def laplace_3d_cuda(d, n):
L = d.shape[0]
M = d.shape[1]
N = d.shape[2]
blockdim = (8, 8, 8)
griddim = (L // blockdim[0], M // blockdim[1], N // blockdim[2])
#print(griddim)
stream = cuda.stream()
dd = cuda.to_device(d, stream)
dn = cuda.to_device(n, stream)
#%timeit -n 32 -r 16 d0td1_cuda_kernel[griddim, blockdim](dd, dn)
for i in range(0, 100):
laplace_3d_cuda_opt_kernel[griddim, blockdim, stream](dd, dn, L, M, N)
evtstart = cuda.event(timing=True)
evtend = cuda.event(timing=True)
evtstart.record(stream)
for i in range(100):
laplace_3d_cuda_opt_kernel[griddim, blockdim, stream](dd, dn, L, M, N)
evtend.record(stream)
evtend.synchronize()
print(cuda.event_elapsed_time(evtstart, evtend) / 100.)
dd.to_host()
def main():
inp = np.arange(1000000, dtype=np.int32)
factor = 4
start, end = cuda.event(True), cuda.event(True)
reses = []
for (name, f) in [
("not shared", mult_by_x_not_shared),
("shared", mult_by_x_shared),
("not shared", mult_by_x_not_shared),
]:
times = []
for i in range(100):
d_out = cuda.device_array_like(inp)
start.record()
f[blocks, threadsPerBlock](cuda.to_device(inp), d_out,
cuda.to_device(np.array([factor])))
end.record()
end.synchronize()
out = d_out.copy_to_host()
# Compilation...
if i != 0:
times.append(cuda.event_elapsed_time(start, end))
print(
f"{name}: {np.mean(times):.2f} +/- {np.std(times) / np.sqrt(len(times)):.3f} (max: {np.max(times):.2f})"
)
reses.append(out)
assert np.all([reses[0] == reses_i for reses_i in reses])
def __exit__(self, *args):
cuda.select_device(self.gpu)
suffix = 'ms (' + self.label + ')' if self.label else 'ms'
self.end.record()
self.end.synchronize()
time = cuda.event_elapsed_time(self.start, self.end)
print('elapsed time:', int(time), suffix)
def main(image1, image2):
streams = []
start_events = []
end_events = []
data1_gpu = []
data2_gpu = []
gpu_out = []
out = []
data_image1 = np.array(image1)
data_image2 = np.array(image2)
print(data_image1.shape, data_image2.shape)
shpape_A = data_image1.shape
# prevaod na 1 rozmerne pole
data_image1 = data_image1.ravel()
data_image2 = data_image2.ravel()
input1 = np.split(data_image1, X)
input2 = np.split(data_image1, X)
for _ in range(len(input1)):
streams.append(cuda.stream())
start_events.append(cuda.event())
end_events.append(cuda.event())
for i in range(len(input1)):
data1_gpu.append(cuda.to_device(input1[i], stream=streams[i]))
data2_gpu.append(cuda.to_device(input2[i], stream=streams[i]))
t_start = perf_counter()
for i in range(len(input1)):
start_events[i].record(streams[i])
sumImages[1, 32, streams[i]](data1_gpu[i], data2_gpu[i])
t_end = perf_counter()
for i in range(len(input1)):
end_events[i].record(streams[i])
gpu_out.append(data2_gpu[i].copy_to_host(stream=streams[i]))
for i in range(len(gpu_out)):
out = np.concatenate((out, gpu_out[i]))
kernel_times = []
for k in range(len(input1)):
kernel_times.append(
cuda.event_elapsed_time(start_events[k], end_events[k]))
out = out.reshape(shpape_A)
out = out.astype('uint8')
out = Image.fromarray(out)
out.save("out_stream.png")
print(f'Total time: {t_end - t_start}')
print(f'Mean kernel duration (milliseconds): {np.mean(kernel_times)}')
print(f'Mean kernel standard deviation \
(milliseconds): {np.std(kernel_times)}')
def main():
# Compile
lots_of_copies()
few_copies()
# Now benchmark
start, end = cuda.event(timing=True), cuda.event(timing=True)
