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randomwalk.py
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randomwalk.py
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from numba import cuda, float64
import scipy.io as sci
import numpy as np
import time as tim
import sys
from numba.cuda.random import create_xoroshiro128p_states, xoroshiro128p_uniform_float64
@cuda.jit(device=True)
def get_node(p, cn, rng_state):
prob = p
currnode = cn
rng_states = rng_state
thread_id = cuda.grid(1)
r = cuda.random.xoroshiro128p_uniform_float64(rng_states,thread_id)
upto = 0
c=0
while c < 116:
#print(upto)
if (upto + prob[(currnode)*116+c]) >= r:
currnode = c
break
upto += prob[(currnode)*116+c]
c += 1
return currnode
@cuda.jit(device=True)
def loop(p, t, so, ta, stepp, ste, rng_state, reg):
source = so -1
targ = ta -1
regions = reg
steps = stepp
rng_states = rng_state
prob = p
time = t
delay = 0;
currnode = source
prevnode = -1;
step = 0
if source != targ:
while currnode != targ:
step += 1
currnode = get_node(prob,currnode, rng_states)
if prevnode != -1:
delay += time[(prevnode)*116+currnode]
if step>13456:
step = 13456
break
prevnode = currnode
ste[0]= delay
return step
@cuda.jit
def random_walk(prob, time, regions, steps, delay, ste, rng_states):
targ = cuda.threadIdx.x+1
source = cuda.blockIdx.x + 1
p = prob
t = time
r=regions
ids = (source-1)*116 + targ-1
c = cuda.local.array(shape=4,dtype=float64)
steps[ids] = loop(p, t, source,targ,steps, c, rng_states,r)
delay[ids] = c[0]
#print("source: ", source, " target: ", targ, " steps: ", steps[ids], " delay: ")
cuda.syncthreads()
start_time = tim.time()
cuda.config.ENABLE_CUDASIM = 1
name = sys.argv[1]
s = sci.loadmat(name+"_prob.mat")
s2 = sci.loadmat(name+"_time.mat")
prob = np.matrix(s[name+"_prob"], dtype=np.float64)
time = np.matrix(s2[name+"_time"], dtype=np.float64)
p = np.array(prob)
p = p.ravel()
t = np.array(time)
t = t.ravel()
regions = np.array(range(1,117), dtype=np.uint8)
cuda.select_device(0)
stream = cuda.stream()
steps = np.zeros((116*116,), dtype=np.uint64,)
ste = np.zeros((116*116,), dtype=np.float64,)
tt = np.zeros((116*116,), dtype=np.uint64,)
stream = cuda.stream()
g_prob = cuda.to_device(p,stream=stream)
g_time = cuda.to_device(t,stream=stream)
g_reg = cuda.to_device(regions,stream=stream)
g_ste = cuda.to_device(ste,stream=stream)
rng_states = create_xoroshiro128p_states(13456, seed=int(tim.time()))
g_steps = cuda.to_device(steps,stream=stream)
g_del = cuda.to_device(tt,stream=stream)
ranwalk = np.empty((10000,116,116), dtype=g_steps.dtype)
delay = np.empty((10000,116,116), dtype=g_steps.dtype)
output = np.empty(shape=g_steps.shape, dtype=g_steps.dtype)
delay_o = np.empty(shape=g_steps.shape, dtype=g_steps.dtype)
for i in range(10000):
random_walk[116, 116](g_prob, g_time, g_reg, g_steps, g_del, g_ste, rng_states)
print(i)
#print("g_steps size:", g_steps.size, " output size: ", output.size)
cuda.cudadrv.driver.Context.synchronize(cuda.current_context())
#time.sleep(0.7)
#print("synchronized")
output = g_steps.copy_to_host(stream=stream)
delay_o = g_del.copy_to_host(stream=stream)
#print("ovde zaglaviv")
sd = output
sc = delay_o
ranwalk[i,:,:] = sd.reshape((116,116))
delay[i,:,:] = sc.reshape((116,116))
del rng_states
#del g_steps
rng_states = create_xoroshiro128p_states(116*116, seed=np.uint64(tim.time()))
#g_steps = cuda.to_device(steps)
sci.savemat(name +'_randomwalk_steps.mat', {'ranwalk':ranwalk})
sci.savemat(name +'_randomwalk_time.mat', {'delay':delay})
cuda.close()
elapsed_time = tim.time() - start_time
print("elapsed time: ", elapsed_time)