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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, h, xmax, ymax, backend=None):
self.backend = backend
self.num_particles = x.length
self.x, self.y = x, y
self.h = h
cmax = np.array([floor(xmax / h), floor(ymax / h)], dtype=np.int32)
self.max_key = 1 + flatten(cmax[0], cmax[1], 1 + cmax[1])
self.qmax = 1 + cmax[1]
# 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)
self.init_arrays()
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.h, self.qmax,
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.h, self.qmax,
self.start_indices, self.sorted_indices,
self.bin_counts, self.nbr_starts,
self.nbrs, self.max_key)
class NNPSCountingSort(NNPS):
def __init__(self, x, y, h, xmax, ymax, backend=None):
super().__init__(x, y, h, xmax, ymax, 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.h, self.qmax, 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, h, xmax, ymax, backend=None):
super().__init__(x, y, h, xmax, ymax, 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.h, self.qmax, 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)
if __name__ == "__main__":
import sys
backend = sys.argv[1] if len(sys.argv) > 1 else 'cython'
np.random.seed(123)
num_particles = 20
x = np.random.uniform(0, 10., size=num_particles).astype(np.float32)
y = np.random.uniform(0, 10., size=num_particles).astype(np.float32)
x, y = wrap(x, y, backend=backend)
nnps = NNPSRadixSort(x, y, 3., 10., 10., backend=backend)
nnps.build()
nnps.get_neighbors()
print(nnps.start_indices)
print(nnps.bin_counts)
print(nnps.nbr_lengths)
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