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cuda_lib_gl.py
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# coding: utf-8
__author__ = 'hervemn'
import numpy as np
import pycuda
import pycuda.tools
from pycuda import characterize
from pycuda import *
import pycuda.characterize
import pycuda.driver as cuda
import pycuda.compiler
from gpustruct import GPUStruct
from pycuda import gpuarray as ga
import pycuda.gl as cudagl
import time
import matplotlib.pyplot as plt
import optim_rippe_curve_update as opti
import optim_hic_curve as opti_rv
from OpenGL.arrays import vbo
from scipy import stats
import timing
from PIL import Image
timings = timing.Timing()
class sampler():
def __init__(self, use_rippe, S_o_A_frags, collector_id_repeats, frag_dispatcher,
id_frag_duplicated, id_frags_blacklisted,
n_frags, n_new_frags, init_n_sub_frags, n_new_sub_frags, np_rep_sub_frags_id,
hic_matrix_sub_sampled,
np_sub_frags_len_bp, np_sub_frags_id, np_sub_frags_accu,
mean_squared_frags_per_bin, norm_vect_accu,
S_o_A_sub_frags,
hic_matrix, mean_value_trans, n_iterations, is_simu, gl_window, pos_vbo, col_vbo, vel, pos,
raw_im_init,pbo_im_buffer,
sub_sample_factor):
self.o = 0
self.sub_sample_factor = sub_sample_factor
if self.sub_sample_factor <= 1 and self.sub_sample_factor > 0:
self.perform_sub_sample = True
else:
self.perform_sub_sample = False
self.raw_im_init = raw_im_init
self.pbo_im_buffer = pbo_im_buffer
self.use_rippe = use_rippe
self.gl_window = gl_window
self.ctx = gl_window.ctx_gl
self.pos_vbo = pos_vbo
self.col_vbo = col_vbo
self.pos = pos
self.vel = vel
self.load_gl_cuda_vbo()
self.id_frags_blacklisted = id_frags_blacklisted
self.int4 = np.dtype([('x', np.int32), ('y', np.int32), ('z', np.int32),('n', np.int32)], align=True)
self.float3 = np.dtype([('x', np.float32), ('y', np.float32), ('z', np.float32)], align=True)
self.int3 = np.dtype([('x', np.int32), ('y', np.int32), ('z', np.int32)], align=True)
self.np_id_frag_duplicated = np.int32(id_frag_duplicated)
self.id_frag_duplicated = id_frag_duplicated
self.n_frags = np.int32(n_frags)
self.n_new_frags = np.int32(n_new_frags)
self.init_n_sub_frags = np.int32(init_n_sub_frags)
self.n_new_sub_frags = np.int32(n_new_sub_frags)
self.uniq_frags = np.int32(np.lib.arraysetops.setdiff1d(np.arange(0, self.n_frags, dtype=np.int32),
self.np_id_frag_duplicated))
self.n_frags_uniq = np.int32(len(self.uniq_frags))
self.n_values_triu = np.int32(self.n_frags * (self.n_frags - 1) / 2)
self.init_n_values_triu = np.int32(self.n_frags * (self.n_frags - 1) / 2)
self.new_n_values_triu = np.int32(self.n_new_frags * (self.n_new_frags - 1) / 2)
self.init_n_sub_values_triu = np.int32(self.init_n_sub_frags * ( self.init_n_sub_frags - 1) / 2)
self.new_n_sub_values_triu = np.int32(self.n_new_sub_frags * ( self.n_new_sub_frags - 1) / 2)
self.init_n_values_triu_extra = self.init_n_values_triu + self.n_frags
self.init_n_sub_values_triu_extra = self.init_n_sub_values_triu + self.init_n_sub_frags
self.cpu_likelihood = np.zeros((self.init_n_values_triu_extra,), dtype=np.float64)
self.gpu_likelihood = ga.to_gpu(ary=self.cpu_likelihood)
self.curr_likelihood = ga.to_gpu(np.zeros((self.init_n_values_triu_extra,), dtype=np.float64))
self.curr_likelihood_forward = ga.to_gpu(np.zeros((self.init_n_values_triu_extra,), dtype=np.float64))
# self.cpu_full_expected_lin = np.zeros((self.init_n_sub_values_triu, ), dtype=np.float32)
# self.gpu_full_expected_lin = ga.to_gpu(ary=self.cpu_full_expected_lin)
self.cpu_full_likelihood = np.zeros((self.init_n_sub_values_triu,), dtype=np.float64)
self.gpu_full_likelihood = ga.to_gpu(ary=self.cpu_full_likelihood)
# self.cpu_2d_full_expected_mat = np.zeros((self.init_n_sub_frags, self.init_n_sub_frags), dtype=np.float32)
# self.gpu_2d_full_expected_mat = ga.to_gpu(ary=self.cpu_2d_full_expected_mat)
# self.cpu_full_expected_rep_lin = np.zeros((self.new_n_sub_values_triu, ), dtype=np.float32)
# self.gpu_full_expected_rep_lin = ga.to_gpu(ary=self.cpu_full_expected_rep_lin)
# self.cpu_2d_full_expected_rep_mat = np.zeros((self.n_new_sub_frags, self.n_new_sub_frags), dtype=np.float32)
# self.gpu_2d_full_expected_rep_mat = ga.to_gpu(ary=self.cpu_2d_full_expected_rep_mat)
self.n_modif_metropolis = 13
self.n_tmp_struct = 13
self.is_simu = is_simu
self.norm_vect_accu = norm_vect_accu
self.np_sub_frags_len_bp = np_sub_frags_len_bp
self.np_sub_frags_id = np_sub_frags_id
self.np_sub_frags_accu = np_sub_frags_accu
self.hic_matrix_sub_sampled = hic_matrix_sub_sampled
self.mean_squared_frags_per_bin = np.float32(mean_squared_frags_per_bin)
print "size hic matrix = ", hic_matrix.nbytes/10**6
################################################################################################################
self.collector_id_repeats = collector_id_repeats
self.gpu_collector_id_repeats = ga.to_gpu(ary=self.collector_id_repeats)
self.frag_dispatcher = frag_dispatcher