n = 200
for f in [lots_of_copies, few_copies]:
times = []
for _ in range(n):
start.record()
f()
end.record()
end.synchronize()
t = cuda.event_elapsed_time(start, end)
times.append(t)
print(f.__name__, np.mean(times), np.std(times) / np.sqrt(n))
def test_last_block():
MAX_TPB = 512
n = 1024
a = np.arange(n).astype(np.int32)
reference = np.empty_like(a)
start = timer()
MyScan.exprefixsumNumba(a, reference, init=0)
end = timer()
auxidx = -1
elb = a.size
p2elb = np.int(np.ceil(np.log2(elb)))
telb = 2 ** p2elb
tlb = telb / 2
startIdx = 0
sm_size = telb * a.itemsize
aux = np.empty(1, dtype=np.int8)
trash = cuda.device_array(1)
e1, e2 = cuda.event(), cuda.event()
e1.record()
MyScan.last_scan[1, tlb, 0, sm_size](a, aux, -1, elb, startIdx)
e2.record()
print "CPU took: ", (end - start) * 1000, " ms"
print "Kernel took: ", cuda.event_elapsed_time(e1, e2), " ms"
print (a == reference).all()
def last_block_test():
MAX_TPB = 512
n = 1024
a = np.arange(n).astype(np.int32)
reference = np.empty_like(a)
start = timer()
scan.exprefixsumNumba(a, reference, init = 0)
end = timer()
auxidx = -1
elb = a.size
p2elb = np.int(np.ceil(np.log2(elb)))
telb = 2 ** p2elb
tlb = telb / 2
startIdx = 0
sm_size = telb * a.itemsize
aux = np.empty(1,dtype=np.int8)
trash = cuda.device_array(1)
e1, e2 = cuda.event(), cuda.event()
e1.record()
scan.last_scan[1, tlb, 0, sm_size](a, aux, -1, elb, startIdx)
e2.record()
print "CPU took: ", (end - start) * 1000, " ms"
print "Kernel took: ", cuda.event_elapsed_time(e1,e2), " ms"
print (a == reference).all()
d_z2 = cuda.to_device(z2.astype(np.float32))
d_w2 = cuda.to_device(w2.astype(np.float32))
d_rbins_squared = cuda.to_device(DEFAULT_RBINS_SQUARED.astype(np.float32))
d_result_nb = cuda.device_array_like(result.astype(np.float32))
# running the Numba jit kernel
for i in range(4):
if i > 0:
start.record()
_s = time.time()
count_weighted_pairs_3d_cuda[blocks,
threads](d_x1, d_y1, d_z1, d_w1, d_x2,
d_y2, d_z2, d_w2,
d_rbins_squared, d_result_nb)
if i > 0:
end.record()
end.synchronize()
_e = time.time()
timing_nb += cuda.event_elapsed_time(start, end)
timing_nb_wall += (_e - _s)
print('numba events:', timing_nb / 3, 'ms')
print('numba wall :', timing_nb_wall / 3 * 1000, 'ms')
# print(count_weighted_pairs_3d_cuda.inspect_types())
if kind in ['both']:
# check that the CUDA kernel agrees with the Numba kernel
assert cp.allclose(d_result_cp, d_result_nb, rtol=5E-4)
def mst_cluster_coassoc():
t1,t2 = Timer(), Timer()
#foldername = "/home/courses/aac2015/diogoaos/QCThesis/datasets/gaussmix1e4/"
foldername = home + "QCThesis/datasets/gaussmix1e4/"
print "Loading datasets"
t1.tic()
# dest = np.genfromtxt(foldername + "prot_dest.csr", dtype = np.int32, delimiter=",")
# weight = np.genfromtxt(foldername + "prot_weight.csr", dtype = np.float32, delimiter=",")
# fe = np.genfromtxt(foldername + "prot_fe.csr", dtype = np.int32, delimiter=",")
dest = np.genfromtxt(foldername + "full_dest.csr", dtype = np.int32, delimiter=",")
weight = np.genfromtxt(foldername + "full_weight.csr", dtype = np.float32, delimiter=",")
fe = np.genfromtxt(foldername + "full_fe.csr", dtype = np.int32, delimiter=",")
t1.tac()
print "loading elapsed time : ", t1.elapsed
fe = fe[:-1]
od = np.empty_like(fe)
outdegree_from_firstedge(fe, od, dest.size)
# fix weights to dissimilarity
weight = 100 - weight
