def __blas(name, a, b, alpha=1.0, c=None, beta=0.0, shape_matters=True):
if not b is None:
if not (a.ndim == 2 and b.ndim == 2):
stderr.write("[ext] Matrices need to be two-dimensional.\n")
return None
if a.shape[1] != b.shape[0] and shape_matters:
stderr.write("[ext] Wrong shape of matrices: first argument has shape {} and second has shape {}.\n".format(a.shape, b.shape))
return None
if not b.flags['C_CONTIGUOUS']:
b = b.copy()
else:
b = np.empty(shape=(a.shape[0], a.shape[1]), dtype=a.dtype)
if not a.flags['C_CONTIGUOUS']:
a = a.copy()
if c is None:
c = np.empty(shape=(a.shape[0], b.shape[1]), dtype=a.dtype)
elif not c.flags['C_CONTIGUOUS']:
c = c.copy()
if alpha != 1.0:
a = a * alpha
if beta != 0.0:
c = c * beta
ufuncs.extmethod(name, c, a, b) # modifies 'c'
return c
def __blas(name, a, b, alpha=1.0, c=None, beta=0.0, shape_matters=True):
if not b is None:
if not (a.ndim == 2 and b.ndim == 2):
stderr.write("[ext] Matrices need to be two-dimensional.\n")
return None
if a.shape[1] != b.shape[0] and shape_matters:
stderr.write(
"[ext] Wrong shape of matrices: first argument has shape {} and second has shape {}.\n".format(a.shape,
b.shape))
return None
if not b.flags['C_CONTIGUOUS']:
b = b.copy()
else:
b = np.empty(shape=(a.shape[0], a.shape[1]), dtype=a.dtype)
if not a.flags['C_CONTIGUOUS']:
a = a.copy()
if c is None:
c = np.empty(shape=(a.shape[0], b.shape[1]), dtype=a.dtype)
elif not c.flags['C_CONTIGUOUS']:
c = c.copy()
if alpha != 1.0:
a = a * alpha
if beta != 0.0:
c = c * beta
ufuncs.extmethod(name, c, a, b) # modifies 'c'
return c
def hsv2rgbArray(hsv):
"""
Transform an hsv image to an rgb array
:param hsv: the input image. can be pil image or numpy array
:return rgb: the output array
This comes from scikit-image:
https://github.com/scikit-image/scikit-image/blob/master/skimage/color/colorconv.py
"""
hsv = numpyArray(hsv)
if len(hsv.shape) == 2:
# this is a black and white image, so treat it as value, with zero hue and saturation
hs = np.empty((hsv.shape))
return np.dstack((hs, hs, hsv))
hi = np.floor(hsv[:, :, 0] * 6)
f = hsv[:, :, 0] * 6 - hi
p = hsv[:, :, 2] * (1 - hsv[:, :, 1])
q = hsv[:, :, 2] * (1 - f * hsv[:, :, 1])
t = hsv[:, :, 2] * (1 - (1 - f) * hsv[:, :, 1])
v = hsv[:, :, 2]
hi = np.dstack([hi, hi, hi]).astype(np.uint8) % 6
out = np.choose(hi, [
np.dstack((v, t, p)),
np.dstack((q, v, p)),
np.dstack((p, v, t)),
np.dstack((p, q, v)),
np.dstack((t, p, v)),
np.dstack((v, p, q))
])
return out
def tdma(a, b, c, d, workgrp_size=None):
import pyopencl as cl
import bohrium as bh
assert a.shape == b.shape == c.shape == d.shape
assert a.dtype == b.dtype == c.dtype == d.dtype
# Check that PyOpenCL is installed and that the Bohrium runtime uses the OpenCL backend
if not bh.interop_pyopencl.available():
raise NotImplementedError("OpenCL not available")
# Get the OpenCL context from Bohrium
ctx = bh.interop_pyopencl.get_context()
queue = cl.CommandQueue(ctx)
ret = bh.empty(a.shape, dtype=a.dtype)
a_buf, b_buf, c_buf, d_buf, ret_buf = map(bh.interop_pyopencl.get_buffer,
(a, b, c, d, ret))
prg = compile_tdma(ret.shape[-1],
bh.interop_pyopencl.type_np2opencl_str(a.dtype))
global_size = functools.reduce(operator.mul, ret.shape[:-1])
prg.tdma(queue, [global_size], workgrp_size, a_buf, b_buf, c_buf, d_buf,
ret_buf)
return ret
def init(self):
"""
This function is used as a means to control til --dtype argument
passed to the benchmark script and provide a uuid for benchmark output.