self.gpu_frag_dispatcher = cuda.mem_alloc(self.frag_dispatcher.nbytes)
cuda.memcpy_htod(self.gpu_frag_dispatcher, self.frag_dispatcher)
self.np_rep_sub_frags_id = np_rep_sub_frags_id
self.gpu_rep_sub_frags_id = cuda.mem_alloc_like(self.np_rep_sub_frags_id)
cuda.memcpy_htod(self.gpu_rep_sub_frags_id, self.np_rep_sub_frags_id)
self.gpu_uniq_frags = ga.to_gpu(ary=self.uniq_frags)
################################################################################################################
self.S_o_A_frags = S_o_A_frags
self.S_o_A_sub_frags = S_o_A_sub_frags
self.mean_value_trans = mean_value_trans
self.param_simu_rippe = np.dtype([('kuhn', np.float32), ('lm', np.float32), ('c1', np.float32), ('slope', np.float32),
('d', np.float32), ('l_max', np.float32), ('fact', np.float32),
('v_inter', np.float32)], align=True)
self.param_simu_exp = np.dtype([('d0', np.float32), ('d1', np.float32), ('d_max', np.float32),
('alpha_0', np.float32), ('alpha_1', np.float32), ('alpha_2', np.float32),
('fact', np.float32),
('v_inter', np.float32)], align=True)
if self.use_rippe:
self.param_simu_T = self.param_simu_rippe
else:
self.param_simu_T = self.param_simu_exp
################################################################################################################
self.n_iterations = n_iterations
(free, total)=cuda.mem_get_info()
print("Global memory occupancy before init:%f%% free"%(free*100/total))
print("Global free memory before init:%i Mo free"%(free/10**6))
################################################################################################################
self.sub_width_mat = np.int32(hic_matrix.shape[0])
self.sub_n_frags = np.int32(self.sub_width_mat)
self.hic_matrix_ori = np.copy(np.float32(hic_matrix))
self.hic_matrix = np.copy(np.float32(hic_matrix))
idx_diag = np.diag_indices_from(self.hic_matrix)
self.hic_matrix[idx_diag] = 0
idx_diag_sub = np.diag_indices_from(self.hic_matrix_sub_sampled)
self.hic_matrix_sub_sampled[idx_diag_sub] = 0
for id_f in self.id_frags_blacklisted:
real_id = self.S_o_A_frags['id_d'][id_f]
self.hic_matrix_sub_sampled[real_id, :] = 0
self.hic_matrix_sub_sampled[:, real_id] = 0
da = self.np_sub_frags_id[real_id]
for i in range(0, da['w']):
id = da[i]
self.hic_matrix[id,:] = self.mean_value_trans
self.hic_matrix[:, id] =self.mean_value_trans
self.hic_matrix_ori[id,:] = self.mean_value_trans
self.hic_matrix_ori[:, id] = self.mean_value_trans
############### DEBUGGG #############################
#da1 = self.np_sub_frags_id[71]
#
#da2 = self.np_sub_frags_id[72]
#da3 = self.np_sub_frags_id[79]
#
#self.hic_matrix[da1['x'], da2['x']:da3['z']] = self.mean_value_trans
#self.hic_matrix[da2['x']:da3['z'], da1['x']] = self.mean_value_trans
#
#self.hic_matrix_sub_sampled[71, 72:79] = 0
#self.hic_matrix_sub_sampled[72:79, 71] = 0
############### DEBUGGG #############################
self.define_neighbourhood()
self.n_new_values_triu = np.int32(self.n_new_frags * (self.n_new_frags - 1) / 2)
self.sub_n_values_triu = np.int32(self.sub_n_frags * (self.sub_n_frags - 1) / 2)
################################################################################################################
self.gpu_2d_hic_matrix = ga.to_gpu(self.hic_matrix)
self.cpu_simu_matrix = np.zeros((self.hic_matrix.shape), dtype=np.float32)
self.cpu_simu_sub_matrix = np.zeros((self.hic_matrix_sub_sampled.shape), dtype=np.float32)
self.gpu_2d_simu_hic_matrix = ga.to_gpu(self.cpu_simu_matrix)
# if self.is_simu:
# self.gpu_2d_sub_hic_matrix = ga.to_gpu(self.hic_matrix_sub_sampled)
# self.cpu_2d_full_sub_hic_matrix = np.zeros((self.n_new_frags, self.n_new_frags), dtype=np.float32)
# self.gpu_2d_full_sub_hic_matrix = ga.to_gpu(ary=self.cpu_2d_full_sub_hic_matrix)
# self.cpu_full_mat_rep = np.zeros((self.n_new_sub_frags, self.n_new_sub_frags), dtype=np.float32)
#
# self.lin_full_mat_rep = np.zeros((self.n_sub_values_triu, ), dtype=np.float32)
# self.cpu_full_mat_rep = np.zeros((self.n_new_sub_frags, self.n_new_sub_frags), dtype=np.float32)
# self.gpu_full_mat_rep = ga.to_gpu(ary=self.lin_full_mat_rep)
# self.gpu_2d_full_mat_rep = ga.to_gpu(ary=self.cpu_full_mat_rep)
################################################################################################################
self.gpu_sub_frag_len_bp = cuda.mem_alloc(self.np_sub_frags_len_bp.nbytes)
cuda.memcpy_htod(self.gpu_sub_frag_len_bp, self.np_sub_frags_len_bp)
self.gpu_sub_frag_id = cuda.mem_alloc(self.np_sub_frags_id.nbytes)
cuda.memcpy_htod(self.gpu_sub_frag_id, self.np_sub_frags_id)
self.gpu_sub_frag_accu = cuda.mem_alloc(self.np_sub_frags_accu.nbytes)
cuda.memcpy_htod(self.gpu_sub_frag_accu, self.np_sub_frags_accu)
################################################################################################################
self.cpu_likelihood_fi = np.zeros((self.n_values_triu,), dtype=np.float64)
print "size likelihood = ", self.cpu_likelihood_fi.nbytes / 10**6.