print "# edges : ", dest.size
print "# vertices : ", fe.size
print "edges/vertices ratio : ", dest.size * 1.0 / fe.size
t1.tic()
mst, n_edges = boruvka_minho_seq(dest, weight, fe, od)
t1.tac()
print "seq: time elapsed : ", t1.elapsed
print "seq: mst size :", mst.size
print "seq: n_edges : ", n_edges
if n_edges < mst.size:
mst = mst[:n_edges]
mst.sort()
ev1,ev2 = cuda.event(), cuda.event()
ev1.record()
d_dest = cuda.to_device(dest)
d_weight = cuda.to_device(weight)
d_fe = cuda.to_device(fe)
d_od = cuda.to_device(od)
ev2.record()
send_graph_time = cuda.event_elapsed_time(ev1,ev2)
t2.tic()
mst2, n_edges2 = boruvka_minho_gpu(d_dest, d_weight, d_fe, d_od, MAX_TPB=512, returnDevAry = True)
t2.tac()
ev1.record()
mst2 = mst2.copy_to_host()
n_edges2 = n_edges2.getitem(0)
ev2.record()
recv_mst_time = cuda.event_elapsed_time(ev1,ev2)
print "gpu: send graph time : ", send_graph_time
print "gpu: time elapsed : ", t2.elapsed
print "gpu: rcv mst time : ", recv_mst_time
print "gpu: mst size :", mst2.size
print "seq: n_edges : ", n_edges2
if n_edges2 < mst2.size:
mst2 = mst2[:n_edges2]
mst2.sort()
if n_edges == n_edges2:
mst_is_equal = (mst == mst2).all()
else:
mst_is_equal = False
print "mst gpu == seq : ", mst_is_equal
for i in range(num_arrays):
# Configure the blocks
threadsperblock = (16, 16)
blockspergrid_x = int(math.ceil(A.shape[0] / threadsperblock[0]))
blockspergrid_y = int(math.ceil(B.shape[1] / threadsperblock[1]))
blockspergrid = (blockspergrid_x, blockspergrid_y)
# Start the kernel
start_events[i].record(streams[i])
matmul[blockspergrid, threadsperblock, streams[i]](A_gpu[i], B_gpu[i],
C_gpu[i])
for i in range(num_arrays):
end_events[i].record(streams[i])
for i in range(num_arrays):
# Copy the result back to the host
C_out.append(C_gpu[i].copy_to_host(stream=streams[i]))
t_end = perf_counter()
kernel_times = []
for i in range(num_arrays):
kernel_times.append(cuda.event_elapsed_time(start_events[i],
end_events[i]))
print('Total time: %f' % (t_end - t_start))
print('Mean kernel duration (milliseconds): %f' % numpy.mean(kernel_times))
print('Mean kernel standard deviation (milliseconds): %f' %
numpy.std(kernel_times))
def _cu_label(self, data, centroids):
#WARNING: data is being transposed when sending to GPU
data_ev1, data_ev2 = cuda.event(), cuda.event()
labels_ev1, labels_ev2 = cuda.event(), cuda.event()
dists_ev1, dists_ev2 = cuda.event(), cuda.event()
N, D = data.shape
K, cD = centroids.shape
if self._cuda_mem not in ('manual','auto'):
raise Exception("cuda_mem = \'manual\' or \'auto\'")
if self._gridDim is None or self._blockDim is None:
self._compute_cuda_dims(data)
labels = np.empty(N, dtype=np.int32)
if self._cuda_mem == 'manual':
# copy dataset and centroids, allocate memory
## cuda persistent handles
# avoids redundant data transfer
# if dataset has not been sent to device, send it and save handle
if self._cudaDataHandle is None:
dataT = np.ascontiguousarray(data.T)
self.man_prof['data_ev1'].record()
dData = cuda.to_device(dataT)
self.man_prof['data_ev2'].record()
self.man_prof['data_ev2'].synchronize()
time_ms = cuda.event_elapsed_time(self.man_prof['data_ev1'],
self.man_prof['data_ev2'])
self.man_prof['data_timings'].append(time_ms)
self._cudaDataHandle = dData
# otherwise just use handle
else:
dData = self._cudaDataHandle