"""
self.uuid = str(uuid.uuid4())
for dtype in self.dtypes:
yield ({0:bh.empty(self.size, bohrium=False, dtype=dtype)},
"%s: " % str(dtype)
)
def norm(x, ord=None, axis=None):
"""
This version of norm is not fully compliant with the NumPy version,
it only supports computing 2-norm of a vector.
"""
if ord != None:
raise NotImplementedError("Unsupported value param ord=%s" % ord)
if axis != None:
raise NotImplementedError("Unsupported value of param ord=%s" % axis)
r = np.sum(x*x)
if issubclass(np.dtype(r.dtype).type, np.integer):
r_f32 = np.empty(r.shape, dtype=np.float32)
r_f32[:] = r
r = r_f32
return np.sqrt(r)
def norm(x, ord=None, axis=None):
"""
This version of norm is not fully compliant with the NumPy version,
it only supports computing 2-norm of a vector.
"""
if ord != None:
raise NotImplementedError("Unsupported value param ord=%s" % ord)
if axis != None:
raise NotImplementedError("Unsupported value of param ord=%s" % axis)
r = np.sum(x * x)
if issubclass(np.dtype(r.dtype).type, np.integer):
r_f32 = np.empty(r.shape, dtype=np.float32)
r_f32[:] = r
r = r_f32
return np.sqrt(r)
def matmul(a, b):
"""
Matrix multiplication of two 2-D arrays.
Parameters
----------
a : array_like
First argument.
b : array_like
Second argument.
Returns
-------
output : ndarray
Returns the matrix multiplication of `a` and `b`.
Raises
------
ValueError
If the last dimension of `a` is not the same size as
the second-to-last dimension of `b`.
See Also
--------
dot : Dot product of two arrays.
Examples
--------
>>> np.matmul(np.array([[1,2],[3,4]]),np.array([[5,6],[7,8]]))
array([[19, 22],
[43, 50]])
"""
if not dtype_equal(a, b):
raise ValueError("Input must be of same type")
if a.ndim != 2 and b.ndim != 2:
raise ValueError("Input must be 2-D.")
if not (bhary.check(a) or bhary.check(b)):
return numpy.dot(a, b)
a = array_create.array(a)
b = array_create.array(b)
c = np.empty((a.shape[0], b.shape[1]), dtype=a.dtype)
target.matmul(ufunc.get_bhc(c), ufunc.get_bhc(a), ufunc.get_bhc(b))
return c
def matmul(a,b):
"""
Matrix multiplication of two 2-D arrays.
Parameters
----------
a : array_like
First argument.
b : array_like
Second argument.
Returns
-------
output : ndarray
Returns the matrix multiplication of `a` and `b`.
Raises
------
ValueError
If the last dimension of `a` is not the same size as
the second-to-last dimension of `b`.
See Also
--------
dot : Dot product of two arrays.
Examples
--------
>>> np.matmul(np.array([[1,2],[3,4]]),np.array([[5,6],[7,8]]))
array([[19, 22],
[43, 50]])
"""
if not dtype_equal(a,b):
raise ValueError("Input must be of same type")
if a.ndim != 2 and b.ndim != 2:
raise ValueError("Input must be 2-D.")
if not(bhary.check(a) or bhary.check(b)):
return numpy.dot(a, b)
a = array_create.array(a)
b = array_create.array(b)
c = np.empty((a.shape[0], b.shape[1]), dtype=a.dtype)
target.matmul(ufunc.get_bhc(c), ufunc.get_bhc(a), ufunc.get_bhc(b))
return c
def init(self):
"""
This function is used as a means to control til --dtype argument
passed to the benchmark script and provide a uuid for benchmark output.
"""
# Lets make sure that benchpress is installed
try:
import benchpress
except ImportError:
print("ERROR: benchpress is not installed -- skipping test.")
raise StopIteration()
self.uuid = str(uuid.uuid4())
for dtype in self.dtypes:
yield ({
0: bh.empty(self.size, bohrium=False, dtype=dtype)
}, "%s: " % str(dtype))
def init(self):
"""
This function is used as a means to control til --dtype argument
passed to the benchmark script and provide a uuid for benchmark output.
