def test_speed(rng):
try:
import pyopencl_blas
except ImportError:
pyopencl_blas = None
# enable_out_of_order = (
# cl.command_queue_properties.OUT_OF_ORDER_EXEC_MODE_ENABLE)
k = 300
# k = 100
# k = 32
# k = 16
ms = [rng.randint(100, 1000) for i in range(k)]
ns = [rng.randint(100, 1000) for i in range(k)]
# ms = [4096 for i in range(k)]
# ns = [4096 for i in range(k)]
aa = [rng.uniform(-1, 1, size=(m, n)).astype('float32')
for m, n in zip(ms, ns)]
xx = [rng.uniform(-1, 1, size=n).astype('float32') for n in ns]
yy = [rng.uniform(-1, 1, size=m).astype('float32') for m in ms]
ajs = [np.int32(i) for i in range(k)]
xjs = [np.int32(i) for i in range(k)]
# ajs = [rng.randint(k, size=p) for i in range(k)]
# xjs = [rng.randint(k, size=p) for i in range(k)]
# alpha = 0.5
# beta = 0.1
alpha = 1.0
beta = 1.0
# -- prepare initial conditions on device
queue = cl.CommandQueue(ctx)
# queue = cl.CommandQueue(ctx, properties=enable_out_of_order)
clA = CLRA.from_arrays(queue, aa)
clX = CLRA.from_arrays(queue, xx)
clY = CLRA.from_arrays(queue, yy)
A_js = RA(ajs, dtype=np.int32)
X_js = RA(xjs, dtype=np.int32)
# -- run cl computation
prog = plan_ragged_gather_gemv(
queue, alpha, clA, A_js, clX, X_js, beta, clY)
plans = prog.choose_plans()
print('')
print('-' * 5 + ' Plans ' + '-' * 45)
for plan in plans:
print(plan)
with Timer() as timer:
for plan in plans:
plan()
print("nengo_ocl: %0.3f" % timer.duration)
# -- speed test in ocl blas
if pyopencl_blas:
pyopencl_blas.setup()
def array(a):
cla = cl.array.Array(queue, a.shape, a.dtype)
cla.set(a)
return cla
clAs = [array(a) for a in aa]
clXs = [array(x.ravel()) for x in xx]
clYs = [array(y.ravel()) for y in yy]
queues = [cl.CommandQueue(ctx) for _ in range(k)]
# queues = [cl.CommandQueue(ctx, properties=enable_out_of_order)
# for _ in range(k)]
queue.finish()
with Timer() as timer:
if 0:
# use a single queue
for A, X, Y in zip(clAs, clXs, clYs):
pyopencl_blas.gemv(queue, A, X, Y)
queue.finish()
else:
# use multiple parallel queues
events = []
for i, [A, X, Y] in enumerate(zip(clAs, clXs, clYs)):
q = queues[i % len(queues)]
e = pyopencl_blas.gemv(q, A, X, Y)
events.append(e)
for q in queues:
q.flush()
cl.wait_for_events(events)
print("clBLAS: %0.3f" % timer.duration)
rng = np.random.RandomState(1) # change the seed to see different data
A[...] = rng.uniform(-1, 1, size=A.shape)
x[...] = rng.uniform(-1, 1, size=x.shape)
y[...] = rng.uniform(-1, 1, size=y.shape)
# allocate OpenCL memory on the device
clA = Array(queue, A.shape, A.dtype)
clx = Array(queue, x.shape, x.dtype)
cly = Array(queue, y.shape, y.dtype)
# copy data to device
clA.set(A)
clx.set(x)
# compute a matrix-vector product (gemv)
blas.gemv(queue, clA, clx, cly)
# check the result
print("Expected: ", np.dot(A, x))
print("Actual: ", cly.get())
# try a matrix-vector product with the transpose
cly.set(y)
blas.gemv(queue, clA, cly, clx, transA=True)
print("Expected: ", np.dot(A.T, y))
print("Actual: ", clx.get())
# tidy up the BLAS
blas.teardown()
def ATdotx(x):
clx = cl.array.to_device(queue, x.astype(np.float32))
cly = cl.array.Array(queue, (n, ), dtype=np.float32)
pyopencl_blas.gemv(queue, clA, clx, cly, transA=True)
return cly.get()
def matvec(x, y):
blas.gemv(queue, cla, x, y)
def matvect(x, y):
blas.gemv(queue, cla, x, y, transA=True)
return
def test_speed(ctx, rng):
try:
import pyopencl_blas
except ImportError:
pyopencl_blas = None
# enable_out_of_order = (
# cl.command_queue_properties.OUT_OF_ORDER_EXEC_MODE_ENABLE)
k = 300
# k = 100
# k = 32
# k = 16
ms = [rng.randint(100, 1000) for i in range(k)]
ns = [rng.randint(100, 1000) for i in range(k)]
# ms = [4096 for i in range(k)]
# ns = [4096 for i in range(k)]
aa = [
rng.uniform(-1, 1, size=(m, n)).astype('float32')
for m, n in zip(ms, ns)
]
xx = [rng.uniform(-1, 1, size=n).astype('float32') for n in ns]
yy = [rng.uniform(-1, 1, size=m).astype('float32') for m in ms]
ajs = [np.int32(i) for i in range(k)]
xjs = [np.int32(i) for i in range(k)]
# ajs = [rng.randint(k, size=p) for i in range(k)]
# xjs = [rng.randint(k, size=p) for i in range(k)]
# alpha = 0.5
# beta = 0.1
alpha = 1.0
beta = 1.0
# -- prepare initial conditions on device
queue = cl.CommandQueue(ctx)
# queue = cl.CommandQueue(ctx, properties=enable_out_of_order)
clA = CLRA.from_arrays(queue, aa)
clX = CLRA.from_arrays(queue, xx)
clY = CLRA.from_arrays(queue, yy)
A_js = RA(ajs, dtype=np.int32)
X_js = RA(xjs, dtype=np.int32)
# -- run cl computation
prog = plan_ragged_gather_gemv(queue, alpha, clA, A_js, clX, X_js, beta,
clY)
plans = prog.choose_plans()
print('')
print('-' * 5 + ' Plans ' + '-' * 45)
for plan in plans:
print(plan)
with Timer() as timer:
for plan in plans:
plan()
print("nengo_ocl: %0.3f" % timer.duration)
# -- speed test in ocl blas
if pyopencl_blas:
pyopencl_blas.setup()
def array(a):
cla = cl.array.Array(queue, a.shape, a.dtype)
cla.set(a)
return cla
clAs = [array(a) for a in aa]
clXs = [array(x.ravel()) for x in xx]
clYs = [array(y.ravel()) for y in yy]
queues = [cl.CommandQueue(ctx) for _ in range(k)]
# queues = [cl.CommandQueue(ctx, properties=enable_out_of_order)
# for _ in range(k)]
queue.finish()
with Timer() as timer:
if 0:
# use a single queue
for A, X, Y in zip(clAs, clXs, clYs):
pyopencl_blas.gemv(queue, A, X, Y)
queue.finish()
else:
# use multiple parallel queues
events = []
for i, [A, X, Y] in enumerate(zip(clAs, clXs, clYs)):
q = queues[i % len(queues)]
e = pyopencl_blas.gemv(q, A, X, Y)
events.append(e)
for q in queues:
q.flush()
cl.wait_for_events(events)
print("clBLAS: %0.3f" % timer.duration)
