282 lines
10 KiB
Python
282 lines
10 KiB
Python
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# SPDX-FileCopyrightText: 2021 Guillaume DIDIER
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#
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-License-Identifier: MIT
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sys import exit
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import numpy as np
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from scipy import optimize
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import sys
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# TODO
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# sys.argv[1] should be the root
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# with root-result_lite.csv.bz2 the result
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# and .stats.csv
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# root.slices a slice mapping - done
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# root.cores a core + socket mapping - done -> move to analyse csv ?
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#
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# Facet plot with actual dot cloud + plot the linear regression
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# each row is a slice
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# each row is an origin core
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# each column a helper core if applicable
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stats = pd.read_csv(sys.argv[1] + ".stats.csv",
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dtype={
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"main_core": np.int8,
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"helper_core": np.int8,
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# "address": int,
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"hash": np.int8,
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# "time": np.int16,
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"clflush_remote_hit": np.float64,
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"clflush_shared_hit": np.float64,
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# "clflush_miss_f": np.int32,
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# "clflush_local_hit_f": np.int32,
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"clflush_miss_n": np.float64,
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"clflush_local_hit_n": np.float64,
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# "reload_miss": np.int32,
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# "reload_remote_hit": np.int32,
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# "reload_shared_hit": np.int32,
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# "reload_local_hit": np.int32
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}
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)
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slice_mapping = pd.read_csv(sys.argv[1] + ".slices.csv")
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core_mapping = pd.read_csv(sys.argv[1] + ".cores.csv")
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print(core_mapping.to_string())
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print(slice_mapping.to_string())
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print("core {} is mapped to '{}'".format(4, repr(core_mapping.iloc[4])))
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min_time_miss = stats["clflush_miss_n"].min()
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max_time_miss = stats["clflush_miss_n"].max()
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def remap_core(key):
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def remap(core):
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remapped = core_mapping.iloc[core]
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return remapped[key]
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return remap
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stats["main_socket"] = stats["main_core"].apply(remap_core("socket"))
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stats["main_core_fixed"] = stats["main_core"].apply(remap_core("core"))
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stats["main_ht"] = stats["main_core"].apply(remap_core("hthread"))
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stats["helper_socket"] = stats["helper_core"].apply(remap_core("socket"))
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stats["helper_core_fixed"] = stats["helper_core"].apply(remap_core("core"))
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stats["helper_ht"] = stats["helper_core"].apply(remap_core("hthread"))
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# slice_mapping = {3: 0, 1: 1, 2: 2, 0: 3}
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stats["slice_group"] = stats["hash"].apply(lambda h: slice_mapping["slice_group"].iloc[h])
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graph_lower_miss = int((min_time_miss // 10) * 10)
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graph_upper_miss = int(((max_time_miss + 9) // 10) * 10)
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print("Graphing from {} to {}".format(graph_lower_miss, graph_upper_miss))
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g_ = sns.FacetGrid(stats, col="main_core_fixed", row="slice_group")
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g_.map(sns.distplot, 'clflush_miss_n', bins=range(graph_lower_miss, graph_upper_miss), color="b")
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#g.map(sns.scatterplot, 'slice_group', 'clflush_local_hit_n', color="g")
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plt.show()
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# also explains remote
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# shared needs some thinking as there is something weird happening there.
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#
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# M 0 1 2 3 4 5 6 7
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#
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print(stats.head())
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num_core = len(stats["main_core_fixed"].unique())
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print("Found {}".format(num_core))
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def miss_topology(main_core_fixed, slice_group, C, h):
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return C + h * abs(main_core_fixed - slice_group) + h * abs(slice_group + 1)
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def miss_topology_df(x, C, h):
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return x.apply(lambda x, C, h: miss_topology(x["main_core_fixed"], x["slice_group"], C, h), args=(C, h), axis=1)
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res_miss = optimize.curve_fit(miss_topology_df, stats[["main_core_fixed", "slice_group"]], stats["clflush_miss_n"])
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print("Miss topology:")
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print(res_miss)
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memory = -1
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gpu_if_any = num_core
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def exclusive_hit_topology_gpu(main_core, slice_group, helper_core, C, h1, h2):
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round_trip = gpu_if_any - memory
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if slice_group <= num_core/2:
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# send message towards higher cores first
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if helper_core < slice_group:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(round_trip - (helper_core - memory))
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else:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
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else:
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# send message toward lower cores first
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if helper_core > slice_group:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - memory)
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else:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
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return r
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def exclusive_hit_topology_gpu_df(x, C, h1, h2):
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return x.apply(lambda x, C, h1, h2: exclusive_hit_topology_gpu(x["main_core_fixed"], x["slice_group"], x["helper_core_fixed"], C, h1, h2), args=(C, h1, h2), axis=1)
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def exclusive_hit_topology_gpu2(main_core, slice_group, helper_core, C, h1, h2):
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round_trip = gpu_if_any + 1 - memory
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if slice_group <= num_core/2:
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# send message towards higher cores first
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if helper_core < slice_group:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(round_trip - (helper_core - memory))
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else:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
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else:
