-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathcompute_and_analyze_FFGs.py
273 lines (231 loc) · 11.7 KB
/
compute_and_analyze_FFGs.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
import numpy as np
import csv
import json
import os
import itertools
import warnings
import matplotlib.pyplot as plt
import networkx as nx
import pickle
import bloopy
from bloopy.individual import individual, continuous_individual
import bloopy.utils as utils
import bloopy.analysis.analysis_utils as anutil
import bloopy.analysis.critical_points as critpts
import bloopy.analysis.FFG
import gpu_utils
def compute_and_analyze():
np.set_printoptions(precision=4)
current_dir = os.path.dirname(os.path.abspath(__file__))
root_dir = "/".join(current_dir.split('/')[:-1]) + "/"
# Read file
# We do hyperparameter tuning on GTX 1080Ti files
data_path = root_dir + 'AutoTuning_AMD_vs_Nvidia_GPUs/processed_cache_files/'
#data_path = '/var/scratch/mli940/'
convolution_files = [
#'convolution_MI50_processed.json',
'convolution_MI250X_processed.json',
'convolution_W6600_processed.json',
'convolution_A4000_processed.json',
'convolution_A100_processed.json'
]
hotspot_files = [
#'hotspot_MI50_processed.json',
'hotspot_MI250X_processed.json',
'hotspot_W6600_processed.json',
'hotspot_A4000_processed.json',
'hotspot_A100_processed.json'
]
dedisp_files = [
#'dedisp_MI50_processed.json',
'dedisp_MI250X_processed.json',
'dedisp_W6600_processed.json',
'dedisp_A4000_processed.json',
'dedisp_A100_processed.json'
]
gemm_files = [
#'gemm_MI50_processed.json',
'gemm_MI250X_processed.json',
'gemm_W6600_processed.json',
'gemm_A4000_processed.json',
'gemm_A100_processed.json'
]
for files in (gemm_files, convolution_files, hotspot_files, dedisp_files):
for filename in files:
print(f"Computing and analyzing {filename} FFG")
with open(data_path + filename, 'r') as myfile:
data=myfile.read()
data = json.loads(data)
print("\nDevice: " + str(data['device_name']))
print("Kernel name: " + str(data['kernel_name']))
print("Tunable parameters: " + str(data['tune_params_keys']), end='\n\n')
# Pre-process the search space
searchspace_orig = data['tune_params']
searchspace = utils.clean_up_searchspace(searchspace_orig)
#print("Processed search space:", searchspace)
### Calculate bitstring size
bsize = utils.calculate_bitstring_length(searchspace)
#print("Size of bitstring after pre-processing:", bsize)
### Number of variables
nr_vars = len(searchspace.keys())
# Construct the GPU tuning space
GPU_space = gpu_utils.GPU_tuning_space(searchspace, searchspace_orig, data['cache'])
disc_space = utils.discrete_space(GPU_space.get_runtime, searchspace)
### Compute optimal fitness for reference
if "dedisp" in data["kernel_name"]:
performance = "GB/s"
else:
performance = "GFLOP/s"
best_fit = 100000000
bestkey = None
for k in list(data['cache'].keys()):
try:
# HACK: bloopy assumse that we want to minimize "time", however, we
# instead want to maximize "performance". To solve this, we
# set the "time" column to "1/performance" as a 'workaround'.
fake_time = 1 / float(data['cache'][k][performance])
data['cache'][k]["time"] = fake_time
if fake_time < best_fit:
best_fit = fake_time
bestkey = k
except e:
print(e)
pass
print("Optimal settings in cache are:", bestkey, "with time {0:.4f}".format(best_fit))
print("There are", len(data['cache'].keys()), "keys in the searchspace")
### <<< ANALYZING SEARCH SPACES >>>
#method = 'circular'
method = 'bounded'
#method = 'Hamming'
boundary_list = utils.generate_boundary_list(searchspace)
indiv = individual(bsize, boundary_list=boundary_list)
## Find the global minimum
best_key_bs = gpu_utils.convert_gpusetting_to_bitidxs(bestkey, boundary_list, searchspace_orig)
utils.set_bitstring(indiv, list(best_key_bs))
glob_fit = disc_space.fitness(indiv.bitstring)
print("Global minimum:", bestkey, "with fitness", glob_fit)
## Loop through the space and assign point types to each point.
## Also build the space dictionary.
nidxs_dict = anutil.build_nodeidxs_dict(boundary_list, disc_space.fitness, bsize)
tot, minimas, maximas, saddles, regulars, spacedict = critpts.classify_points(bsize, boundary_list, nidxs_dict, method=method)
idxs_to_pts = anutil.indices_to_points(spacedict)
print(tot, minimas, maximas, saddles, regulars)
### COMPUTE FFG ###
G = bloopy.analysis.FFG.build_FFG(nidxs_dict, boundary_list, method=method)
### ANALYZE FFG ###
graph_name = "FFG_data/FFG_" + method + "_" + filename[:-5] + ".txt"
## Check graph properties such as cycles
globopt_idx = spacedict[tuple(best_key_bs)][2]
print("Global optimum is node:", globopt_idx)
print(len(G.nodes()), "nodes,", len(G.edges()), "edges, in search space graph")
## Calculate centralities
#centrality = "degree"
#centrality = "katz"
centrality = "pagerank"
#centrality = "closeness"
if centrality == "degree":