self.gpu_sub_index = ga.to_gpu(np.zeros((self.n_new_frags,), dtype=np.int32))
self.list_likelihood = []
self.gpu_expected_mat = ga.to_gpu(np.zeros((self.n_values_triu,), dtype=np.float32))
self.gpu_full_expected_mat = ga.to_gpu(np.zeros((self.n_values_triu,), dtype=np.float32))
for i in range(0, self.n_tmp_struct):
self.list_likelihood.append(ga.to_gpu(np.zeros((self.n_values_triu,), dtype=np.float32)))
########################
self.np_init_prev = np.copy(self.S_o_A_frags['prev'])
self.np_init_next = np.copy(self.S_o_A_frags['next'])
self.np_init_orientable = []
for idf in xrange(0, self.n_new_frags):
id_d = self.S_o_A_frags['id_d'][idf]
self.np_init_orientable.append(self.np_sub_frags_id[id_d]['w'] > 1)
self.np_init_orientable = np.array(self.np_init_orientable, dtype=np.int32)
self.np_init_ori = np.ones((self.n_new_frags,), dtype=np.int32)
self.gpu_vect_frags_forward = GPUStruct([(np.int32, '*pos', np.copy(self.S_o_A_frags['pos'])),
(np.int32, '*id_c', np.copy(self.S_o_A_frags['id_c'])),
(np.int32, '*start_bp', np.copy(self.S_o_A_frags['start_bp'])),
(np.int32, '*len_bp', np.copy(self.S_o_A_frags['len_bp'])),
(np.int32, '*circ', np.copy(self.S_o_A_frags['circ'])),
(np.int32, '*id', np.copy(self.S_o_A_frags['id'])),
(np.int32, '*prev', np.copy(self.S_o_A_frags['prev'])),
(np.int32, '*next', np.copy(self.S_o_A_frags['next'])),
(np.int32, '*l_cont', np.copy(self.S_o_A_frags['l_cont'])),
(np.int32, '*l_cont_bp', np.copy(self.S_o_A_frags['l_cont_bp'])),
(np.int32, '*ori', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*rep', np.copy(self.S_o_A_frags['rep'])),
(np.int32, '*activ', np.copy(self.S_o_A_frags['activ'])),
(np.int32, '*id_d', np.copy(self.S_o_A_frags['id_d']))])
self.gpu_vect_frags = GPUStruct([(np.int32, '*pos', np.copy(self.S_o_A_frags['pos'])),
(np.int32, '*id_c', np.copy(self.S_o_A_frags['id_c'])),
(np.int32, '*start_bp', np.copy(self.S_o_A_frags['start_bp'])),
(np.int32, '*len_bp', np.copy(self.S_o_A_frags['len_bp'])),
(np.int32, '*circ', np.copy(self.S_o_A_frags['circ'])),
(np.int32, '*id', np.copy(self.S_o_A_frags['id'])),
(np.int32, '*prev', np.copy(self.S_o_A_frags['prev'])),
(np.int32, '*next', np.copy(self.S_o_A_frags['next'])),
(np.int32, '*l_cont', np.copy(self.S_o_A_frags['l_cont'])),
(np.int32, '*l_cont_bp', np.copy(self.S_o_A_frags['l_cont_bp'])),
(np.int32, '*ori', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*rep', np.copy(self.S_o_A_frags['rep'])),
(np.int32, '*activ', np.copy(self.S_o_A_frags['activ'])),
(np.int32, '*id_d', np.copy(self.S_o_A_frags['id_d']))])
self.gpu_vect_frags_forward.copy_to_gpu()
self.gpu_vect_frags.copy_to_gpu()
self.cpu_id_contigs = np.copy(self.S_o_A_frags['id_c'])
self.gpu_id_contigs = ga.to_gpu(self.cpu_id_contigs)
self.collector_gpu_vect_frags = []
for k in xrange(0, max(self.n_tmp_struct, self.n_modif_metropolis)):
self.collector_gpu_vect_frags.append(GPUStruct([(np.int32, '*pos', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_c', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*start_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*len_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*circ', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*prev', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*next', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*ori', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*rep', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*activ', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_d', np.zeros((self.n_new_frags,), dtype=np.int32))]))
for k in xrange(0, max(self.n_tmp_struct, self.n_modif_metropolis)):
self.collector_gpu_vect_frags[k].copy_to_gpu()
self.pop_gpu_vect_frags = GPUStruct([(np.int32, '*pos', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_c', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*start_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*len_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*circ', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*prev', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*next', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*ori', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*rep', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*activ', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_d', np.zeros((self.n_new_frags,), dtype=np.int32))])
self.scrambled_gpu_vect_frags = GPUStruct([(np.int32, '*pos', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_c', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*start_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*len_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*circ', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*prev', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*next', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*ori', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*rep', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*activ', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_d', np.zeros((self.n_new_frags,), dtype=np.int32))])