# avoids creating labels array in device more than once
if self._cuda_labels_handle is None:
dLabels = cuda.device_array_like(labels)
self._cuda_labels_handle = dLabels
else:
dLabels = self._cuda_labels_handle
# avoids creating dists array in device more than once
if self._cuda_dists_handle is None:
dDists = cuda.device_array_like(self._dists)
self._cuda_dists_handle = dDists
else:
dDists = self._cuda_dists_handle
# copy centroids to device
self.man_prof['centroids_ev1'].record()
dCentroids = cuda.to_device(centroids)
self.man_prof['centroids_ev2'].record()
# launch kernel
self.man_prof['kernel_ev1'].record()
_cu_label_kernel_dists[self._gridDim, self._blockDim](dData,
dCentroids,
dLabels,
dDists)
self.man_prof['kernel_ev2'].record()
# cuda.synchronize()
# self.man_prof['kernel_ev2'].synchronize()
# copy labels from device to host
self.man_prof['labels_ev1'].record()
dLabels.copy_to_host(ary=labels)
self.man_prof['labels_ev2'].record()
# copy distance to centroids from device to host
self.man_prof['dists_ev1'].record()
dists = dDists.copy_to_host()
self.man_prof['dists_ev2'].record()
self._dists = dists
# synchronize host with gpu before computing times
self.man_prof['dists_ev2'].synchronize()
# store timings
time_ms = cuda.event_elapsed_time(self.man_prof['centroids_ev1'],
self.man_prof['centroids_ev2'])
self.man_prof['centroids_timings'].append(time_ms)
time_ms = cuda.event_elapsed_time(self.man_prof['kernel_ev1'],
self.man_prof['kernel_ev2'])
self.man_prof['kernel_timings'].append(time_ms)
time_ms = cuda.event_elapsed_time(self.man_prof['labels_ev1'],
self.man_prof['labels_ev2'])
self.man_prof['labels_timings'].append(time_ms)
time_ms = cuda.event_elapsed_time(self.man_prof['dists_ev1'],
self.man_prof['dists_ev2'])
self.man_prof['dists_timings'].append(time_ms)
elif self._cuda_mem == 'auto':
self.auto_prof['kernel_ev1'].record()
_cu_label_kernel_dists[self._gridDim,self._blockDim](data,
centroids,
labels,
self._dists)
self.auto_prof['kernel_ev2'].record()
time_ms = cuda.event_elapsed_time(self.auto_prof['kernel_ev1'],
self.auto_prof['kernel_ev2'])
self.auto_prof['kernel_timings'].append(time_ms)
else:
raise ValueError("CUDA memory management type may either \
be \'manual\' or \'auto\'.")
return labels
## data column major
# GPU data
data_t = data.T
dData = cuda.to_device(data_t)
dCentroids = cuda.to_device(centroids)
dLabels = cuda.device_array(n, dtype=np.int32)
dDists = cuda.device_array(n, dtype=np.float32)
# kernel
kt_start.record()
_cu_label_kernel_dists[bpg, tpb](dData, dCentroids, dLabels, dDists)
kt_end.record()
kt_end.synchronize()
# time
time_ms = cuda.event_elapsed_time(kt_start, kt_end)
print 'Kernel time (data column major):{} ms'.format(time_ms)
## data row major
# GPU data
dData = cuda.to_device(data)
dCentroids = cuda.to_device(centroids)
dLabels = cuda.device_array(n, dtype=np.int32)
dDists = cuda.device_array(n, dtype=np.float32)
# kernel
kt_start.record()
_cu_label_kernel_dists[bpg, tpb](dData, dCentroids, dLabels, dDists)
kt_end.record()
kt_end.synchronize()
def elapsed_time(self):
return self._time_off + 1.e-3*cuda.event_elapsed_time(self._t_start,
self._t_end)
def generate_batch(self,
end=None,
verbose=False,
fused=False,
nested_cva_at=None,
nested_im_at=None,
indicator_in_cva=False,
alpha=None,
im_window=None):
if end is None:
end = self.num_coarse_steps
t = 0.