"""
#Lets make sure that benchpress is installed
try:
p = subprocess.Popen(
['bp-info', '--benchmarks'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
out, err = p.communicate()
rc = p.returncode
except OSError:
print("ERROR: benchpress is not installed -- skipping test.")
raise StopIteration()
self.uuid = str(uuid.uuid4())
for dtype in self.dtypes:
yield ({0:bh.empty(self.size, bohrium=False, dtype=dtype)},
"%s: " % str(dtype)
)
import bohrium as np from math import pi import matplotlib.pyplot as plt import matplotlib.colors as colors fig = plt.figure() plt.xticks([]) plt.yticks([]) k = 5 # number of plane waves stripes = 37 # number of stripes per wave N = 512 # image size in pixels ite = 30 # iterations phases = np.arange(0, 2 * pi, 2 * pi / ite) image = np.empty((N, N)) d = np.arange(-N / 2, N / 2, dtype=np.float64) xv, yv = np.meshgrid(d, d) theta = np.arctan2(yv, xv) r = np.log(np.sqrt(xv * xv + yv * yv)) r[np.isinf(r) == True] = 0 tcos = theta * np.cos(np.arange(0, pi, pi / k))[:, np.newaxis, np.newaxis] rsin = r * np.sin(np.arange(0, pi, pi / k))[:, np.newaxis, np.newaxis] inner = (tcos - rsin) * stripes cinner = np.cos(inner) sinner = np.sin(inner)
def main():
B = util.Benchmark()
nx = B.size[0]
ny = B.size[1]
nz = B.size[2]
ITER = B.size[3]
NO_OBST = 1
omega = 1.0
density = 1.0
deltaU = 1e-7
t1 = 1/3.0
t2 = 1/18.0
t3 = 1/36.0
B.start()
F = np.ones((19, nx, ny, nz), dtype=np.float64)
F[:] = density/19.0
FEQ = np.ones((19, nx, ny, nz), dtype=np.float64)
FEQ[:] = density/19.0
T = np.zeros((19, nx, ny, nz), dtype=np.float64)
#Create the scenery.
BOUND = np.zeros((nx, ny, nz), dtype=np.float64)
BOUNDi = np.ones((nx, ny, nz), dtype=np.float64)
"""
if not NO_OBST:
for i in xrange(nx):
for j in xrange(ny):
for k in xrange(nz):
if ((i-4)**2+(j-5)**2+(k-6)**2) < 6:
BOUND[i,j,k] += 1.0
BOUNDi[i,j,k] += 0.0
BOUND[:,0,:] += 1.0
BOUNDi[:,0,:] *= 0.0
"""
if util.Benchmark().bohrium:
np.flush()
for ts in xrange(0, ITER):
##Propagate / Streaming step
T[:] = F
#nearest-neighbours
F[1,:,:,0] = T[1,:,:,-1]
F[1,:,:,1:] = T[1,:,:,:-1]
F[2,:,:,:-1] = T[2,:,:,1:]
F[2,:,:,-1] = T[2,:,:,0]
F[3,:,0,:] = T[3,:,-1,:]
F[3,:,1:,:] = T[3,:,:-1,:]
F[4,:,:-1,:] = T[4,:,1:,:]
F[4,:,-1,:] = T[4,:,0,:]
F[5,0,:,:] = T[5,-1,:,:]
F[5,1:,:,:] = T[5,:-1,:,:]
F[6,:-1,:,:] = T[6,1:,:,:]
F[6,-1,:,:] = T[6,0,:,:]
#next-nearest neighbours
F[7,0 ,0 ,:] = T[7,-1 , -1,:]
F[7,0 ,1:,:] = T[7,-1 ,:-1,:]
F[7,1:,0 ,:] = T[7,:-1, -1,:]
F[7,1:,1:,:] = T[7,:-1,:-1,:]
F[8,0 ,:-1,:] = T[8,-1 ,1:,:]
F[8,0 , -1,:] = T[8,-1 ,0 ,:]
F[8,1:,:-1,:] = T[8,:-1,1:,:]
F[8,1:, -1,:] = T[8,:-1,0 ,:]
F[9,:-1,0 ,:] = T[9,1:, -1,:]
F[9,:-1,1:,:] = T[9,1:,:-1,:]
F[9,-1 ,0 ,:] = T[9,0 , 0,:]
F[9,-1 ,1:,:] = T[9,0 ,:-1,:]
F[10,:-1,:-1,:] = T[10,1:,1:,:]