self.pop_cpu_id_contigs = np.copy(self.cpu_id_contigs)
self.pop_gpu_id_contigs = ga.to_gpu(self.pop_cpu_id_contigs)
self.pop_gpu_vect_frags.copy_to_gpu()
self.scrambled_gpu_vect_frags.copy_to_gpu()
self.trans1_gpu_vect_frags = GPUStruct([(np.int32, '*pos', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_c', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*start_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*len_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*circ', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*prev', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*next', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*ori', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*rep', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*activ', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_d', np.zeros((self.n_new_frags,), dtype=np.int32))])
self.trans1_cpu_id_contigs = np.copy(self.cpu_id_contigs)
self.trans1_gpu_id_contigs = ga.to_gpu(self.trans1_cpu_id_contigs)
self.trans1_gpu_vect_frags.copy_to_gpu()
self.trans2_gpu_vect_frags = GPUStruct([(np.int32, '*pos', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_c', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*start_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*len_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*circ', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*prev', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*next', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*l_cont_bp', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*ori', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*rep', np.zeros((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*activ', np.ones((self.n_new_frags,), dtype=np.int32)),
(np.int32, '*id_d', np.zeros((self.n_new_frags,), dtype=np.int32))])
self.trans2_cpu_id_contigs = np.copy(self.cpu_id_contigs)
self.trans2_gpu_id_contigs = ga.to_gpu(self.trans2_cpu_id_contigs)
self.trans2_gpu_vect_frags.copy_to_gpu()
########################
self.n_generators = 2000
seed = 1
self.rng_states = cuda.mem_alloc(self.n_generators*pycuda.characterize.sizeof('curandStateXORWOW',
'#include <curand_kernel.h>'))
################################################################################################################
(free,total)=cuda.mem_get_info()
print("Global memory occupancy after init:%f%% free"%(free*100./total))
print("Global free memory after init:%i Mo free"%(free/10**6.))
################################################################################################################
print "loading kernels ..."
if self.use_rippe:
self.loadProgram('kernels3.cu')
else:
self.loadProgram('kernels4.cu')
print "kernels compiled"
self.init_rng = self.module.get_function('init_rng')
self.gen_rand_mat = self.module.get_function('gen_rand_mat')
self.simulate_data_2d = self.module.get_function('simulate_data_2d')
self.evaluate_likelihood = self.module.get_function('evaluate_likelihood')
self.fill_2d_mat = self.module.get_function('fill_2d_contacts')
self.fill_sub_index_A = self.module.get_function("fill_sub_index_fA")
self.fill_sub_index_B = self.module.get_function("fill_sub_index_fB")
self.sub_compute_likelihood_1d = self.module.get_function("sub_compute_likelihood")
self.set_null = self.module.get_function("set_null")
self.copy_gpu_array = self.module.get_function("copy_gpu_array")
self.gl_update_pos = self.module.get_function("gl_update_pos")
self.gl_update_im = self.module.get_function("reorder_tex")
self.gpu_transloc = []
self.pop_out = self.module.get_function('pop_out_frag')
self.flip_frag = self.module.get_function('flip_frag')
self.pop_in_1 = self.module.get_function('pop_in_frag_1') # split insert @ left
self.pop_in_2 = self.module.get_function('pop_in_frag_2') # split insert @ right
self.pop_in_3 = self.module.get_function('pop_in_frag_3') # insert @ left
self.pop_in_4 = self.module.get_function('pop_in_frag_4') # insert @ right
self.split = self.module.get_function('split_contig')
self.paste = self.module.get_function('paste_contigs')
self.simple_copy = self.module.get_function('simple_copy')
self.copy_vect = self.module.get_function('copy_struct')
self.swap_activity = self.module.get_function('swap_activity_frag')
self.modification_str = ['eject frag',
'flip frag',
'pop out split insert @ left or 1', 'pop out split insert @ left or -1',
'pop out split insert @ right or 1', 'pop out split insert @ right or -1',
'pop out insert @ right or 1', 'pop out insert @ right or -1',
'swap activity',
# 'pop out insert @ left or 1', 'pop out insert @ left or -1',
'transloc_1','transloc_2','transloc_3','transloc_4',
'local_scramble d1', 'local_scramble d2', 'local_scramble d3', 'local_scramble d4']