self._reset()
self.cuda_generate_exp1(self.d_exp_1, self.d_rng_states)
self.stream.synchronize()
self.cuda_compute_mtm(
0, t, self.d_X, self.d_mtm_by_cpty, self.d_cash_flows_by_cpty,
self.d_vanillas_on_fx_f32, self.d_vanillas_on_fx_i32,
self.d_vanillas_on_fx_b8, self.d_irs_f32, self.d_irs_i32,
self.d_zcs_f32, self.d_zcs_i32, self.dt, self.max_coarse_per_reset,
self.cDtoH_freq, True)
self.stream.synchronize()
self.d_mtm_by_cpty[0].copy_to_host(ary=self.mtm_by_cpty[0],
stream=self.stream)
self.d_cash_flows_by_cpty[0].copy_to_host(
ary=self.cash_flows_by_cpty[0], stream=self.stream)
_cuda_bulk_diffuse_event_begin = [cuda.event() for i in range(end)]
_cuda_bulk_diffuse_event_end = [cuda.event() for i in range(end)]
_cuda_compute_mtm_event_begin = [cuda.event() for i in range(end)]
_cuda_compute_mtm_event_end = [cuda.event() for i in range(end)]
_cuda_nested_cva_event_begin = [cuda.event() for i in range(end)]
_cuda_nested_cva_event_end = [cuda.event() for i in range(end)]
_cuda_nested_im_event_begin = [cuda.event() for i in range(end)]
_cuda_nested_im_event_end = [cuda.event() for i in range(end)]
for coarse_idx in range(1, end + 1):
t += self.dT
idx_in_dev_arr = (coarse_idx - 1) % self.cDtoH_freq + 1
if not fused:
_cuda_bulk_diffuse_event_begin[coarse_idx -
1].record(stream=self.stream)
self.cuda_bulk_diffuse(
idx_in_dev_arr, t, self.d_X, self.d_def_indicators,
self.d_dom_rate_integral, self.d_spread_integrals,
self.d_irs_f32, self.d_irs_i32, self.d_exp_1,
self.d_rng_states, self.dt, self.max_coarse_per_reset)
_cuda_bulk_diffuse_event_end[coarse_idx -
1].record(stream=self.stream)
_cuda_compute_mtm_event_begin[coarse_idx -
1].record(stream=self.stream)
self.cuda_compute_mtm(
idx_in_dev_arr, t, self.d_X, self.d_mtm_by_cpty,
self.d_cash_flows_by_cpty, self.d_vanillas_on_fx_f32,
self.d_vanillas_on_fx_i32, self.d_vanillas_on_fx_b8,
self.d_irs_f32, self.d_irs_i32, self.d_zcs_f32,
self.d_zcs_i32, self.dt, self.max_coarse_per_reset,
self.cDtoH_freq, False)
_cuda_compute_mtm_event_end[coarse_idx -
1].record(stream=self.stream)
else:
_cuda_bulk_diffuse_event_begin[coarse_idx -
1].record(stream=self.stream)
if idx_in_dev_arr == 1:
self.cuda_diffuse_and_price(
1, self.cDtoH_freq, t, self.d_X,
self.d_dom_rate_integral, self.d_spread_integrals,
self.d_mtm_by_cpty, self.d_cash_flows_by_cpty,
self.d_irs_f32, self.d_irs_i32,
self.d_vanillas_on_fx_f32, self.d_vanillas_on_fx_i32,
self.d_vanillas_on_fx_b8, self.d_rng_states, self.dt,
self.max_coarse_per_reset, self.cDtoH_freq)
self.cuda_oversimulate_defs(1, self.cDtoH_freq,
self.d_def_indicators,
self.d_spread_integrals,
self.d_exp_1)
_cuda_bulk_diffuse_event_end[coarse_idx -
1].record(stream=self.stream)
if nested_cva_at is not None:
_cuda_nested_cva_event_begin[coarse_idx -
1].record(stream=self.stream)
if coarse_idx in nested_cva_at:
self.cuda_nested_cva(
