def main():
from pycuda.tools import DeviceMemoryPool, PageLockedMemoryPool
dev_pool = DeviceMemoryPool()
pagelocked_pool = PageLockedMemoryPool()
from scipy.io import mmread
csr_mat = mmread(args[0]).tocsr().astype(numpy.float32)
inv_mat_diag = 1 / csr_mat.diagonal()
print "building..."
from pycuda.sparse.packeted import PacketedSpMV
spmv = PacketedSpMV(csr_mat, options.is_symmetric, csr_mat.dtype)
rhs = numpy.random.rand(spmv.shape[0]).astype(spmv.dtype)
from pycuda.sparse.operator import DiagonalPreconditioner
if True:
precon = DiagonalPreconditioner(
spmv.permute(
gpuarray.to_gpu(inv_mat_diag, allocator=dev_pool.allocate)))
else:
precon = None
from pycuda.sparse.cg import solve_pkt_with_cg
print "start solve"
for i in range(4):
start = drv.Event()
stop = drv.Event()
start.record()
rhs_gpu = gpuarray.to_gpu(rhs, dev_pool.allocate)
res_gpu, it_count, res_count = \
solve_pkt_with_cg(spmv, rhs_gpu, precon,
tol=1e-7 if spmv.dtype == numpy.float64 else 5e-5,
pagelocked_allocator=pagelocked_pool.allocate)
res = res_gpu.get()
stop.record()
stop.synchronize()
elapsed = stop.time_since(start) * 1e-3
est_flops = (csr_mat.nnz * 2 * (it_count + res_count) +
csr_mat.shape[0] * (2 + 2 + 2 + 2 + 2) * it_count)
if precon is not None:
est_flops += csr_mat.shape[0] * it_count
print "residual norm: %g" % (la.norm(csr_mat * res - rhs) /
la.norm(rhs))
print(
"size: %d, elapsed: %g s, %d it, %d residual, it/second: %g, "
"%g gflops/s" % (csr_mat.shape[0], elapsed, it_count, res_count,
it_count / elapsed, est_flops / elapsed / 1e9))
# TODO: mixed precision
# TODO: benchmark
pagelocked_pool.stop_holding()
dev_pool.stop_holding()
def _solve_cuda(lap_sparse, B, return_full_prob=False, maxiter=100, tol=5e-5):
"""
solves lap_sparse X_i = B_i for each phase i, using the conjugate
gradient method. For each pixel, the label i corresponding to the
maximal X_i is returned.
"""
print("using gpu mode")
dev_pool = DeviceMemoryPool()
pagelocked_pool = PageLockedMemoryPool()
csr_mat = lap_sparse
csr_mat = csr_mat.astype(np.float32)
inv_mat_diag = 1 / csr_mat.diagonal()
spmv = PacketedSpMV(csr_mat, True, csr_mat.dtype)
X = []
for i in range(len(B)):
rhs = -B[i].astype(spmv.dtype)
if True:
precon = DiagonalPreconditioner(
spmv.permute(
gpuarray.to_gpu(inv_mat_diag,
allocator=dev_pool.allocate)))
else:
precon = None
print("start solve")
start = drv.Event()
stop = drv.Event()
start.record()
rhs_gpu = gpuarray.to_gpu(rhs, dev_pool.allocate)
tol = 1e-7 if spmv.dtype == np.float64 else tol
res_gpu, it_count, res_count = solve_pkt_with_cg(
spmv,
rhs_gpu,
precon,
tol=tol,
pagelocked_allocator=pagelocked_pool.allocate)
res = res_gpu.get()
stop.record()
stop.synchronize()
elapsed = stop.time_since(start) * 1e-3
est_flops = (csr_mat.nnz * 2 * (it_count + res_count) +
csr_mat.shape[0] * (2 + 2 + 2 + 2 + 2) * it_count)
if precon is not None:
est_flops += csr_mat.shape[0] * it_count
print("size: %d, elapsed: %g s, %d it, %d residual, it/second: %g, "
"%g gflops/s" % (csr_mat.shape[0], elapsed, it_count, res_count,
it_count / elapsed, est_flops / elapsed / 1e9))
x0 = res[0]
X.append(x0)
pagelocked_pool.stop_holding()
dev_pool.stop_holding()
if not return_full_prob:
X = np.array(X)
X = np.argmax(X, axis=0)
return X
def test_mempool(self):
from pycuda.tools import bitlog2
from pycuda.tools import DeviceMemoryPool
pool = DeviceMemoryPool()
queue = []
free, total = drv.mem_get_info()
e0 = bitlog2(free)
for e in range(e0 - 6, e0 - 4):
for i in range(100):
queue.append(pool.allocate(1 << e))
if len(queue) > 10:
queue.pop(0)
del queue
pool.stop_holding()
def test_mempool(self):
from pycuda.tools import bitlog2
from pycuda.tools import DeviceMemoryPool
pool = DeviceMemoryPool()
maxlen = 10
queue = []
free, total = drv.mem_get_info()
e0 = bitlog2(free)
for e in range(e0-6, e0-4):
for i in range(100):
queue.append(pool.allocate(1<<e))
if len(queue) > 10:
queue.pop(0)
del queue
pool.stop_holding()
def test1d(wavelet='haar', use_float32=False, depth=1, num_rows=512, row_size=512,
iterations=20, gpu_input=False, gpu_output=False, gpu_mempool=False):
try:
dtype = numpy.float64
if use_float32:
dtype = numpy.float32
img = (numpy.array(scipy.misc.ascent(), dtype=dtype)-128.)/128.
