Add all_facets
Segment code in multiple functions Reformat with black
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@ -3,19 +3,19 @@
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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 argparse
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import sys
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import os
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import sys
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import argparse
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import warnings
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warnings.filterwarnings('ignore')
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from scipy import optimize
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import matplotlib.pyplot as plt
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warnings.filterwarnings("ignore")
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print("warnings are filtered, enable them back if you are having some trouble")
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# TODO
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@ -41,7 +41,7 @@ parser.add_argument(
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dest="no_plot",
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action="store_true",
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default=False,
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help="No visible plot (save figures to files)"
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help="No visible plot (save figures to files)",
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)
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parser.add_argument(
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@ -49,45 +49,46 @@ parser.add_argument(
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dest="rslice",
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action="store_true",
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default=False,
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help="Create slice{} directories with segmented grid"
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help="Create slice{} directories with segmented grid",
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)
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args = parser.parse_args()
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img_dir = os.path.dirname(args.path)+"/figs/"
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img_dir = os.path.dirname(args.path) + "/figs/"
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os.makedirs(img_dir, exist_ok=True)
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assert os.path.exists(args.path + ".stats.csv")
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assert os.path.exists(args.path + ".slices.csv")
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assert os.path.exists(args.path + ".cores.csv")
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stats = pd.read_csv(args.path + ".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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stats = pd.read_csv(
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args.path + ".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(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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@ -100,15 +101,23 @@ def remap_core(key):
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return remap
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def plot(filename, g=None):
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if args.no_plot:
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if g is not None:
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g.savefig(img_dir+filename)
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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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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(filename, "saved")
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plt.close()
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plt.show()
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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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@ -118,17 +127,15 @@ 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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stats["slice_group"] = stats["hash"].apply(
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lambda h: slice_mapping["slice_group"].iloc[h]
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)
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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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@ -138,23 +145,19 @@ 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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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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func = lambda x, C, h: miss_topology(x["main_core_fixed"], x["slice_group"], C, h)
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return x.apply(func, args=(C, h), axis=1)
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memory = -1
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@ -164,171 +167,269 @@ 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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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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r = (
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C
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+ h1 * abs(main_core - slice_group)
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+ h2 * abs(round_trip - (helper_core - memory))
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)
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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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r = (
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C
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+ h1 * abs(main_core - slice_group)
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+ h2 * abs(helper_core - slice_group)
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)
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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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r = (
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C
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+ h1 * abs(main_core - slice_group)
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+ h2 * abs(helper_core - slice_group)
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)
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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 func(x, C, h1, h2):
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return exclusive_hit_topology_gpu(
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x["main_core_fixed"], x["slice_group"], x["helper_core_fixed"], C, h1, h2
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)
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return x.apply(func, 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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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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r = (
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C
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+ h1 * abs(main_core - slice_group)
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+ h2 * abs(round_trip - (helper_core - memory))
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)
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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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r = (
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C
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+ h1 * abs(main_core - slice_group)
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+ h2 * abs(helper_core - slice_group)
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)
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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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r = (
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C
