slice_g
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for x, y in itertools.product(main, helper)
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],
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columns=['main', 'helper', 'slice_group']
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)
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# Define the function
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def my_function(x):
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return hit_topo(x["main"], x["helper"], x["slice_group"], ini, core_step, ring_step)
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# Apply the function to create a new column
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data['predicted_hit'] = data.apply(my_function, axis=1)
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fig = sns.FacetGrid(data, col="main", row="helper")
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fig.map(sns.scatterplot, "slice_group", "predicted_hit", color="r", marker="+")
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fig.map(sns.scatterplot, "slice_group", "predicted_hit", color="r", marker="x")
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fig.set_titles(col_template="$A$ = {col_name}", row_template="$V$ = {row_name}")
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plot("model_hit.png", g=fig)
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hit()
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