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# send message toward lower cores first
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if helper_core > slice_group:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - memory)
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else:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
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return r
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def exclusive_hit_topology_gpu2_df(x, C, h1, h2):
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return x.apply(lambda x, C, h1, h2: exclusive_hit_topology_gpu2(x["main_core_fixed"], x["slice_group"], x["helper_core_fixed"], C, h1, h2), args=(C, h1, h2), axis=1)
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# unlikely
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def exclusive_hit_topology_nogpu(main_core, slice_group, helper_core, C, h1, h2):
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round_trip = (num_core-1) - memory
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if slice_group <= num_core/2:
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# send message towards higher cores first
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if helper_core < slice_group:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(round_trip - (helper_core - memory))
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else:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
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else:
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# send message toward lower cores first
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if helper_core > slice_group:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - memory)
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else:
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r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
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return r
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def exclusive_hit_topology_nogpu_df(x, C, h1, h2):
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return x.apply(lambda x, C, h1, h2: exclusive_hit_topology_nogpu(x["main_core_fixed"], x["slice_group"], x["helper_core_fixed"], C, h1, h2), args=(C, h1, h2), axis=1)
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#res_no_gpu = optimize.curve_fit(exclusive_hit_topology_nogpu_df, stats[["main_core_fixed", "slice_group", "helper_core_fixed"]], stats["clflush_remote_hit"])
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#print("Exclusive hit topology (No GPU):")
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#print(res_no_gpu)
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res_gpu = optimize.curve_fit(exclusive_hit_topology_gpu_df, stats[["main_core_fixed", "slice_group", "helper_core_fixed"]], stats["clflush_remote_hit"])
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print("Exclusive hit topology (GPU):")
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print(res_gpu)
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#res_gpu2 = optimize.curve_fit(exclusive_hit_topology_gpu2_df, stats[["main_core_fixed", "slice_group", "helper_core_fixed"]], stats["clflush_remote_hit"])
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#print("Exclusive hit topology (GPU2):")
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#print(res_gpu2)
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def remote_hit_topology_2(x, C, h):
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main_core = x["main_core_fixed"]
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slice_group = x["slice_group"]
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helper_core = x["helper_core_fixed"]
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return C + h * abs(main_core - slice_group) + h * abs(slice_group - helper_core) + h * abs(helper_core - main_core)
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def shared_hit_topology_1(x, C, h):
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main_core = x["main_core_fixed"]
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slice_group = x["slice_group"]
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helper_core = x["helper_core_fixed"]
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return C + h * abs(main_core - slice_group) + h * max(abs(slice_group - main_core), abs(slice_group - helper_core))
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def plot_func(function, *params):
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def plot_it(x, **kwargs):
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# plot_x = []
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# plot_y = []
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# for x in set(x):
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# plot_y.append(function(x, *params))
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# plot_x = x
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print(x)
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plot_y = function(x, *params)
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sns.lineplot(x, plot_y, **kwargs)
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return plot_it
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stats["predicted_miss"] = miss_topology_df(stats, *(res_miss[0]))
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figure_median_I = sns.FacetGrid(stats, col="main_core_fixed")
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figure_median_I.map(sns.scatterplot, 'slice_group', 'clflush_miss_n', color="b")
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figure_median_I.map(sns.lineplot, 'slice_group', 'predicted_miss', color="b")
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figure_median_I.set_titles(col_template="$A$ = {col_name}")
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figure_median_I.tight_layout()
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import tikzplotlib
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tikzplotlib.save("fig-median-I.tex", axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
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plt.show()
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#stats["predicted_remote_hit_no_gpu"] = exclusive_hit_topology_nogpu_df(stats, *(res_no_gpu[0]))
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stats["predicted_remote_hit_gpu"] = exclusive_hit_topology_gpu_df(stats, *(res_gpu[0]))
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#stats["predicted_remote_hit_gpu2"] = exclusive_hit_topology_gpu_df(stats, *(res_gpu2[0]))
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stats_A0 = stats[stats["main_core_fixed"] == 0]
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figure_median_E_A0 = sns.FacetGrid(stats_A0, col="slice_group")
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figure_median_E_A0.map(sns.scatterplot, 'helper_core_fixed', 'clflush_remote_hit', color="r")
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figure_median_E_A0.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu', color="r")
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figure_median_E_A0.set_titles(col_template="$S$ = {col_name}")
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tikzplotlib.save("fig-median-E-A0.tex", axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
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plt.show()
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g = sns.FacetGrid(stats, row="main_core_fixed")
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g.map(sns.scatterplot, 'slice_group', 'clflush_miss_n', color="b")
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g.map(sns.scatterplot, 'slice_group', 'clflush_local_hit_n', color="g")
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g0 = sns.FacetGrid(stats, row="slice_group")
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g0.map(sns.scatterplot, 'main_core_fixed', 'clflush_miss_n', color="b")
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g0.map(sns.scatterplot, 'main_core_fixed', 'clflush_local_hit_n', color="g") # this gives away the trick I think !
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# possibility of sending a general please discard this everyone around one of the ring + wait for ACK - direction depends on the core.
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g2 = sns.FacetGrid(stats, row="main_core_fixed", col="slice_group")
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g2.map(sns.scatterplot, 'helper_core_fixed', 'clflush_remote_hit', color="r")
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g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu', color="r")
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#g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu2', color="g")
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#g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_no_gpu', color="g")
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#g2.map(plot_func(exclusive_hit_topology_nogpu_df, *(res_no_gpu[0])), 'helper_core_fixed', color="g")
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g3 = sns.FacetGrid(stats, row="main_core_fixed", col="slice_group")
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g3.map(sns.scatterplot, 'helper_core_fixed', 'clflush_shared_hit', color="y")
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# more ideas needed
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plt.show()
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