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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 run the following code I get some memory associated error from CUDA API
Code:
@cuda.jit
def SIR(rng_states, y, particles, weight, beta, omega, gamma,
greater, equal, phi, phi_sub):
# 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)
# 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
y = np.ones((event_number, timestep), dtype = np.int8)
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)
vectoradd_time = timer() - start
print("sirModule1 took %f seconds" % vectoradd_time)
if __name__ == '__main__':
main()
Then I get
numba.cuda.cudadrv.driver.CudaAPIError: [715] Call to cuMemcpyDtoH results in UNKNOWN_CUDA_ERROR
numba.cuda.cudadrv.driver.CudaAPIError: [715] Call to cuMemFree results in UNKNOWN_CUDA_ERROR
errors....
Did anybody face the same problem? I checked online and some suggest that the problem arise from WDDM TDR but I thought thats for only Windows, right?
The following is the missing part of the code.
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
"""
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'))
"""
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):
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 / LOOKUP_TABLE[ 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 ]
But these are the device functions. Should this be a source of problem?
And for the error, I get the following error message where line 207 in my code is where I call SIR module.
Traceback (most recent call last):
File "CUDA_MonteCarlo_Testesr.py", line 214, in <module>
main()
File "CUDA_MonteCarlo_Testesr.py", line 207, in main
omega, gamma, greater, equal, phi, phi_sub)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/compiler.py", line 703, in __call__
cfg(*args)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/compiler.py", line 483, in __call__
sharedmem=self.sharedmem)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/compiler.py", line 585, in _kernel_call
wb()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/compiler.py", line 600, in <lambda>
retr.append(lambda: devary.copy_to_host(val, stream=stream))
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/devicearray.py", line 198, in copy_to_host
_driver.device_to_host(hostary, self, self.alloc_size, stream=stream)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 1597, in device_to_host
fn(host_pointer(dst), device_pointer(src), size, *varargs)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 288, in safe_cuda_api_call
self._check_error(fname, retcode)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 323, in _check_error
raise CudaAPIError(retcode, msg)
numba.cuda.cudadrv.driver.CudaAPIError: [715] Call to cuMemcpyDtoH results in UNKNOWN_CUDA_ERROR
Traceback (most recent call last):
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/utils.py", line 647, in _exitfunc
f()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/utils.py", line 571, in __call__
return info.func(*info.args, **(info.kwargs or {}))
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 1099, in deref
mem.free()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 1013, in free
self._finalizer()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/utils.py", line 571, in __call__
return info.func(*info.args, **(info.kwargs or {}))
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 863, in core
deallocations.add_item(dtor, handle, size=bytesize)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 519, in add_item
self.clear()
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 530, in clear
dtor(handle)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 288, in safe_cuda_api_call
self._check_error(fname, retcode)
File "/home/ryan/anaconda3/envs/py53/lib/python3.5/site-packages/numba/cuda/cudadrv/driver.py", line 323, in _check_error
raise CudaAPIError(retcode, msg)
numba.cuda.cudadrv.driver.CudaAPIError: [715] Call to cuMemFree results in UNKNOWN_CUDA_ERROR
803
asked Apr 23 '26 02:04
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 using numba.cuda.device_array() for that.
You don't seem to be transferring the array y to 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
#
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