F[10,:-1, -1,:] = T[10,1:,0 ,:]
F[10,-1 ,:-1,:] = T[10,0 ,1:,:]
F[10,-1 , -1,:] = T[10,0 ,0 ,:]
F[11,0 ,:,0 ] = T[11,0 ,:, -1]
F[11,0 ,:,1:] = T[11,0 ,:,:-1]
F[11,1:,:,0 ] = T[11,:-1,:, -1]
F[11,1:,:,1:] = T[11,:-1,:,:-1]
F[12,0 ,:,:-1] = T[12, -1,:,1:]
F[12,0 ,:, -1] = T[12, -1,:,0 ]
F[12,1:,:,:-1] = T[12,:-1,:,1:]
F[12,1:,:, -1] = T[12,:-1,:,0 ]
F[13,:-1,:,0 ] = T[13,1:,:, -1]
F[13,:-1,:,1:] = T[13,1:,:,:-1]
F[13, -1,:,0 ] = T[13,0 ,:, -1]
F[13, -1,:,1:] = T[13,0 ,:,:-1]
F[14,:-1,:,:-1] = T[14,1:,:,1:]
F[14,:-1,:, -1] = T[14,1:,:,0 ]
F[14,-1 ,:,:-1] = T[14,0 ,:,1:]
F[14,-1 ,:, -1] = T[14,0 ,:,0 ]
F[15,:,0 ,0 ] = T[15,:, -1, -1]
F[15,:,0 ,1:] = T[15,:, -1,:-1]
F[15,:,1:,0 ] = T[15,:,:-1, -1]
F[15,:,1:,1:] = T[15,:,:-1,:-1]
F[16,:,0 ,:-1] = T[16,:, -1,1:]
F[16,:,0 , -1] = T[16,:, -1,0 ]
F[16,:,1:,:-1] = T[16,:,:-1,1:]
F[16,:,1:, -1] = T[16,:,:-1,0 ]
F[17,:,:-1,0 ] = T[17,:,1:, -1]
F[17,:,:-1,1:] = T[17,:,1:,:-1]
F[17,:, -1,0 ] = T[17,:,0 , -1]
F[17,:, -1,1:] = T[17,:,0 ,:-1]
F[18,:,:-1,:-1] = T[18,:,1:,1:]
F[18,:,:-1, -1] = T[18,:,1:,0 ]
F[18,:,-1 ,:-1] = T[18,:,0 ,1:]
F[18,:,-1 , -1] = T[18,:,0 ,0 ]
#Densities bouncing back at next timestep
BB = np.empty(F.shape)
T[:] = F
T[1:,:,:,:] *= BOUND[np.newaxis,:,:,:]
BB[2 ,:,:,:] += T[1 ,:,:,:]
BB[1 ,:,:,:] += T[2 ,:,:,:]
BB[4 ,:,:,:] += T[3 ,:,:,:]
BB[3 ,:,:,:] += T[4 ,:,:,:]
BB[6 ,:,:,:] += T[5 ,:,:,:]
BB[5 ,:,:,:] += T[6 ,:,:,:]
BB[10,:,:,:] += T[7 ,:,:,:]
BB[9 ,:,:,:] += T[8 ,:,:,:]
BB[8 ,:,:,:] += T[9 ,:,:,:]
BB[7 ,:,:,:] += T[10,:,:,:]
BB[14,:,:,:] += T[11,:,:,:]
BB[13,:,:,:] += T[12,:,:,:]
BB[12,:,:,:] += T[13,:,:,:]
BB[11,:,:,:] += T[14,:,:,:]
BB[18,:,:,:] += T[15,:,:,:]
BB[17,:,:,:] += T[16,:,:,:]
BB[16,:,:,:] += T[17,:,:,:]
BB[15,:,:,:] += T[18,:,:,:]
# Relax calculate equilibrium state (FEQ) with equivalent speed and density to F
DENSITY = np.add.reduce(F)
#UX = F[5,:,:,:].copy()
UX = np.ones(F[5,:,:,:].shape, dtype=np.float64)
UX[:,:,:] = F[5,:,:,:]
UX += F[7,:,:,:]
UX += F[8,:,:,:]
UX += F[11,:,:,:]
UX += F[12,:,:,:]
UX -= F[6,:,:,:]
UX -= F[9,:,:,:]
UX -= F[10,:,:,:]
UX -= F[13,:,:,:]
UX -= F[14,:,:,:]
UX /=DENSITY
#UY = F[3,:,:,:].copy()
UY = np.ones(F[3,:,:,:].shape, dtype=np.float64)
UY[:,:,:] = F[3,:,:,:]
UY += F[7,:,:,:]
UY += F[9,:,:,:]
UY += F[15,:,:,:]
UY += F[16,:,:,:]
UY -= F[4,:,:,:]
UY -= F[8,:,:,:]
UY -= F[10,:,:,:]
UY -= F[17,:,:,:]
UY -= F[18,:,:,:]
UY /=DENSITY
#UZ = F[1,:,:,:].copy()
UZ = np.ones(F[1,:,:,:].shape, dtype=np.float64)
UZ[:,:,:] = F[1,:,:,:]
UZ += F[11,:,:,:]
UZ += F[13,:,:,:]
UZ += F[15,:,:,:]
UZ += F[17,:,:,:]
UZ -= F[2,:,:,:]
UZ -= F[12,:,:,:]
UZ -= F[14,:,:,:]
UZ -= F[16,:,:,:]
UZ -= F[18,:,:,:]
UZ /=DENSITY
UX[0,:,:] += deltaU #Increase inlet pressure
#Set bourderies to zero.