idx_in_dev_arr, self.num_coarse_steps - coarse_idx, t,
self.d_X, self.d_def_indicators,
self.d_dom_rate_integral, self.d_spread_integrals,
self.d_mtm_by_cpty, self.d_cash_flows_by_cpty,
self.d_irs_f32, self.d_irs_i32,
self.d_vanillas_on_fx_f32, self.d_vanillas_on_fx_i32,
self.d_vanillas_on_fx_b8, self.d_exp_1,
self.d_rng_states, self.dt, self.cDtoH_freq,
indicator_in_cva, self.d_nested_cva,
self.d_nested_cva_sq)
self.d_nested_cva.copy_to_host(
ary=self.nested_cva[coarse_idx], stream=self.stream)
self.d_nested_cva_sq.copy_to_host(
ary=self.nested_cva_sq[coarse_idx], stream=self.stream)
_cuda_nested_cva_event_end[coarse_idx -
1].record(stream=self.stream)
if nested_im_at is not None:
_cuda_nested_im_event_begin[coarse_idx -
1].record(stream=self.stream)
if coarse_idx in nested_im_at:
for adam_iter in range(self.num_adam_iters):
adam_init = adam_iter == 0
step_size = self.lam * (adam_iter + 1)**(-self.gamma)
self.cuda_nested_im(
alpha, adam_init, step_size, idx_in_dev_arr,
im_window, t, self.d_X,
self.d_mtm_by_cpty[idx_in_dev_arr], self.d_irs_f32,
self.d_irs_i32, self.d_vanillas_on_fx_f32,
self.d_vanillas_on_fx_i32,
self.d_vanillas_on_fx_b8, self.d_rng_states,
self.dt, self.d_nested_im_by_cpty,
self.d_nested_im_std_by_cpty, self.d_nested_im_m,
self.d_nested_im_v, self.adam_b1, self.adam_b2,
adam_iter)
self.d_nested_im_by_cpty.copy_to_host(
ary=self.nested_im_by_cpty[coarse_idx],
stream=self.stream)
_cuda_nested_im_event_end[coarse_idx -
1].record(stream=self.stream)
if coarse_idx % self.cDtoH_freq == 0:
self.d_X[self.max_coarse_per_reset:].copy_to_host(
ary=self.X[coarse_idx - self.cDtoH_freq + 1:coarse_idx +
1],
stream=self.stream)
self.d_spread_integrals[1:].copy_to_host(
ary=self.spread_integrals[coarse_idx - self.cDtoH_freq +
1:coarse_idx + 1],
stream=self.stream)
self.d_dom_rate_integral[1:].copy_to_host(
ary=self.dom_rate_integral[coarse_idx - self.cDtoH_freq +
1:coarse_idx + 1],
stream=self.stream)
self.d_def_indicators[1:].copy_to_host(
ary=self.def_indicators[coarse_idx - self.cDtoH_freq +
1:coarse_idx + 1],
stream=self.stream)
self.d_mtm_by_cpty[1:].copy_to_host(
ary=self.mtm_by_cpty[coarse_idx - self.cDtoH_freq +
1:coarse_idx + 1],
stream=self.stream)
self.d_cash_flows_by_cpty[1:].copy_to_host(
ary=self.cash_flows_by_cpty[coarse_idx - self.cDtoH_freq +
1:coarse_idx + 1],
stream=self.stream)
self.d_X[:self.max_coarse_per_reset].copy_to_device(
self.d_X[-self.max_coarse_per_reset:], stream=self.stream)
self.d_spread_integrals[0].copy_to_device(
self.d_spread_integrals[self.cDtoH_freq],
stream=self.stream)
self.d_dom_rate_integral[0].copy_to_device(
self.d_dom_rate_integral[self.cDtoH_freq],
stream=self.stream)
self.d_def_indicators[0].copy_to_device(
self.d_def_indicators[self.cDtoH_freq], stream=self.stream)
if end % self.cDtoH_freq != 0:
start_idx = (end // self.cDtoH_freq) * self.cDtoH_freq + 1
