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
|
from nnps_kernels import *
from compyle.config import get_config
from compyle.api import declare, annotate
from compyle.parallel import serial, Elementwise, Reduction, Scan
from compyle.array import get_backend, wrap
from compyle.low_level import atomic_inc, cast
from math import floor
from time import time
import numpy as np
import compyle.array as carr
class NNPS(object):
def __init__(self, x, y, z, h, eps, xmax, ymax, zmax, backend=None):
self.backend = backend
self.num_particles = x.length
self.eps = eps
self.x, self.y, self.z = x, y, z
self.xmax, self.ymax, self.zmax = xmax, ymax, zmax
self.h = h + 2 * eps
cmax = np.array([floor((xmax + eps) / self.h),
floor((ymax + eps) / self.h),
floor((zmax + eps) / self.h)], dtype=np.int32)
self.max_key = 1 + \
flatten(cmax[0], cmax[1], cmax[2], 1 + cmax[1], 1 + cmax[2])
self.pmax = 1 + cmax[0]
self.qmax = 1 + cmax[1]
self.rmax = 1 + cmax[2]
self.setup_kernels()
self.init_arrays()
def setup_kernels(self):
# neighbor kernels
self.find_neighbor_lengths = Elementwise(find_neighbor_lengths_knl,
backend=self.backend)
self.find_neighbors = Elementwise(find_neighbors_knl,
backend=self.backend)
self.scan_start_indices = Scan(input=input_start_indices,
output=output_start_indices,
scan_expr="a+b", dtype=np.int32,
backend=self.backend)
def init_arrays(self):
# sort arrays
self.bin_counts = carr.zeros(self.max_key, dtype=np.int32,
backend=self.backend)
self.start_indices = carr.zeros(self.max_key, dtype=np.int32,
backend=self.backend)
self.keys = carr.zeros(self.num_particles, dtype=np.int32,
backend=self.backend)
self.sorted_indices = carr.zeros(self.num_particles, dtype=np.int32,
backend=self.backend)
# neighbor arrays
self.nbr_lengths = carr.zeros(self.num_particles, dtype=np.int32,
backend=self.backend)
self.nbr_starts = carr.zeros(self.num_particles, dtype=np.int32,
backend=self.backend)
self.nbrs = carr.zeros(2 * self.num_particles, dtype=np.int32,
backend=self.backend)
def reset_arrays(self):
# sort arrays
self.bin_counts.fill(0)
self.start_indices.fill(0)
self.sorted_indices.fill(0)
# neighbors array
self.nbr_lengths.fill(0)
self.nbr_starts.fill(0)
def get_neighbors(self):
self.find_neighbor_lengths(self.x, self.y, self.z, self.h, self.eps,
self.qmax, self.rmax, self.start_indices,
self.sorted_indices, self.bin_counts,
self.nbr_lengths, self.max_key)
self.scan_start_indices(counts=self.nbr_lengths,
indices=self.nbr_starts)
self.total_neighbors = int(self.nbr_lengths[-1] + self.nbr_starts[-1])
self.nbrs.resize(self.total_neighbors)
self.find_neighbors(self.x, self.y, self.z, self.h, self.eps, self.qmax,
self.rmax, self.start_indices, self.sorted_indices,
self.bin_counts, self.nbr_starts,
self.nbrs, self.max_key)
def find_nearest_neighbors(self, qid):
start_idx = self.nbr_starts.dev[qid]
length = self.nbr_lengths.dev[qid]
return self.nbrs.dev[start_idx:start_idx + length]
class NNPSCountingSort(NNPS):
def __init__(self, x, y, z, h, eps, xmax, ymax, zmax, backend=None):
super().__init__(x, y, z, h, eps, xmax, ymax, zmax, backend=backend)
# sort kernels
self.count_bins = Elementwise(count_bins, backend=self.backend)
self.sort_indices = Elementwise(sort_indices, backend=self.backend)
def init_arrays(self):
super().init_arrays()
self.sort_offsets = carr.zeros(self.num_particles, dtype=np.int32,
backend=self.backend)
def reset_arrays(self):
super().reset_arrays()
# sort arrays
self.sort_offsets.fill(0)
def build(self):
self.reset_arrays()
self.count_bins(self.x, self.y, self.z, self.h, self.eps, self.qmax,
self.rmax, self.keys, self.bin_counts,
self.sort_offsets)
self.scan_start_indices(counts=self.bin_counts,
indices=self.start_indices)
self.sort_indices(self.keys, self.sort_offsets, self.start_indices,
self.sorted_indices)
class NNPSRadixSort(NNPS):
def __init__(self, x, y, z, h, eps, xmax, ymax, zmax, backend=None):
super().__init__(x, y, z, h, eps, xmax, ymax, zmax, backend=backend)