################################################################################################################
self.texref = self.module.get_texref("tex")
self.load_gl_cuda_tex_buffer(self.raw_im_init)
################################################################################################################
self.stride = 50
self.index_4_metropolis_operations = []
self.index_4_metropolis_operations.append(0)
self.index_4_metropolis_operations.append(1)
self.index_4_metropolis_operations.append(4)
self.index_4_metropolis_operations.append(5)
self.index_4_metropolis_operations.append(6)
self.index_4_metropolis_operations.append(7)
################################################################################################################
self.alpha_space = np.arange(-2, -0.4, .1)
d_space_0 = np.arange(0.1, 2.1, 0.1)
d_space_1 = np.arange(2, 100, 3)
d_space = []
d_space.extend(list(d_space_0))
d_space.extend(list(d_space_1))
self.d_space = np.array(d_space, np.float32)
self.gpu_alpha_space = ga.to_gpu(self.alpha_space)
self.gpu_d_space = ga.to_gpu(self.d_space)
self.gpu_likeli_d_space = ga.to_gpu(ary=np.zeros(self.d_space.shape, dtype=np.float64))
self.gpu_likeli_alpha_space = ga.to_gpu(ary=np.zeros(self.alpha_space.shape, dtype=np.float64))
self.n_values_d_space = np.int32(len(self.d_space))
self.n_values_alpha_space = np.int32(len(self.alpha_space))
################################################################################################################
seed = 1
self.init_rng(np.int32(self.n_generators), self.rng_states, np.uint64(seed), np.uint64(0), block=(64, 1, 1),
grid=(self.n_generators//64 + 1, 1))
################################################################################################################
self.n_neighbors = 10
self.setup_distri_frags()
self.define_repeats()
def init_likelihood(self):
# self.likelihood_t = self.compute_likelihood(self.gpu_vect_frags, self.curr_likelihood)
self.likelihood_t = self.eval_likelihood()
def define_repeats(self):
self.gpu_vect_frags.copy_from_gpu()
c = self.gpu_vect_frags
tmp_repeated = c.id != c.id_d
id_repeat = np.unique(c.id_d[tmp_repeated])
self.is_repeat = []
self.n_frags_duplicated = 0
tmp = []
tmp.extend(self.id_frags_blacklisted)
print "id_repeat = ", id_repeat
for id_f in xrange(0, self.n_new_frags):
if c.id_d[id_f] in id_repeat:
self.is_repeat.append(True)
self.n_frags_duplicated += 1
tmp.append(id_f)
else:
self.is_repeat.append(False)
self.n_frags_4_dist = len(np.unique(tmp))
# print "n tot frags = ", self.n_new_frags
# print "frags to remove dist = ", self.n_frags_4_dist
# print "tmp = ", tmp
# print "n frags duplicated = ", self.n_frags_duplicated
def dist_inter_genome(self, tmp_gpu_vect_frags):
tmp_gpu_vect_frags.copy_from_gpu()
g1 = tmp_gpu_vect_frags
n_frags_blacklisted = len(self.id_frags_blacklisted)
# d = 3.0 * (self.n_new_frags - n_frags_blacklisted - self.n_frags_duplicated)
d = 3.0 * (self.n_new_frags - self.n_frags_4_dist)
# norm_distance = 3.0 * (self.n_new_frags - n_frags_blacklisted - self.n_frags_duplicated)
norm_distance = 3.0 * (self.n_new_frags - self.n_frags_4_dist)
# d = 3.0 * self.n_new_frags
for id_f in xrange(0, self.n_new_frags):
if id_f not in self.id_frags_blacklisted and not self.is_repeat[id_f]:
prev_t0 = self.np_init_prev[id_f]
tmp_prev_t1 = g1.prev[id_f]
if tmp_prev_t1 != -1:
prev_t1 = g1.id_d[tmp_prev_t1]
else:
prev_t1 = tmp_prev_t1
next_t0 = self.np_init_next[id_f]
tmp_next_t1 = g1.next[id_f]
if tmp_next_t1 != -1:
next_t1 = g1.id_d[tmp_next_t1]
else:
next_t1 = tmp_next_t1
ori_t0 = self.np_init_ori[id_f]
ori_t1 = g1.ori[id_f]
swap = 1
if ((prev_t1 == prev_t0) and (next_t1 == next_t0)) or ((prev_t1 == next_t0) and (next_t1 == prev_t0)):
d -= 1
# if not(self.np_init_orientable[id_f]):
# d -= 2
if self.np_init_orientable[id_f]:
if ori_t0 != ori_t1:
tmp = prev_t1
prev_t1 = next_t1
next_t1 = tmp
swap = -1
if prev_t0 == prev_t1:
if prev_t0 == -1:
d -= 1
elif not(self.np_init_orientable[prev_t1]):
d -= 1
else:
d -= 0.5
ori_prev_t0 = self.np_init_ori[prev_t0]
ori_prev_t1 = g1.ori[prev_t1]
if ori_prev_t0 == swap * ori_prev_t1:
d -= 0.5
if next_t0 == next_t1:
if next_t0 == -1:
d -= 1
elif not(self.np_init_orientable[next_t1]):
d -= 1
else:
d -= 0.5
ori_next_t0 = self.np_init_ori[next_t0]
ori_next_t1 = g1.ori[next_t1]
if ori_next_t0 == swap * ori_next_t1:
d -= 0.5
else:
if ((prev_t1 == prev_t0) or (prev_t1 == next_t0)):
d -= 1
if ((next_t1 == next_t0) or (next_t1 == prev_t0)):
d -= 1
return d / norm_distance
def eval_likelihood(self,):
start = cuda.Event()
end = cuda.Event()
size_block = 512
block_ = (size_block, 1, 1)
n_block = self.init_n_values_triu_extra // (size_block) + 1
grid_all = (int(n_block / self.stride), 1)
print "block = ", block_
print "grid_all = ", grid_all
#####################################################################################
start.record()
self.evaluate_likelihood(self.data,
self.gpu_vect_frags.get_ptr(),