length = end % self.cDtoH_freq
self.d_X[self.max_coarse_per_reset:self.max_coarse_per_reset +
length].copy_to_host(ary=self.X[start_idx:start_idx +
length],
stream=self.stream)
self.d_spread_integrals[1:length + 1].copy_to_host(
ary=self.spread_integrals[start_idx:start_idx + length],
stream=self.stream)
self.d_dom_rate_integral[1:length + 1].copy_to_host(
ary=self.dom_rate_integral[start_idx:start_idx + length],
stream=self.stream)
self.d_def_indicators[1:length + 1].copy_to_host(
ary=self.def_indicators[start_idx:start_idx + length],
stream=self.stream)
self.d_mtm_by_cpty[1:length + 1].copy_to_host(
ary=self.mtm_by_cpty[start_idx:start_idx + length],
stream=self.stream)
self.d_cash_flows_by_cpty[1:length + 1].copy_to_host(
ary=self.cash_flows_by_cpty[start_idx:start_idx + length],
stream=self.stream)
if verbose:
print('Everything was successfully queued!')
for evt_cuda_bulk_diffuse_event, evt_cuda_compute_mtm_event, evt_cuda_nested_cva_event, evt_cuda_nested_im_event in zip(
_cuda_bulk_diffuse_event_end, _cuda_compute_mtm_event_end,
_cuda_nested_cva_event_end, _cuda_nested_im_event_end):
evt_cuda_bulk_diffuse_event.synchronize()
evt_cuda_compute_mtm_event.synchronize()
evt_cuda_nested_cva_event.synchronize()
evt_cuda_nested_im_event.synchronize()
self.stream.synchronize()
if not fused:
print('cuda_bulk_diffuse average elapsed time per launch: {0} ms'.
format(
round(
sum(
cuda.event_elapsed_time(evt_begin, evt_end)
for evt_begin, evt_end in zip(
_cuda_bulk_diffuse_event_begin,
_cuda_bulk_diffuse_event_end)) / end, 3)))
print('compute_mtm average elapsed time per launch: {0} ms'.format(
round(
sum(
cuda.event_elapsed_time(evt_begin, evt_end)
for evt_begin, evt_end in zip(
_cuda_compute_mtm_event_begin,
_cuda_compute_mtm_event_end)) / end, 3)))
else:
print('cuda_diffuse_and_price elapsed time: {0} ms'.format(
round(
sum(
cuda.event_elapsed_time(evt_begin, evt_end)
for evt_begin, evt_end in zip(
_cuda_bulk_diffuse_event_begin,
_cuda_bulk_diffuse_event_end)), 3)))
if nested_cva_at is not None:
print('cuda_nested_cva average elapsed time per launch: {0} ms'.
format(
round(
sum(
cuda.event_elapsed_time(evt_begin, evt_end)
for evt_begin, evt_end in zip(
_cuda_nested_cva_event_begin,
_cuda_nested_cva_event_end)) /
len(nested_cva_at), 3)))
if nested_im_at is not None:
print('cuda_nested_im average elapsed time per launch: {0} ms'.
format(
round(
sum(
cuda.event_elapsed_time(evt_begin, evt_end)
for evt_begin, evt_end in zip(
_cuda_nested_im_event_begin,
_cuda_nested_im_event_end)) /
len(nested_im_at), 3)))
# TODO: port this to CUDA
self.cash_pos_by_cpty = ne.evaluate('c*exp(-r)',
local_dict={
'c':
self.cash_flows_by_cpty,
'r':
self.dom_rate_integral[:,
None, :]
})
np.cumsum(self.cash_pos_by_cpty, axis=0, out=self.cash_pos_by_cpty)
self.cash_pos_by_cpty *= np.exp(self.dom_rate_integral[:, None, :])