self.max_bits = np.ceil(np.log2(self.max_key))
# sort kernels
self.fill_keys = Elementwise(fill_keys, backend=self.backend)
self.fill_bin_counts = Elementwise(fill_bin_counts,
backend=self.backend)
self.scan_keys = Scan(input=input_scan_keys,
output=output_scan_keys,
scan_expr="a+b", dtype=np.int32,
backend=self.backend)
def init_arrays(self):
super().init_arrays()
# sort arrays
self.sorted_keys = carr.zeros(self.num_particles, dtype=np.int32,
backend=self.backend)
self.indices = carr.zeros(self.num_particles, dtype=np.int32,
backend=self.backend)
def reset_arrays(self):
super().reset_arrays()
self.sorted_keys.fill(0)
def build(self):
self.reset_arrays()
self.fill_keys(self.x, self.y, self.z, self.h, self.eps, self.qmax,
self.rmax, self.indices, self.keys)
self.sorted_keys, self.sorted_indices = carr.sort_by_keys(
[self.keys, self.indices],
key_bits=self.max_bits, backend=self.backend)
self.scan_keys(keys=self.sorted_keys,
start_indices=self.start_indices)
self.fill_bin_counts(self.sorted_keys, self.start_indices,
self.bin_counts, self.num_particles)
class NNPSCountingSortPeriodic(NNPSCountingSort):
def setup_kernels(self):
# neighbor kernels
self.find_neighbor_lengths = Elementwise(
find_neighbor_lengths_periodic_knl,
backend=self.backend)
self.find_neighbors = Elementwise(
find_neighbors_periodic_knl,
backend=self.backend)
self.scan_start_indices = Scan(input=input_start_indices,
output=output_start_indices,
scan_expr="a+b", dtype=np.int32,
backend=self.backend)
def get_neighbors(self):
self.find_neighbor_lengths(self.x, self.y, self.z, self.h,
self.eps, self.xmax, self.ymax, self.zmax,
self.pmax, self.qmax,
self.rmax, self.start_indices,
self.sorted_indices, self.bin_counts,
self.nbr_lengths, self.max_key)
self.scan_start_indices(counts=self.nbr_lengths,
indices=self.nbr_starts)
self.total_neighbors = int(self.nbr_lengths[-1] + self.nbr_starts[-1])
self.nbrs.resize(self.total_neighbors)
self.find_neighbors(self.x, self.y, self.z, self.h, self.eps,
self.xmax, self.ymax, self.zmax,
self.pmax, self.qmax,
self.rmax, self.start_indices, self.sorted_indices,
self.bin_counts, self.nbr_starts,
self.nbrs, self.max_key)
class NNPSRadixSortPeriodic(NNPSRadixSort):
def setup_kernels(self):
# neighbor kernels
self.find_neighbor_lengths = Elementwise(
find_neighbor_lengths_periodic_knl,
backend=self.backend)
self.find_neighbors = Elementwise(
find_neighbors_periodic_knl,
backend=self.backend)
self.scan_start_indices = Scan(input=input_start_indices,
output=output_start_indices,
scan_expr="a+b", dtype=np.int32,
backend=self.backend)
def get_neighbors(self):
self.find_neighbor_lengths(self.x, self.y, self.z, self.h,
self.eps, self.xmax, self.ymax, self.zmax,
self.pmax, self.qmax,
self.rmax, self.start_indices,
self.sorted_indices, self.bin_counts,
self.nbr_lengths, self.max_key)
self.scan_start_indices(counts=self.nbr_lengths,
indices=self.nbr_starts)
self.total_neighbors = int(self.nbr_lengths[-1] + self.nbr_starts[-1])
self.nbrs.resize(self.total_neighbors)
self.find_neighbors(self.x, self.y, self.z, self.h, self.eps,
self.xmax, self.ymax, self.zmax,
self.pmax, self.qmax,
self.rmax, self.start_indices, self.sorted_indices,
self.bin_counts, self.nbr_starts,
self.nbrs, self.max_key)
if __name__ == "__main__":
import sys
import matplotlib.pyplot as plt
backend = sys.argv[1] if len(sys.argv) > 1 else 'cython'
np.random.seed(123)
num_particles = 10000
x, y, z = np.mgrid[0:30:50j, 0:30:50j, 0:30:50j]
x = x.ravel().astype(np.float64)
y = y.ravel().astype(np.float64)
z = z.ravel().astype(np.float64)
x, y, z = wrap(x, y, z, backend=backend)
nnps = NNPSRadixSortPeriodic(
x, y, z, 3., 0.01, 30., 30., 30., backend=backend)
nnps.build()
nnps.get_neighbors()
nbrs = nnps.find_nearest_neighbors(75000)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlim(30)
ax.set_ylim(30)
ax.set_zlim(30)
ax.scatter(x[nbrs], y[nbrs], z[nbrs])
plt.show()
|