UX[:,:,:] *= BOUNDi
UY[:,:,:] *= BOUNDi
UZ[:,:,:] *= BOUNDi
DENSITY[:,:,:] *= BOUNDi
U_SQU = UX**2 + UY**2 + UZ**2
# Calculate equilibrium distribution: stationary
FEQ[0,:,:,:] = (t1*DENSITY)*(1.0-3.0*U_SQU/2.0)
# nearest-neighbours
T1 = 3.0/2.0*U_SQU
tDENSITY = t2*DENSITY
FEQ[1,:,:,:]=tDENSITY*(1.0 + 3.0*UZ + 9.0/2.0*UZ**2 - T1)
FEQ[2,:,:,:]=tDENSITY*(1.0 - 3.0*UZ + 9.0/2.0*UZ**2 - T1)
FEQ[3,:,:,:]=tDENSITY*(1.0 + 3.0*UY + 9.0/2.0*UY**2 - T1)
FEQ[4,:,:,:]=tDENSITY*(1.0 - 3.0*UY + 9.0/2.0*UY**2 - T1)
FEQ[5,:,:,:]=tDENSITY*(1.0 + 3.0*UX + 9.0/2.0*UX**2 - T1)
FEQ[6,:,:,:]=tDENSITY*(1.0 - 3.0*UX + 9.0/2.0*UX**2 - T1)
# next-nearest neighbours
T1 = 3.0*U_SQU/2.0
tDENSITY = t3*DENSITY
U8 = UX+UY
FEQ[7,:,:,:] =tDENSITY*(1.0 + 3.0*U8 + 9.0/2.0*(U8)**2 - T1)
U9 = UX-UY
FEQ[8,:,:,:] =tDENSITY*(1.0 + 3.0*U9 + 9.0/2.0*(U9)**2 - T1)
U10 = -UX+UY
FEQ[9,:,:,:] =tDENSITY*(1.0 + 3.0*U10 + 9.0/2.0*(U10)**2 - T1)
U8 *= -1.0
FEQ[10,:,:,:]=tDENSITY*(1.0 + 3.0*U8 + 9.0/2.0*(U8)**2 - T1)
U12 = UX+UZ
FEQ[11,:,:,:]=tDENSITY*(1.0 + 3.0*U12 + 9.0/2.0*(U12)**2 - T1)
U12 *= 1.0
FEQ[14,:,:,:]=tDENSITY*(1.0 + 3.0*U12 + 9.0/2.0*(U12)**2 - T1)
U13 = UX-UZ
FEQ[12,:,:,:]=tDENSITY*(1.0 + 3.0*U13 + 9.0/2.0*(U13)**2 - T1)
U13 *= -1.0
FEQ[13,:,:,:]=tDENSITY*(1.0 + 3.0*U13 + 9.0/2.0*(U13)**2 - T1)
U16 = UY+UZ
FEQ[15,:,:,:]=tDENSITY*(1.0 + 3.0*U16 + 9.0/2.0*(U16)**2 - T1)
U17 = UY-UZ
FEQ[16,:,:,:]=tDENSITY*(1.0 + 3.0*U17 + 9.0/2.0*(U17)**2 - T1)
U17 *= -1.0
FEQ[17,:,:,:]=tDENSITY*(1.0 + 3.0*U17 + 9.0/2.0*(U17)**2 - T1)
U16 *= -1.0
FEQ[18,:,:,:]=tDENSITY*(1.0 + 3.0*U16 + 9.0/2.0*(U16)**2 - T1)
F *= (1.0-omega)
F += omega * FEQ
#Densities bouncing back at next timestep
F[1:,:,:,:] *= BOUNDi[np.newaxis,:,:,:]
F[1:,:,:,:] += BB[1:,:,:,:]
del BB
del T1
del UX, UY, UZ
del U_SQU
del DENSITY, tDENSITY
del U8, U9, U10, U12, U13, U16, U17
if util.Benchmark().bohrium:
np.flush()
B.stop()
B.pprint()
if B.outputfn:
B.tofile(B.outputfn, {'res': UX})
"""