resized_img = resize(img, (num_rows, row_size), mode='constant')
if gpu_input:
cont_input_array = numpy.ascontiguousarray(resized_img, dtype=dtype)
img_array_gpu = gpuarray.to_gpu(cont_input_array)
else:
img_array_gpu = resized_img
if gpu_mempool:
dev_mem_pool = DeviceMemoryPool()
gpu_alloc = dev_mem_pool.allocate
else:
gpu_alloc = cuda.mem_alloc
pwt = PycudaWaveletTransform(wavelet=wavelet, use_float32=use_float32)
# Forward Transform
print('---------FORWARD DWT---------')
t = time.time()
for _ in range(iterations):
dec_cpu = pywt.wavedec(resized_img, wavelet=wavelet, mode='periodization', level=depth)
t = time.time()-t
print('PyWavelets:\t\t\t\t{:.3f} ms'.format((t*1000.)/iterations))
t = time.time()
for _ in range(iterations):
dec_gpu = pwt.dwt1d(img_array_gpu, depth=depth, gpu_output=gpu_output, gpu_allocator=gpu_alloc)
t = time.time()-t
print('PycudaWaveletTransform:\t{:.3f} ms'.format((t*1000.)/iterations))
for i, (d1, d2) in enumerate(zip(dec_gpu, dec_cpu)):
if i == 0:
result1 = d1.get() if gpu_output else d1
result2 = d2
else:
result1 = numpy.concatenate((result1, d1.get() if gpu_output else d1), axis=1)
result2 = numpy.concatenate((result2, d2), axis=1)
print('RMSE: {} \n'.format(rmse(result1, result2)))
dec_cpu_g = []
if gpu_input:
for d in dec_cpu:
cont_array = numpy.ascontiguousarray(d, dtype=dtype)
dec_cpu_g.append(gpuarray.to_gpu(cont_array))
else:
dec_cpu_g = dec_cpu
# Inverse Transform
print('---------INVERSE DWT---------')
t = time.time()
for _ in range(iterations):
rec_cpu = pywt.waverec(dec_cpu, wavelet=wavelet, mode='periodization')
t = time.time()-t
print('PyWavelets:\t\t\t\t{:.3f} ms'.format((t*1000.)/iterations))
t = time.time()
for _ in range(iterations):
rec_gpu = pwt.idwt1d(dec_cpu_g, gpu_output=gpu_output, gpu_allocator=gpu_alloc)
t = time.time()-t
print('PycudaWaveletTransform:\t{:.3f} ms'.format((t*1000.)/iterations))
print('RMSE: {} '.format(rmse(rec_gpu.get() if gpu_output else rec_gpu, rec_cpu)))
if gpu_mempool:
dev_mem_pool.stop_holding()
except Exception as e:
tb = traceback.format_exc()
print("%s",tb)
def main_cg():
from optparse import OptionParser
parser = OptionParser(
usage="%prog [options] MATRIX-MARKET-FILE")
parser.add_option("-s", "--is-symmetric", action="store_true",
help="Specify that the input matrix is already symmetric")
options, args = parser.parse_args()
from pycuda.tools import DeviceMemoryPool, PageLockedMemoryPool
dev_pool = DeviceMemoryPool()
pagelocked_pool = PageLockedMemoryPool()
from scipy.io import mmread
csr_mat = mmread(args[0]).tocsr().astype(numpy.float32)
inv_mat_diag = 1/csr_mat.diagonal()
print "building..."
from pycuda.sparse.packeted import PacketedSpMV
spmv = PacketedSpMV(csr_mat, options.is_symmetric, csr_mat.dtype)
rhs = numpy.random.rand(spmv.shape[0]).astype(spmv.dtype)
from pycuda.sparse.operator import DiagonalPreconditioner
if True:
precon = DiagonalPreconditioner(
spmv.permute(gpuarray.to_gpu(
inv_mat_diag, allocator=dev_pool.allocate)))
else:
precon = None
from pycuda.sparse.cg import solve_pkt_with_cg
print "start solve"
for i in range(4):
start = drv.Event()
stop = drv.Event()
start.record()
rhs_gpu = gpuarray.to_gpu(rhs, dev_pool.allocate)
res_gpu, it_count, res_count = \
solve_pkt_with_cg(spmv, rhs_gpu, precon,
tol=1e-7 if spmv.dtype == numpy.float64 else 5e-5,
pagelocked_allocator=pagelocked_pool.allocate)
res = res_gpu.get()
stop.record()
stop.synchronize()
elapsed = stop.time_since(start)*1e-3
est_flops = (csr_mat.nnz*2*(it_count+res_count)
+ csr_mat.shape[0]*(2+2+2+2+2)*it_count)
if precon is not None:
est_flops += csr_mat.shape[0] * it_count
print "residual norm: %g" % (la.norm(csr_mat*res - rhs)/la.norm(rhs))
print ("size: %d, elapsed: %g s, %d it, %d residual, it/second: %g, "
"%g gflops/s" % (
csr_mat.shape[0],
elapsed, it_count, res_count, it_count/elapsed,
est_flops/elapsed/1e9))
# TODO: mixed precision
# TODO: benchmark
pagelocked_pool.stop_holding()
dev_pool.stop_holding()
def test2d(wavelet='haar',
use_float32=False,
depth=1,
num_slices=1,
row_size=512,
col_size=512,
iterations=20,
gpu_input=False,
gpu_output=False,
gpu_mempool=False):
try:
dtype = numpy.float64
if use_float32:
dtype = numpy.float32
# Prepare Image Array
img = (numpy.array(scipy.misc.ascent(), dtype=dtype) - 128.) / 128.