self.gpu_collector_id_repeats,
self.gpu_frag_dispatcher,
self.gpu_sub_frag_id,
self.gpu_rep_sub_frags_id,
self.gpu_sub_frag_len_bp,
self.gpu_sub_frag_accu,
self.curr_likelihood,
self.gpu_param_simu,
self.init_n_values_triu,
self.init_n_values_triu_extra,
self.n_frags,
self.init_n_sub_frags,
self.mean_squared_frags_per_bin,
block=block_, grid=grid_all, shared=0)
#self.evaluate_likelihood(self.data,
# self.gpu_vect_frags.get_ptr(),
# self.gpu_collector_id_repeats,
# self.gpu_frag_dispatcher,
# self.gpu_sub_frag_id,
# self.gpu_rep_sub_frags_id,
# self.gpu_sub_frag_len_bp,
# self.gpu_sub_frag_accu,
# # self.gpu_full_expected_lin,
# # self.gpu_full_expected_rep_lin,
# self.curr_likelihood,
# self.gpu_param_simu,
# self.init_n_values_triu,
# self.init_n_values_triu_extra,
# self.n_frags,
# self.init_n_sub_frags,
# self.mean_squared_frags_per_bin,
# block=block_, grid=grid_all, shared=0)
end.record()
end.synchronize()
secs = start.time_till(end) * 1e-3
print "CUDA clock execution timing (compute likelihood): ", secs
#####################################################################################
# size_block = 32
# block_ = (size_block, 1, 1)
# n_block = self.init_n_sub_values_triu // (size_block) + 1
# grid_all = (int(n_block / self.stride), 1)
# print "block = ", block_
# print "grid_all = ", grid_all
#
# start.record()
# self.fill_2d_mat(self.gpu_full_expected_lin,
# self.gpu_2d_full_expected_mat,
# self.init_n_sub_values_triu,
# self.init_n_sub_frags,
# block=block_, grid=grid_all, shared=0)
# end.record()
# end.synchronize()
# self.cpu_2d_full_expected_mat = self.gpu_2d_full_expected_mat.get()
# secs = start.time_till(end) * 1e-3
# print "CUDA clock execution timing( fill 2d full matrix): ", secs
# ####################################################################################
# block_ = (size_block, 1, 1)
# n_block = self.new_n_sub_values_triu // (size_block) + 1
# grid_all = (int(n_block / self.stride), 1)
# start.record()
# self.fill_2d_mat(self.gpu_full_expected_rep_lin,
# self.gpu_2d_full_expected_rep_mat,
# self.new_n_sub_values_triu,
# self.n_new_sub_frags,
# block=block_, grid=grid_all, shared=0)
# end.record()
# end.synchronize()
# self.cpu_2d_full_expected_rep_mat = self.gpu_2d_full_expected_rep_mat.get()
# secs = start.time_till(end) * 1e-3
# print "CUDA clock execution timing( fill 2d matrix): ", secs
# ####################################################################################
likelihood = ga.sum(self.curr_likelihood,dtype=np.float64).get()
print "likelihood = ", likelihood
return likelihood
# ####################################################################################
# plt.imshow(self.cpu_2d_full_expected_mat, interpolation='nearest', vmin=0, vmax=10,)
# plt.show()
def setup_texture(self,):
# if self.perform_sub_sample:
# start = cuda.Event()
# end = cuda.Event()
#
# size_block_x = 32
# size_block_y = 32
# n_blocks_x = int(self.sub_n_frags)//(size_block_x) + 1
# n_blocks_y = int(self.sub_n_frags)//(size_block_y) + 1
# self.grid = (n_blocks_x, n_blocks_y, 1)
# self.block = (size_block_x, size_block_y, 1)
#
# new_data = np.zeros((self.sub_n_frags, self.sub_n_frags), dtype=np.float32)
# self.gpu_new_data = ga.to_gpu(new_data)
# start.record()
# self.gen_rand_mat(self.gpu_2d_hic_matrix, self.gpu_new_data, self.rng_states, np.int32(self.n_generators),
# self.sub_n_frags, np.float32(self.sub_sample_factor),
# block=self.block, grid=self.grid, shared=0)
# end.record()
# end.synchronize()
# secs = start.time_till(end) * 1e-3
# print "CUDA execution time (random matrix generation): ", secs
# self.data = self.gpu_new_data
# new_data = self.gpu_new_data.get()
# self.hic_matrix = new_data
# else:
# self.data = self.gpu_2d_hic_matrix
self.data = self.gpu_2d_hic_matrix
def update_texture_4_sub(self, fact):
if self.perform_sub_sample:
start = cuda.Event()
end = cuda.Event()
size_block_x = 32
size_block_y = 32
n_blocks_x = int(self.sub_n_frags)//(size_block_x) + 1
n_blocks_y = int(self.sub_n_frags)//(size_block_y) + 1
self.grid = (n_blocks_x, n_blocks_y, 1)
self.block = (size_block_x, size_block_y, 1)
new_data = np.zeros((self.sub_n_frags, self.sub_n_frags), dtype=np.float32)
self.gpu_new_data = ga.to_gpu(new_data)
start.record()
self.gen_rand_mat(self.gpu_2d_hic_matrix, self.gpu_new_data, self.rng_states, np.int32(self.n_generators),
self.sub_n_frags, np.float32(fact),
block=self.block, grid=self.grid, shared=0)
end.record()
end.synchronize()
secs = start.time_till(end) * 1e-3
print "CUDA execution time (random matrix generation): ", secs
self.data = self.gpu_new_data
new_data = self.gpu_new_data.get()
self.hic_matrix = new_data
else:
self.data = self.gpu_2d_hic_matrix
# self.data = self.gpu_2d_hic_matrix
# tex_ref = self.module.get_texref("texData")
# self.data.bind_to_texref_ext(tex_ref, channels=2, allow_double_hack=False, allow_offset=False)
def meminfo(self, kernel):