# Degree centrality can be seen as the immediate risk of a node for catching whatever is flowing through the network.
# So a high degree centrality for global optimum means there is
# a better chance of ending up there.
centrality_dict = nx.algorithms.centrality.degree_centrality(G)
print("Degree centrality of global optimum:", centrality_dict[globopt_idx])
elif centrality == "eigen":
# Eigen vector centraliy is similar, it is a measure of the influence
# of a node in a network.
centrality_dict = nx.eigenvector_centrality_numpy(G.reverse())
print("Eigen vector centrality of global optimum:", centrality_dict[globopt_idx])
elif centrality == "katz":
# Katz centrality is a variant of eigenvector, look up details
centrality_dict = nx.katz_centrality_numpy(G)
print("Katz vector centrality of global optimum:", centrality_dict[globopt_idx])
elif centrality == "secondorder":
# The second order centrality of a given node is the standard
# deviation of the return times to that node of a perpetual
# random walk on G.
centrality_dict = nx.algorithms.centrality.second_order_centrality(G)
print("Second order centrality of global optimum:", centrality_dict[globopt_idx])
elif centrality == "closeness":
centrality_dict = nx.algorithms.centrality.closeness_centrality(G)
print("Closeness centrality of global optimum:", centrality_dict[globopt_idx])
elif centrality == "pagerank":
centrality_dict = nx.algorithms.link_analysis.pagerank_alg.pagerank(G)
print("Pagerank centrality of global optimum:", centrality_dict[globopt_idx])
else:
raise Exception("Unknown centrality type")
centr_name = "FFG_data/propFFG_centrality_" + centrality + "_" + method + "_" + filename[:-5] + ".csv"
percs = np.arange(0.0, 0.16, 0.01).tolist()
centralities = [["Percentage","proportion_centr","sum_accept_centr", "tot_centr", "minima_centr", "nr_of_nodes"]]
for perc in percs:
acceptable_minima = critpts.strong_local_minima(perc, glob_fit, spacedict)
accept_centr, tot_centr, minima_centr = bloopy.analysis.FFG.average_centrality_nodes(centrality_dict, acceptable_minima, spacedict, idxs_to_pts)
prop_centr = accept_centr/float(minima_centr)
centralities.append([perc, prop_centr, accept_centr, tot_centr, minima_centr, len(centrality_dict.values())])
print("Proportion of centrality of strong local minima", perc, ":",prop_centr)
# Save to CSV file
with open(centr_name, "w", newline="") as f:
writer = csv.writer(f)
writer.writerows(centralities)
#NOTE: (Un)comment continue if you only want to compute pagerank centralities
continue
## Plot the graph with NetworkX
color_map = []
size_map = []
threshold = 0.75
for node in G:
pt = idxs_to_pts[node][0]
if spacedict[pt][0] == 1:
fit = spacedict[pt][1]
color_map.append(glob_fit/fit)
siz = 5
if glob_fit/fit > threshold:
siz += 4*8*(glob_fit/fit - threshold)
size_map.append(siz)
else:
fit = spacedict[pt][1]
color_map.append(glob_fit/fit)
size_map.append(0.7)
# Define colormap with transparency
from matplotlib.colors import ListedColormap
#cmap = plt.get_cmap('viridis')
cmap = plt.get_cmap('winter')
# Get the colormap colors
my_cmap = cmap(np.arange(cmap.N))
# Set alpha
my_cmap[:,-1] = np.linspace(0.5, 1.0, cmap.N)
# Create new colormap
my_cmap = ListedColormap(my_cmap)
# Some technicalities for plotting
cf = plt.gcf()
cf.set_facecolor("w")
if cf._axstack is None:
ax = cf.add_axes((0, 0, 1, 1))
else:
ax = cf.gca()
if nx.is_directed(G):
arz = 2
arst = '-|>'
wdth = 0.001
alp = 0.1
#pos = nx.drawing.nx_agraph.graphviz_layout(G, prog='dot')
#nx.draw(G, pos=pos, node_color=color_map, arrowsize=arz, node_size=size_map, with_labels=False, arrows=True,arrowstyle=arst, font_size=1, cmap=my_cmap, vmin=0.75, vmax=1.0, width=wdth)
#nx.draw_kamada_kawai(G, arrows=True, arrowstyle=arst, arrowsize=arz, node_size=size_map, node_color=color_map, font_size=1, with_labels=False, cmap=my_cmap, vmin=0.75, vmax=1.0, width=wdth)
pos = nx.kamada_kawai_layout(G)
pltnodes = nx.drawing.nx_pylab.draw_networkx_nodes(G, pos, ax=ax, node_size=size_map, node_color=color_map, cmap=my_cmap, vmin=0.75, vmax=1.0, linewidths=0.0)
nx.drawing.nx_pylab.draw_networkx_edges(G, pos, ax=ax, arrows=True, arrowstyle=arst, arrowsize=arz, width=wdth, node_size=size_map, alpha=alp)
plt.draw_if_interactive()
ax.set_axis_off()
plt.draw_if_interactive()
# Draw colorbar
cbar = plt.colorbar(pltnodes, shrink=0.5)
cbar.set_label('Fraction of optimal fitness', rotation=270, labelpad=15)
plt.axis('off')
plt.draw()
plt.savefig(graph_name[:-4] + ".pdf")
plt.clf()
del G
del spacedict
del centrality_dict
del acceptable_minima
del idxs_to_pts
if __name__ == '__main__':
compute_and_analyze()