CUDA API Error On Python With Numba
I'm kind of new to numba and was trying to speed up my monte carlo method with it. Im currently working on Ubuntu 14.04 with GeForce 950M. The CUDA version is 8.0.61. When I try to
Solution 1:
I think there may be 2 problems.
I'm not sure your use of
LOOKUP_TABLE = cuda.to_device(outside of main is valid. I guess you are trying to create a device array, but I think you should be usingnumba.cuda.device_array()for that.You don't seem to be transferring the array
yto the device properly for use.
When I make those two changes, the code seems to run without CUDA runtime error for me:
# cat t1.py
import numpy as np
import numba as nb
from timeit import default_timer as timer
# from matplotlib import pyplot as pt
import math
from numba import cuda
from numba.cuda.random import create_xoroshiro128p_states, xoroshiro128p_normal_float32
"""
Look up table for factorial
"""
"""
arr_sum - sum element in array
"""
@cuda.jit(device=True)
def arr_sum(arr):
result = 0
for i in range(arr.size):
result = result + arr[i]
return result
"""
dot - dot product of arr1 and arr2
"""
@cuda.jit(device=True)
def dot(arr1, arr2):
result = 0
for i in range(arr1.size):
result = arr1[i]*arr2[i] + result
return result
"""
arr_div - divide element in array
"""
@cuda.jit(device=True)
def arr_div(arr, div):
thread_id = cuda.threadIdx.x
arr[thread_id] = arr[thread_id]/div
"""
SIR module (sample_draw) - module drawing sample for time t (rampling model)
"""
@cuda.jit(device=True)
def sirModule_sample_draw(rng_states, inp, beta, omega, out):
"""Find a value less than 1 from nomral distribution"""
thread_id = cuda.threadIdx.x
# draw candidate sample from normal distribution and store
# when less than 1
while True:
candidate = inp[thread_id] + beta + omega * xoroshiro128p_normal_float32(rng_states, thread_id)
if candidate < 1:
out[thread_id] = candidate
break
"""
SIR module (weight calculation) - weight calculation method
"""
@cuda.jit(device=True)
def sirModule_weight(current, previous, weight, out, y, beta, omega, gamma, lt):
thread_id = cuda.threadIdx.x
PI = 3.14159265359
# calculate the pdf/pmf of given state
Z = ( current[thread_id] - ( previous[ thread_id ] + beta ) ) / omega
p1_div_p3 = 1.0 / 2.0 * ( 1.0 + math.erf( Z ) )
mu = math.log( 1 + math.exp( gamma * current[ thread_id ] ) )
p2 = math.exp( mu ) * mu**y / lt[ y ]
out[thread_id] = weight[thread_id]*p2*p1_div_p3
"""
SIR module (phi distribution calculator)
"""
@cuda.jit(device=True)
def sirModule_tau(current, beta, omega, phi, phi_sub):
thread_id = cuda.threadIdx.x
# calculate phi distribution and subtract from 1
Z = ( 1 - ( current[ thread_id ] + beta ) ) / omega
phi[ thread_id ] = 1.0 / 2.0 * ( 1.0 + math.erf( Z ) )
phi_sub[ thread_id ] = 1 - phi[ thread_id ]
@cuda.jit
def SIR(rng_states, y, particles, weight, beta, omega, gamma,
greater, equal, phi, phi_sub, lt):
# thread/block index for accessing data
tx = cuda.threadIdx.x # Thread id in a 1D block = particle index
ty = cuda.blockIdx.x # Block id in a 1D grid = event index
bw = cuda.blockDim.x # Block width, i.e. number of threads per block = particle number
pos = tx + ty * bw # computed flattened index inside the array
# get current event y_t
y_current = y[ ty ]
# get number of time steps
tn = y_current.size
# iterator over timestep
for i in range(1, tn):
# draw samples
sirModule_sample_draw(rng_states, particles[ty][i-1], beta,
omega, particles[ty][i])
# get weight
sirModule_weight(particles[ty][i], particles[ty][i-1], weight[ty][i-1], weight[ty][i], y_current[i], beta, omega, gamma, lt)
# normalize weight
weight_sum = arr_sum(weight[ty][i])
arr_div(weight[ty][i], weight_sum)
# calculate tau
sirModule_tau(particles[ty][i], beta, omega, phi, phi_sub)
# update greater and equal
greater[ty][i] = greater[ty][i-1]*dot(weight[ty][i-1], phi)
equal[ty][i] = greater[ty][i-1]*dot(weight[ty][i-1], phi_sub)
def main():
beta = 1
omega = 1
gamma = 2
pn = 100
event_number = 50
timestep = 100
LOOKUP_TABLE = cuda.to_device(np.array([
1, 1, 2, 6, 24, 120, 720, 5040, 40320,
362880, 3628800, 39916800, 479001600,
6227020800, 87178291200, 1307674368000,
20922789888000, 355687428096000, 6402373705728000,
121645100408832000, 2432902008176640000], dtype='int64'))
hy = np.ones((event_number, timestep), dtype = np.uint32)
print(hy.size)
print(hy)
y = cuda.to_device(hy)
particles = cuda.to_device(np.zeros((event_number, timestep, pn), dtype = np.float32))
weight = cuda.to_device(np.ones((event_number, timestep, pn), dtype = np.float32))
greater = cuda.to_device(np.ones((event_number, timestep), dtype = np.float32))
equal = cuda.to_device(np.ones((event_number, timestep), dtype = np.float32))
phi = cuda.to_device(np.zeros(particles[0][0].size, dtype = np.float32))
phi_sub = cuda.to_device(np.zeros(particles[0][0].size, dtype = np.float32))
rng_states = create_xoroshiro128p_states(pn, seed=1)
start = timer()
SIR[event_number, pn](rng_states, y, particles, weight, beta, omega, gamma, greater, equal, phi, phi_sub, LOOKUP_TABLE)
vectoradd_time = timer() - start
print("sirModule1 took %f seconds" % vectoradd_time)
cuda.synchronize()
if __name__ == '__main__':
main()
# cuda-memcheck python t1.py
========= CUDA-MEMCHECK
5000
[[1 1 1 ..., 1 1 1]
[1 1 1 ..., 1 1 1]
[1 1 1 ..., 1 1 1]
...,
[1 1 1 ..., 1 1 1]
[1 1 1 ..., 1 1 1]
[1 1 1 ..., 1 1 1]]
sirModule1 took 0.840958 seconds
========= ERROR SUMMARY: 0 errors
#
Solution 2:
Solved! I am working on Ubuntu 16.04. When I installed Numba for the first time, numba.cuda functions worked fine. However later I encountered these kind of errors
raise CudaAPIError(retcode, msg)
CudaAPIError: Call to cuMemcpyHtoD results in CUDA_ERROR_LAUNCH_FAILED
These errors are encountered when you put your system on 'suspend'. In order to avoid such errors, restart your system or don't suspend.
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