shared=kernel.shared_size_bytes
regs=kernel.num_regs
local=kernel.local_size_bytes
const=kernel.const_size_bytes
mbpt=kernel.max_threads_per_block
print("""=MEM=\nLocal:%d,\nShared:%d,\nRegisters:%d,\nConst:%d,\nMax Threads/B:%d"""%(local,shared,regs,
const,mbpt))
def loadProgram(self, filename):
#read in the Cuda source file as a string
f = open(filename, 'r')
fstr = "".join(f.readlines())
#create the program
self.module = pycuda.compiler.SourceModule(fstr, no_extern_c=True, options=["-keep", "-m64", "-cubin"])
# self.module = pycuda.compiler.SourceModule(fstr, no_extern_c=True,arch='sm_30',
# options=["-keep", "-m64", "-cubin"])
def update_neighbourhood(self,):
tmp_sorted = self.hic_matrix_sub_sampled.argsort(axis=1)
sorted_neighbours = []
for i in self.list_frag_to_sample:
all_idx = tmp_sorted[i, :]
pos = np.nonzero(all_idx == i)[0][0]
line = list(all_idx)
line.pop(pos)
print 'filtering neighbourhood of :', i
for j in self.list_to_pop_out:
line = np.array(line)
pos = np.nonzero(line == j)[0][0]
line = list(line)
line.pop(pos)
sorted_neighbours.append(line)
self.sorted_neighbours = np.array(sorted_neighbours)
def pop_out_pop_in_4_mh(self, id_f_pop, id_f_ins, mode, max_id, forward):
size_block = 256
block_ = (size_block, 1, 1)
grid_ = (int(self.n_frags // size_block + 1), 1)
# max_id = np.int32(ga.max(self.gpu_id_contigs).get())
# print 'max_id contig before pop out= ', max_id
start = cuda.Event()
end = cuda.Event()
start.record()
if forward:
gpu_vect_frags = self.gpu_vect_frags
else:
gpu_vect_frags = self.gpu_vect_frags_forward
self.pop_out(self.pop_gpu_vect_frags.get_ptr(), gpu_vect_frags.get_ptr(), self.pop_gpu_id_contigs,
np.int32(id_f_pop), max_id, self.n_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()
secs = start.time_till(end)*1e-3
max_id2 = np.int32(ga.max(self.pop_gpu_id_contigs).get())
# print 'max_id contig after pop out= ', max_id2
start.record()
modif_vector = self.collector_gpu_vect_frags[mode]
or_watson = np.int32(1)
or_crick = np.int32(-1)
# print "max_id from pop = ", max_id
if mode == 0:
self.simple_copy(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
self.n_frags,
block=block_, grid=grid_)
elif mode == 1:
self.flip_frag(self.collector_gpu_vect_frags[mode].get_ptr(), gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), self.n_frags,
block=block_, grid=grid_)
elif mode == 2:
self.pop_in_3(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_watson, self.n_frags,
block=block_, grid=grid_)
elif mode == 3:
self.pop_in_3(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_crick, self.n_frags,
block=block_, grid=grid_)
elif mode == 4:
self.pop_in_4(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_watson, self.n_frags,
block=block_, grid=grid_)
elif mode == 5:
self.pop_in_4(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_crick, self.n_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()
secs = start.time_till(end)*1e-3
def split_4_mh(self, id_fA, max_id, forward):
size_block = 256
block_ = (size_block, 1, 1)
grid_ = (int(self.n_frags // size_block + 1), 1)
# max_id = np.int32(ga.max(self.gpu_id_contigs).get())
start = cuda.Event()
end = cuda.Event()
mode = 0
id_start_split = 6
if forward:
gpu_vect_frags = self.gpu_vect_frags
else:
gpu_vect_frags = self.gpu_vect_frags_forward
for upstreamfA in range(0, 2):
start.record()
vect_frags_2_modif = self.collector_gpu_vect_frags[id_start_split + mode]
self.split(vect_frags_2_modif.get_ptr(), gpu_vect_frags.get_ptr(), self.trans1_gpu_id_contigs,
np.int32(id_fA), np.int32(upstreamfA), max_id, self.n_frags,
block=block_, grid=grid_)
mode += 1
def paste_4_mh(self, id_fA, id_fB, max_id, forward):
size_block = 256
block_ = (size_block, 1, 1)
grid_ = (int(self.n_frags // size_block + 1), 1)
start = cuda.Event()
end = cuda.Event()
if forward:
gpu_vect_frags = self.gpu_vect_frags
else:
gpu_vect_frags = self.gpu_vect_frags_forward
vect_frags_2_modif = self.collector_gpu_vect_frags[8]
# vect_frags_2_modif = self.collector_gpu_vect_frags[8]
start.record()
is_extremity_fA = (gpu_vect_frags.prev[id_fA] == -1) or (gpu_vect_frags.next[id_fA] == -1)
is_extremity_fB = (gpu_vect_frags.prev[id_fB] == -1) or (gpu_vect_frags.next[id_fB] == -1)
if is_extremity_fA and is_extremity_fB:
self.paste(vect_frags_2_modif.get_ptr(), gpu_vect_frags.get_ptr(),
np.int32(id_fA), np.int32(id_fB), max_id, self.n_frags,
block=block_, grid=grid_)
else:
self.simple_copy(vect_frags_2_modif.get_ptr(), gpu_vect_frags.get_ptr(),
self.n_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()
def pop_out_pop_in(self, id_f_pop, id_f_ins, mode, max_id):
size_block = 128
block_ = (size_block, 1, 1)
grid_ = (int(self.n_new_frags // size_block + 1), 1)
# max_id = np.int32(ga.max(self.gpu_id_contigs).get())
# print 'max_id contig before pop out= ', max_id
start = cuda.Event()
end = cuda.Event()
start.record()
self.pop_out(self.pop_gpu_vect_frags.get_ptr(), self.gpu_vect_frags.get_ptr(), self.pop_gpu_id_contigs,