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+ h1 * abs(main_core - slice_group)
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+ h2 * abs(helper_core - slice_group)
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)
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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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def func(x, C, h1, h2):
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return exclusive_hit_topology_gpu2(
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x["main_core_fixed"], x["slice_group"], x["helper_core_fixed"], C, h1, h2
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)
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return x.apply(func, 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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round_trip = (num_core - 1) - memory
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if slice_group <= num_core/2:
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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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r = (
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C
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+ h1 * abs(main_core - slice_group)
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+ h2 * abs(round_trip - (helper_core - memory))
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)
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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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r = (
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C
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+ h1 * abs(main_core - slice_group)
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+ h2 * abs(helper_core - slice_group)
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)
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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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r = (
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C
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+ h1 * abs(main_core - slice_group)
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+ h2 * abs(helper_core - slice_group)
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)
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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 func(x, C, h1, h2):
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return exclusive_hit_topology_nogpu(
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x["main_core_fixed"], x["slice_group"], x["helper_core_fixed"], C, h1, h2
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)
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return x.apply(func, args=(C, h1, h2), axis=1)
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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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return (
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C
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+ h * abs(main_core - slice_group)
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+ h * abs(slice_group - helper_core)
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+ h * abs(helper_core - main_core)
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)
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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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return (
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C
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+ h * abs(main_core - slice_group)
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+ h * max(abs(slice_group - main_core), abs(slice_group - helper_core))
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)
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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(
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miss_topology_df, df[["main_core_fixed", "slice_group"]], df["clflush_miss_n"]
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)
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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(
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exclusive_hit_topology_gpu_df,
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df[["main_core_fixed", "slice_group", "helper_core_fixed"]],
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df["clflush_remote_hit"],
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)
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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(
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# exclusive_hit_topology_gpu2_df,
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# df[["main_core_fixed", "slice_group", "helper_core_fixed"]],
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# df["clflush_remote_hit"]
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# )
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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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# res_no_gpu = optimize.curve_fit(
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# exclusive_hit_topology_nogpu_df,
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# df[["main_core_fixed", "slice_group", "helper_core_fixed"]],
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# df["clflush_remote_hit"]
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# )
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# print("Exclusive hit topology (No GPU):")
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# print(res_no_gpu)
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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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df["predicted_miss"] = miss_topology_df(df, *(res_miss[0]))
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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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# df["predicted_remote_hit_no_gpu"] = exclusive_hit_topology_nogpu_df(df, *(res_no_gpu[0]))
|
||||
df["predicted_remote_hit_gpu"] = exclusive_hit_topology_gpu_df(df, *(res_gpu[0]))
|
||||
# df["predicted_remote_hit_gpu2"] = exclusive_hit_topology_gpu_df(df, *(res_gpu2[0]))
|
||||
|
||||
df_A0 = df[df["main_core_fixed"] == 0]
|
||||
figure_A0 = sns.FacetGrid(df_A0, col="slice_group")
|
||||
figure_A0.map(sns.scatterplot, "helper_core_fixed", "clflush_remote_hit", color="r")
|
||||
figure_A0.map(
|
||||
sns.lineplot, "helper_core_fixed", "predicted_remote_hit_gpu", color="r"
|
||||
)
|
||||
figure_A0.set_titles(col_template="$S$ = {col_name}")
|
||||
plot("medians_remote_hit.png")
|
||||
|
||||
g2 = sns.FacetGrid(df, row="main_core_fixed", col="slice_group")
|
||||
g2.map(sns.scatterplot, "helper_core_fixed", "clflush_remote_hit", color="r")
|
||||
g2.map(sns.lineplot, "helper_core_fixed", "predicted_remote_hit_gpu", color="r")
|
||||
# g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu2', color="g")
|
||||
# g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_no_gpu', color="g")
|
||||
plot("medians_remote_hit_grid.png", g=g2)
|
||||
|
||||
|
||||
stats_A0 = stats[stats["main_core_fixed"] == 0]
|
||||
figure_median_E_A0 = sns.FacetGrid(stats_A0, col="slice_group")
|
||||
figure_median_E_A0.map(sns.scatterplot, 'helper_core_fixed', 'clflush_remote_hit', color="r")
|
||||
figure_median_E_A0.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu', color="r")
|
||||
figure_median_E_A0.set_titles(col_template="$S$ = {col_name}")
|
||||
|
||||
# tikzplotlib.save("fig-median-E-A0.tex", axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
|
||||
plot("medians_remote_hit.png")
|
||||
|
||||
|
||||
g = sns.FacetGrid(stats, row="main_core_fixed")
|
||||
g.map(sns.scatterplot, 'slice_group', 'clflush_miss_n', color="b")
|
||||
g.map(sns.scatterplot, 'slice_group', 'clflush_local_hit_n', color="g")
|
||||
plot("medians_miss_v_localhit_core.png", g=g)
|
||||
|
||||
g0 = sns.FacetGrid(stats, row="slice_group")
|
||||
g0.map(sns.scatterplot, 'main_core_fixed', 'clflush_miss_n', color="b")
|
||||
g0.map(sns.scatterplot, 'main_core_fixed', 'clflush_local_hit_n', color="g") # this gives away the trick I think !
|
||||
# possibility of sending a general please discard this everyone around one of the ring + wait for ACK - direction depends on the core.