resized_img = resize(img, (col_size, row_size), mode='constant')
img_array = numpy.empty([num_slices, col_size, row_size], dtype=dtype)
for s in range(num_slices):
img_array[s, :, :] = resized_img[:, :]
if gpu_input:
cont_input_array = numpy.ascontiguousarray(img_array, dtype=dtype)
img_array_gpu = gpuarray.to_gpu(cont_input_array)
else:
img_array_gpu = img_array
if gpu_mempool:
dev_mem_pool = DeviceMemoryPool()
gpu_alloc = dev_mem_pool.allocate
else:
gpu_alloc = cuda.mem_alloc
pwt = PycudaWaveletTransform(wavelet=wavelet, use_float32=use_float32)
# Forward Transform
print('---------FORWARD 2D DWT---------')
t = time.time()
for _ in range(iterations):
dec_cpu = [
pywt.wavedec2(img_array[s],
wavelet=wavelet,
mode='periodization',
level=depth) for s in range(num_slices)
]
t = time.time() - t
print('PyWavelets:\t\t\t\t{:.3f} ms'.format((t * 1000.) / iterations))
t = time.time()
for _ in range(iterations):
dec_gpu = pwt.dwt2d(img_array_gpu,
depth=depth,
gpu_output=gpu_output,
gpu_allocator=gpu_alloc)
t = time.time() - t
print('PycudaWaveletTransform:\t{:.3f} ms'.format(
(t * 1000.) / iterations))
dec_cpu_g = []
for ig, vg in enumerate(dec_gpu):
if ig == 0:
a = numpy.empty_like(vg.get() if gpu_output else vg)
for ic, vc in enumerate(dec_cpu):
a[ic, :, :] = vc[0]
dec_cpu_g.append(a)
else:
dl = []
for id, vd in enumerate(vg):
d = numpy.empty_like(vd.get() if gpu_output else vd)
for ic, vc in enumerate(dec_cpu):
d[ic, :, :] = vc[ig][id]
dl.append(d)
dec_cpu_g.append(dl)
for i, (d1, d2) in enumerate(zip(dec_gpu, dec_cpu_g)):
if i == 0:
result1 = d1.get().flatten() if gpu_output else d1.flatten()
result2 = d2.flatten()
else:
for d in d1:
result1 = numpy.concatenate(
(result1,
d.get().flatten() if gpu_output else d.flatten()))
for d in d2:
result2 = numpy.concatenate((result2, d.flatten()))
print('RMSE: {} \n'.format(rmse(result1, result2)))
if gpu_input:
for ig, vg in enumerate(dec_cpu_g):
if ig == 0:
cont_array = numpy.ascontiguousarray(vg, dtype=dtype)
dec_cpu_g[ig] = gpuarray.to_gpu(cont_array)
else:
for id, vd in enumerate(vg):
cont_array = numpy.ascontiguousarray(vd, dtype=dtype)
dec_cpu_g[ig][id] = gpuarray.to_gpu(cont_array)
# Inverse Transform
print('---------INVERSE 2D DWT---------')
t = time.time()
for _ in range(iterations):
rec_cpu = [
pywt.waverec2(d, wavelet=wavelet, mode='periodization')
for d in dec_cpu
]
t = time.time() - t
print('PyWavelets:\t\t\t\t{:.3f} ms'.format((t * 1000.) / iterations))
t = time.time()
for _ in range(iterations):
rec_gpu = pwt.idwt2d(dec_cpu_g,
gpu_output=gpu_output,
gpu_allocator=gpu_alloc)
t = time.time() - t
print('PycudaWaveletTransform:\t{:.3f} ms'.format(
(t * 1000.) / iterations))
rec_cpu_g = numpy.empty_like(rec_gpu.get() if gpu_output else rec_gpu)
for ic, vc in enumerate(rec_cpu):
rec_cpu_g[ic, :, :] = vc
print('RMSE: {} '.format(
rmse(rec_gpu.get() if gpu_output else rec_gpu, rec_cpu_g)))
if gpu_mempool:
dev_mem_pool.stop_holding()
except Exception as e:
tb = traceback.format_exc()
print("%s", tb)
