Hi.
I've just finished writing my first pyopencl program. While the OpenCL C
part build()s, calling it fails with a TypeError in pyopencl/__init__.py
I'm trying to run this on an Ubuntu 13.10 saucy with the python-pyopencl
2013.1~git20130613-1 provided in the Ubuntu repo (I've tried installing the
2013.2 version via pip, but run into compile errors due to it not finding
CL/cl.h in /opt/AMDAPP/include/; how can I tell pip that it should make the
compiler look there?).
Here are call, traceback and error:
program.simulate(queue, instances, None,
np.int32(num_particles),
position_buf,
dest_buf)
Traceback (most recent call last):
File "./particle_sim.py", line 88, in <module>
simulate()
File "./particle_sim.py", line 65, in simulate
dest_buf)
File "/usr/lib/python2.7/dist-packages/pyopencl/__init__.py", line 567,
in kernel_call
global_offset, wait_for, g_times_l=g_times_l)
TypeError: object of type 'int' has no len()
The full code is in the attachment for error-reproduction (and, as I've
learned in the meantime, as an example on how not to do pairwise
computation of particle influences). Thanks in advance.
Liebe Grüße,
Sebastian Hoffmann
#!/usr/bin/env python
import pyopencl as cl
import numpy as np
import random
from math import sqrt
# Functions for creating the initial particle data
def initial_position():
x = random.gauss(0, 1)
y = random.gauss(0, 1)
z = random.gauss(0, 1)
distance = sqrt(x**2 + y**2 + z**2)
return [x/distance, y/distance, z/distance]
# Main program
def simulate(num_particles = 10):
# OpenCL basics
context = cl.create_some_context()
queue = cl.CommandQueue(context)
# Setting up particles (data and buffers)
position = np.array([initial_position()
for i in range(0, num_particles)]).astype(np.float32)
interactions = np.zeros((num_particles)).astype(np.int32)
# OpenCL buffers
memflag = cl.mem_flags
position_buf = cl.Buffer(context,
memflag.READ_ONLY | memflag.COPY_HOST_PTR,
hostbuf = position)
dest_buf = cl.Buffer(context,
memflag.WRITE_ONLY,
interactions.nbytes)
# The actual invocation
program = cl.Program(context, """
int2 particle_pair (long i, int num_particles) {
int line = i / num_particles;
int pair_in_line = i % num_particles;
int x = line; // superfluous
int y = pair_in_line + line + 1;
int2 pairing;
if (y < num_particles) {
pairing.s0 = x;
pairing.s1 = y;
}
else {
pairing.s0 = num_particles - 2 - x;
pairing.s1 = (num_particles - 1) - (y - num_particles);
}
return pairing;
}
__kernel void simulate(int num_particles,
__global const float *position,
__global int *interactions) {
int i = get_global_id(0);
int2 pair = particle_pair(i, num_particles);
interactions[pair.s0] += 1;
interactions[pair.s1] += 1;
}
""").build()
instances = ((num_particles-1) * num_particles) / 2
program.simulate(queue, instances, None,
np.int32(num_particles),
position_buf,
dest_buf)
# Copying the results back into the host
cl.enqueue_copy(queue, interactions, dest_buf)
print(interactions)
# FIXME: This is debugging code to test the pairwise combination algorithm.
# Delete this once it's translated to OpenCL C.
matrix = np.zeros((num_particles, num_particles))
for i in range(0, instances):
line = int(i / num_particles)
pair_in_line = i % num_particles
x = line
y = pair_in_line + line + 1
if y < num_particles:
matrix[x, y] = 1
#print(x,y)
else:
x = num_particles - 2 - x
y = (num_particles - 1) - (y - num_particles)
matrix[x, y] = 1
#print(x,y)
#print(matrix)
if __name__ == '__main__':
simulate()
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