Add all_facets & segment code in multiple functions
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f894161143
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@ -6,7 +6,6 @@
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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 argparse
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@ -84,10 +83,10 @@ stats = pd.read_csv(args.path + ".stats.csv",
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slice_mapping = pd.read_csv(args.path + ".slices.csv")
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core_mapping = pd.read_csv(args.path + ".cores.csv")
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print("core mapping:\n", core_mapping.to_string())
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print("slice mapping:\n", slice_mapping.to_string())
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# print("core mapping:\n", core_mapping.to_string())
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# print("slice mapping:\n", slice_mapping.to_string())
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#print("core {} is mapped to '{}'".format(4, repr(core_mapping.iloc[4])))
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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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@ -106,6 +105,8 @@ def plot(filename, g=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(img_dir+filename+".tex", axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
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print(filename, "saved")
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plt.close()
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plt.show()
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@ -123,12 +124,8 @@ stats["slice_group"] = stats["hash"].apply(lambda h: slice_mapping["slice_group"
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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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# 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.histplot, 'clflush_miss_n', bins=range(graph_lower_miss, graph_upper_miss), color="b", edgecolor="b", alpha=0.2)
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plot("medians_miss_grid.png", g=g_)
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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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@ -138,10 +135,10 @@ plot("medians_miss_grid.png", g=g_)
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#
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print(stats.head())
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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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# print("Found {}".format(num_core))
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def miss_topology(main_core_fixed, slice_group, C, h):
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@ -151,16 +148,9 @@ 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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@ -178,11 +168,9 @@ def exclusive_hit_topology_gpu(main_core, slice_group, helper_core, C, h1, h2):
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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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@ -200,11 +188,9 @@ def exclusive_hit_topology_gpu2(main_core, slice_group, helper_core, C, h1, h2):
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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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@ -223,32 +209,15 @@ def exclusive_hit_topology_nogpu(main_core, slice_group, helper_core, C, h1, h2)
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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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@ -256,67 +225,48 @@ def shared_hit_topology_1(x, C, h):
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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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def do_predictions(df):
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res_miss = optimize.curve_fit(miss_topology_df, df[["main_core_fixed", "slice_group"]], df["clflush_miss_n"])
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# print("Miss topology:")
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# print(res_miss)
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stats["predicted_miss"] = miss_topology_df(stats, *(res_miss[0]))
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res_gpu = optimize.curve_fit(exclusive_hit_topology_gpu_df, df[["main_core_fixed", "slice_group", "helper_core_fixed"]], df["clflush_remote_hit"])
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# print("Exclusive hit topology (GPU):")
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# print(res_gpu)
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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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#res_gpu2 = optimize.curve_fit(exclusive_hit_topology_gpu2_df, df[["main_core_fixed", "slice_group", "helper_core_fixed"]], df["clflush_remote_hit"])
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#print("Exclusive hit topology (GPU2):")
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#print(res_gpu2)
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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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plot("medians_miss.png")
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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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#res_no_gpu = optimize.curve_fit(exclusive_hit_topology_nogpu_df, df[["main_core_fixed", "slice_group", "helper_core_fixed"]], df["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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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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plot("medians_remote_hit.png")
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# df["predicted_miss"] = miss_topology_df(df, *(res_miss[0]))
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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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plot("medians_miss_v_localhit_core.png", g=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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plot("medians_miss_v_localhit_slice.png", g=g0)
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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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plot("medians_remote_hit_grid.png", g=g2)
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#df["predicted_remote_hit_no_gpu"] = exclusive_hit_topology_nogpu_df(df, *(res_no_gpu[0]))
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df["predicted_remote_hit_gpu"] = exclusive_hit_topology_gpu_df(df, *(res_gpu[0]))
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#df["predicted_remote_hit_gpu2"] = exclusive_hit_topology_gpu_df(df, *(res_gpu2[0]))
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if args.rslice:
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df_A0 = df[df["main_core_fixed"] == 0]
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figure_median_E_A0 = sns.FacetGrid(df_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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plot("medians_remote_hit.png")
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g2 = sns.FacetGrid(df, 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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plot("medians_remote_hit_grid.png", g=g2)
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def rslice():
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for core in stats["main_core_fixed"].unique():
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os.makedirs(img_dir+f"slices{core}", exist_ok=True)
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for slice in stats["slice_group"].unique():
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@ -327,8 +277,47 @@ if args.rslice:
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plt.close()
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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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plot("medians_sharedhit.png", g=g3)
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# more ideas needed
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def facet_grid(
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df, row, col, third,
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draw_fn=sns.scatterplot,
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shown=["clflush_shared_hit", "clflush_remote_hit", "clflush_local_hit_n", "clflush_miss_n"],
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colors=["y", "r", "g", "b"],
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title=None
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):
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"""
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Creates a facet grid showing all points
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"""
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grid = sns.FacetGrid(df, row=row, col=col)
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for i, el in enumerate(shown):
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grid.map(draw_fn, third, el, color=colors[i%len(colors)])
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if title is not None:
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plot(title, g=grid)
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else:
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print("Hey, title is None, are U sure ?")
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return grid
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def all_facets(df, id, *args, **kwargs):
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"""
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df : panda dataframe
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id: the str to append to filenames
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"""
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facet_grid(df, "main_core_fixed", "helper_core_fixed", "slice_group", title=f"medians_facet_{id}s.png", *args, **kwargs)
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facet_grid(df, "helper_core_fixed", "slice_group", "main_core_fixed", title=f"medians_facet_{id}c.png", *args, **kwargs)
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facet_grid(df, "slice_group", "main_core_fixed", "helper_core_fixed", title=f"medians_facet_{id}h.png", *args, **kwargs)
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if args.rslice:
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rslice()
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do_predictions(stats)
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all_facets(stats, "")
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for main in (0, 1):
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for helper in (0, 1):
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print(f"Doing all facets {main}x{helper}")
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filtered_df = stats[(stats["main_core_fixed"]//(num_core/2) == main) & (stats["helper_core_fixed"]//(num_core/2) == helper)]
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all_facets(filtered_df, f"m{main}h{helper}_")
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