|
||||
plot("medians_miss_v_localhit_slice.png", g=g0)
|
||||
|
||||
g2 = sns.FacetGrid(stats, row="main_core_fixed", col="slice_group")
|
||||
g2.map(sns.scatterplot, 'helper_core_fixed', 'clflush_remote_hit', color="r")
|
||||
g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu', color="r")
|
||||
#g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu2', color="g")
|
||||
#g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_no_gpu', color="g")
|
||||
#g2.map(plot_func(exclusive_hit_topology_nogpu_df, *(res_no_gpu[0])), 'helper_core_fixed', color="g")
|
||||
plot("medians_remote_hit_grid.png", g=g2)
|
||||
|
||||
|
||||
if args.rslice:
|
||||
def rslice():
|
||||
for core in stats["main_core_fixed"].unique():
|
||||
os.makedirs(img_dir+f"slices{core}", exist_ok=True)
|
||||
for slice in stats["slice_group"].unique():
|
||||
df = stats[(stats["slice_group"] == slice) & (stats["main_core_fixed"] == core)]
|
||||
fig = sns.scatterplot(df, x="helper_core_fixed", y="clflush_remote_hit", color="r")
|
||||
fig.set(title=f"main_core={core} slice={slice}")
|
||||
plt.savefig(img_dir+f"slices{core}/"+str(slice)+".png")
|
||||
os.makedirs(img_dir + f"slices{core}", exist_ok=True)
|
||||
for slice_ in stats["slice_group"].unique():
|
||||
df = stats[
|
||||
(stats["slice_group"] == slice_) & (stats["main_core_fixed"] == core)
|
||||
]
|
||||
fig = sns.scatterplot(
|
||||
df, x="helper_core_fixed", y="clflush_remote_hit", color="r"
|
||||
)
|
||||
fig.set(title=f"main_core={core} slice={slice_}")
|
||||
plt.savefig(img_dir + f"slices{core}/" + str(slice_) + ".png")
|
||||
plt.close()
|
||||
|
||||
|
||||
g3 = sns.FacetGrid(stats, row="main_core_fixed", col="slice_group")
|
||||
g3.map(sns.scatterplot, 'helper_core_fixed', 'clflush_shared_hit', color="y")
|
||||
plot("medians_sharedhit.png", g=g3)
|
||||
# more ideas needed
|
||||
def facet_grid(
|
||||
df, row, col, third,
|
||||
draw_fn=sns.scatterplot,
|
||||
shown=[
|
||||
"clflush_shared_hit",
|
||||
"clflush_remote_hit",
|
||||
"clflush_local_hit_n",
|
||||
"clflush_miss_n",
|
||||
],
|
||||
colors=["y", "r", "g", "b"],
|
||||
title=None,
|
||||
):
|
||||
"""
|
||||
Creates a facet grid showing all points
|
||||
"""
|
||||
grid = sns.FacetGrid(df, row=row, col=col)
|
||||
|
||||
for i, el in enumerate(shown):
|
||||
grid.map(draw_fn, third, el, color=colors[i % len(colors)])
|
||||
|
||||
if title is not None:
|
||||
plot(title, g=grid)
|
||||
return grid
|
||||
|
||||
|
||||
def all_facets(df, id_, *args, **kwargs):
|
||||
"""
|
||||
df : panda dataframe
|
||||
id_: the str to append to filenames
|
||||
"""
|
||||
|
||||
facet_grid(
|
||||
df, "main_core_fixed", "helper_core_fixed", "slice_group",
|
||||
title=f"medians_facet_{id_}s.png", *args, **kwargs
|
||||
)
|
||||
facet_grid(
|
||||
df, "helper_core_fixed", "slice_group", "main_core_fixed",
|
||||
title=f"medians_facet_{id}c.png", *args, **kwargs
|
||||
)
|
||||
facet_grid(
|
||||
df, "slice_group", "main_core_fixed", "helper_core_fixed",
|
||||
title=f"medians_facet_{id}h.png", *args, **kwargs
|
||||
)
|
||||
|
||||
|
||||
if args.rslice:
|
||||
rslice()
|
||||
|
||||
do_predictions(stats)
|
||||
all_facets(stats, "")
|
||||
|
||||
for main in (0, 1):
|
||||
for helper in (0, 1):
|
||||
print(f"Doing all facets {main}x{helper}")
|
||||
filtered_df = stats[
|
||||
(stats["main_core_fixed"] // (num_core / 2) == main)
|
||||
& (stats["helper_core_fixed"] // (num_core / 2) == helper)
|
||||
]
|
||||
all_facets(filtered_df, f"m{main}h{helper}_")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user