def list_from_cuda_array(a, n, release_memory=True):
c_int_p = ct.POINTER(ct.c_int)
host_array = (c_int_p * n)()
host_ptr = ct.cast(host_array, c_int_p)
elem_size = ct.sizeof(ct.c_int)
cudart.cuda_memcpy(host_ptr, a, n * elem_size, 'default')
if release_memory:
cudart.cuda_free(a)
a = np.ndarray(shape=(n, ), dtype=np.int32, buffer=host_array, order='F')
return a.tolist()
def list_from_cuda_array(a, n, release_memory=True):
c_int_p = ct.POINTER(ct.c_int)
host_array = (c_int_p * n)()
host_ptr = ct.cast(host_array, c_int_p)
elem_size = ct.sizeof(ct.c_int)
cudart.cuda_memcpy(host_ptr, a, n * elem_size, 'default')
if release_memory:
cudart.cuda_free(a)
a = np.ndarray(shape=(n, ), dtype=np.int32, buffer=host_array, order='F')
return a.tolist()
def test_dependencies(self):
N = 10
k = 6
execution_checklist = cuda_array_from_list([0] * (k * N + 1))
test_results = cuda_array_from_list([0] * (k * N + 1))
contexts = [GpuContext() for _ in xrange(k)]
blocking_nodes = list()
blocking_nodes.append(cuda_array_from_list([]))
for i in xrange(N):
blocking_nodes.append(cuda_array_from_list([i * k]))
blocking_nodes.append(
cuda_array_from_list(range(i * k + 1, i * k + 4)))
blocking_nodes.append(
cuda_array_from_list(range(i * k + 4, i * k + 6)))
for context_id in xrange(5, 6):
test_dependencies(contexts[context_id].cuda_stream, 0,
blocking_nodes[0], 0, execution_checklist,
test_results)
contexts[context_id].block(*contexts[:3])
for i in xrange(N):
for context_id in xrange(3):
test_dependencies(contexts[context_id].cuda_stream,
i * k + context_id + 1,
blocking_nodes[i * 3 + 1], 1,
execution_checklist, test_results)
for context_id in xrange(3, 5):
contexts[context_id].wait(*contexts[:3])
test_dependencies(contexts[context_id].cuda_stream,
i * k + context_id + 1,
blocking_nodes[i * 3 + 2], 3,
execution_checklist, test_results)
for context_id in xrange(5, 6):
contexts[context_id].wait(*contexts[3:5])
test_dependencies(contexts[context_id].cuda_stream,
i * k + context_id + 1,
blocking_nodes[i * 3 + 3], 2,
execution_checklist, test_results)
contexts[context_id].block(*contexts[:3])
for nodes in blocking_nodes:
cudart.cuda_free(nodes)
test_results = list_from_cuda_array(test_results, k * N + 1)
execution_checklist = list_from_cuda_array(execution_checklist,
k * N + 1)
self.assertEqual(
sum(test_results) + sum(execution_checklist), 2 * (k * N + 1))
def test_dependencies(self):
N = 10
k = 6
execution_checklist = cuda_array_from_list([0] * (k * N + 1))
test_results = cuda_array_from_list([0] * (k * N + 1))
contexts = [GpuContext() for _ in xrange(k)]
blocking_nodes = list()
blocking_nodes.append(cuda_array_from_list([]))
for i in xrange(N):
blocking_nodes.append(cuda_array_from_list([i*k]))
blocking_nodes.append(cuda_array_from_list(range(i*k + 1, i*k + 4)))
blocking_nodes.append(cuda_array_from_list(range(i*k + 4, i*k + 6)))
for context_id in xrange(5, 6):
test_dependencies(contexts[context_id].cuda_stream, 0, blocking_nodes[0], 0, execution_checklist, test_results)
contexts[context_id].block(*contexts[:3])
for i in xrange(N):
for context_id in xrange(3):
test_dependencies(contexts[context_id].cuda_stream, i * k + context_id + 1, blocking_nodes[i*3+1], 1, execution_checklist, test_results)
for context_id in xrange(3, 5):
contexts[context_id].wait(*contexts[:3])
test_dependencies(contexts[context_id].cuda_stream, i * k + context_id + 1, blocking_nodes[i*3+2], 3, execution_checklist, test_results)
for context_id in xrange(5, 6):
contexts[context_id].wait(*contexts[3:5])
test_dependencies(contexts[context_id].cuda_stream, i * k + context_id + 1, blocking_nodes[i*3+3], 2, execution_checklist, test_results)
contexts[context_id].block(*contexts[:3])
for nodes in blocking_nodes:
cudart.cuda_free(nodes)
test_results = list_from_cuda_array(test_results, k * N + 1)
execution_checklist = list_from_cuda_array(execution_checklist, k * N + 1)
self.assertEqual(sum(test_results) + sum(execution_checklist), 2 * (k * N + 1))