np.int32(id_f_pop), max_id, self.n_new_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()
secs = start.time_till(end)*1e-3
max_id2 = np.int32(ga.max(self.pop_gpu_id_contigs).get())
# print 'max_id contig after pop out= ', max_id2
start.record()
modif_vector = self.collector_gpu_vect_frags[mode]
or_watson = np.int32(1)
or_crick = np.int32(-1)
# print "max_id from pop = ", max_id
if mode == 0: # eject frag
self.simple_copy(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
self.n_new_frags,
block=block_, grid=grid_)
elif mode == 1:
self.flip_frag(self.collector_gpu_vect_frags[mode].get_ptr(), self.gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), self.n_new_frags,
block=block_, grid=grid_)
elif mode == 2:
self.pop_in_1(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_watson, self.n_new_frags,
block=block_, grid=grid_)
elif mode == 3:
self.pop_in_1(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_crick, self.n_new_frags,
block=block_, grid=grid_)
elif mode == 4:
self.pop_in_2(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_watson, self.n_new_frags,
block=block_, grid=grid_)
elif mode == 5:
self.pop_in_2(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_crick, self.n_new_frags,
block=block_, grid=grid_)
elif mode == 6:
self.pop_in_3(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_watson, self.n_new_frags,
block=block_, grid=grid_)
elif mode == 7:
self.pop_in_3(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_crick, self.n_new_frags,
block=block_, grid=grid_)
# elif mode == 8:
# self.pop_in_4(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
# np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_watson, self.n_frags,
# block=block_, grid=grid_)
# elif mode == 9:
# self.pop_in_4(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
# np.int32(id_f_pop), np.int32(id_f_ins), max_id2, or_crick, self.n_frags,
# block=block_, grid=grid_)
elif mode == 8:
self.swap_activity(self.collector_gpu_vect_frags[mode].get_ptr(), self.pop_gpu_vect_frags.get_ptr(),
np.int32(id_f_pop), max_id2, self.n_new_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()
secs = start.time_till(end)*1e-3
def transloc(self, id_fA, id_fB, max_id):
size_block = 128
block_ = (size_block, 1, 1)
grid_ = (int(self.n_new_frags // size_block + 1), 1)
# max_id = np.int32(ga.max(self.gpu_id_contigs).get())
start = cuda.Event()
end = cuda.Event()
mode = 0
# id_start_transloc = 8
id_start_transloc = 9
for upstreamfA in range(0, 2):
start.record()
self.split(self.trans1_gpu_vect_frags.get_ptr(), self.gpu_vect_frags.get_ptr(), self.trans1_gpu_id_contigs,
np.int32(id_fA), np.int32(upstreamfA), max_id, self.n_new_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()
for upstreamfB in range(0, 2):
max_id1 = np.int32(ga.max(self.trans1_gpu_id_contigs).get())
# print 'max_id1 = ', max_id1
start.record()
self.split(self.trans2_gpu_vect_frags.get_ptr(), self.trans1_gpu_vect_frags.get_ptr(),
self.trans2_gpu_id_contigs,
np.int32(id_fB), np.int32(upstreamfB), max_id1, self.n_new_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()
max_id2 = np.int32(ga.max(self.trans2_gpu_id_contigs).get())
# print 'max_id2 = ', max_id2
curr_vect_trans = self.collector_gpu_vect_frags[id_start_transloc + mode]
start.record()
# self.simple_copy(curr_vect_trans.get_ptr(), self.trans2_gpu_vect_frags.get_ptr(), self.n_frags,
# block=block_, grid=grid_)
self.paste(curr_vect_trans.get_ptr(), self.trans2_gpu_vect_frags.get_ptr(),
np.int32(id_fA), np.int32(id_fB), max_id2, self.n_new_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()
mode += 1
def transloc_4_mh(self, id_fA, id_fB, max_id, forward):
size_block = 256
block_ = (size_block, 1, 1)
grid_ = (int(self.n_frags // size_block + 1), 1)
# max_id = np.int32(ga.max(self.gpu_id_contigs).get())
start = cuda.Event()
end = cuda.Event()
mode = 0
id_start_transloc = 9
if forward:
gpu_vect_frags = self.gpu_vect_frags
else:
gpu_vect_frags = self.gpu_vect_frags_forward
for upstreamfA in range(0, 2):
start.record()
self.split(self.trans1_gpu_vect_frags.get_ptr(), gpu_vect_frags.get_ptr(), self.trans1_gpu_id_contigs,
np.int32(id_fA), np.int32(upstreamfA), max_id, self.n_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()
for upstreamfB in range(0, 2):
# print "mode = ", mode
max_id1 = np.int32(ga.max(self.trans1_gpu_id_contigs).get())
if upstreamfB == 0 :
if gpu_vect_frags.next[id_fB] == -1:
perform_transloc = True
else:
perform_transloc = False
else:
if gpu_vect_frags.prev[id_fB] == -1:
perform_transloc = True
else:
perform_transloc = False
start.record()
curr_vect_trans = self.collector_gpu_vect_frags[id_start_transloc + mode]
if perform_transloc:
self.split(self.trans2_gpu_vect_frags.get_ptr(), self.trans1_gpu_vect_frags.get_ptr(),
self.trans2_gpu_id_contigs,
np.int32(id_fB), np.int32(upstreamfB), max_id1, self.n_frags,
block=block_, grid=grid_)
end.record()
end.synchronize()