def allocation(self):
super(DGModalGpu, self).allocation()
self.ul_gpu = cuda.to_device(self.ul)
self.ul_prev_gpu = cuda.to_device(self.ul)
self.ul_tmp_gpu = cuda.to_device(self.ul)
self.kl_gpu = cuda.to_device(self.ul)
self.el_sum_gpu = cuda.to_device(np.zeros(self.ne))
def test_compare_order():
'''
compare_order between C(row-major), F(column-major)
'''
compare_order = mod_cu.get_function('compare_order')
nx, ny = 3, 4
f_1d = np.arange(nx*ny, dtype='f8')
f_2d_C = f_1d.reshape((nx,ny), order='C')
f_2d_F = f_1d.reshape((nx,ny), order='F')
print ''
print 'f_1d_C\n\n', f_1d
print 'f_2d_C\n', f_2d_C
print 'f_2d_F\n', f_2d_F
print ''
print 'after cuda'
ret_f_1d = np.zeros_like(f_1d)
f_1d_gpu = cuda.mem_alloc_like(f_1d)
f_2d_C_gpu = cuda.to_device(f_2d_C)
compare_order(f_2d_C_gpu, f_1d_gpu, block=(nx*ny,1,1), grid=(1,1))
cuda.memcpy_dtoh(ret_f_1d, f_1d_gpu)
print 'f_1d from f_2d_C\n', ret_f_1d
f_2d_F_gpu = cuda.to_device(f_2d_F)
compare_order(f_2d_F_gpu, f_1d_gpu, block=(nx*ny,1,1), grid=(1,1))
cuda.memcpy_dtoh(ret_f_1d, f_1d_gpu)
print 'f_1d from f_2d_F\n', ret_f_1d
def train_gpu(self, num_iter, model_file_path):
if self.batch == 0:
# Prepare to send the numpy array to gpu
self.syn1_gpu = cuda.to_device(self.syn1)
# Create word idx and related data-structure.
self.base_word_rep = cuda.mem_alloc(len(self.dictionary)*WordRep.memsize)
word_rep_ptr = int(self.base_word_rep)
self.word_reps = {}
for w_idx, word in sorted(self.dictionary.items()):
word_code = 1-2*self.words_rep[word][0].astype(dtype=np.int32)
word_point = self.words_rep[word][1].astype(dtype=np.int32)
self.word_reps[w_idx] = WordRep(word_code, word_point, word_rep_ptr)
word_rep_ptr += WordRep.memsize
print "GPU transfers done."
self.sent_reps_gpu = cuda.to_device(self.sent_reps)
# Prepare sentences for GPU transfer.
idx_sentences = [[self.dictionary.token2id[word] for word in sentence if word in self.dictionary]
for sentence in self.sentences]
# Prepare the kernel function
kernel = self.kernel_str.get_function("train_sg")
words = np.empty(self.num_sents, dtype=np.int32)
# sent_reps = np.copy(self.sent_reps)
for iter in range(num_iter):
# Sample words for each sentence and transfer to GPU
for s_idx in range(self.num_sents):
words[s_idx] = random.choice(idx_sentences[s_idx])
words_gpu = cuda.to_device(words)
kernel(self.sent_reps_gpu, np.float32(self.alpha), words_gpu, self.base_word_rep, self.syn1_gpu,
block=(self.size, 1, 1), grid=(self.num_sents, 1, 1))
# autoinit.context.synchronize()
self.sent_reps = cuda.from_device(self.sent_reps_gpu, self.sent_reps.shape, self.sent_reps.dtype)
pickle_dump(self.sent_reps, model_file_path)
def test_simple_kernel_2(self):
mod = SourceModule("""
__global__ void multiply_them(float *dest, float *a, float *b)
{
const int i = threadIdx.x;
dest[i] = a[i] * b[i];
}
""")
multiply_them = mod.get_function("multiply_them")
a = np.random.randn(400).astype(np.float32)
b = np.random.randn(400).astype(np.float32)
a_gpu = drv.to_device(a)
b_gpu = drv.to_device(b)
dest = np.zeros_like(a)
multiply_them(
drv.Out(dest), a_gpu, b_gpu,
block=(400, 1, 1))
assert la.norm(dest-a*b) == 0
drv.Context.synchronize()
# now try with offsets
dest = np.zeros_like(a)
multiply_them(
drv.Out(dest), np.intp(a_gpu)+a.itemsize, b_gpu,
block=(399, 1, 1))
assert la.norm((dest[:-1]-a[1:]*b[:-1])) == 0
def multiply_csr(matrix, vector, block_size, repeat=1):
'''
Method multiply matrix by vector using CUDA module for CSR.
Calculation executed on nVidia GPU.
Parameters
==========
matrix : Scipy matrix or numpy array
Matrix to multiplication.
vector : numpy array
Vector to multiplication. His length must equal number of columns
matrix.
block_size : int (recommended 128 or 256)
Size of block CUDA.
repeat : int > 0
Number of repetitions multiplications. It has no effect on
result. Specifies the length of returned list of execution times.
Returns
=======
Tuple of result multiplication and list of execution times.
'''
if len(vector) != matrix.shape[1]:
raise ArithmeticError('Length of the vector is not equal to the'
'number of columns of the matrix.')
matrix = mf.convert_to_scipy_csr(matrix)
data = numpy.array(matrix.data, dtype=numpy.float32)
indices = numpy.array(matrix.indices, dtype=numpy.int32)
indptr = numpy.array(matrix.indptr, dtype=numpy.int32)
data = cuda.to_device(data)
indices = cuda.to_device(indices)
indptr = cuda.to_device(indptr)
num_rows = matrix.shape[0]
result = numpy.zeros(num_rows, dtype=numpy.float32)
time_list = []
grid_size = int(numpy.ceil((num_rows+0.0)/block_size))
block = (block_size, 1, 1)
grid = (grid_size, 1)
g_vector = cuda.to_device(vector)
num_rows = numpy.int32(num_rows)
kernel, texref = cudacodes.get_cuda_csr(block_size=block_size)
texref.set_address(g_vector, vector.nbytes)
tex = [texref]
for _ in range(repeat):
start.record()
kernel(data,
indices,
indptr,
cuda.Out(result),
num_rows,
block=block,
grid=grid,
texrefs=tex)
end.record()
end.synchronize()
time_list.append(start.time_till(end))
return (result, time_list)
def get_phir_gpu (XK, XV, surface, field, par_reac, kernel):
REAL = par_reac.REAL
Nq = len(field.xq)
N = len(XK)
MV = numpy.zeros(len(XK))
L = numpy.sqrt(2*surface.Area) # Representative length
AI_int = 0
# Setup vector
K = par_reac.K
tic = time.time()
w = getWeights(K)
X_V = numpy.zeros(N*K)
X_Kx = numpy.zeros(N*K)
X_Ky = numpy.zeros(N*K)
X_Kz = numpy.zeros(N*K)
X_Kc = numpy.zeros(N*K)
X_Vc = numpy.zeros(N*K)
for i in range(N*K):
X_V[i] = XV[i/K]*w[i%K]*surface.Area[i/K]
X_Kx[i] = XK[i/K]*w[i%K]*surface.Area[i/K]*surface.normal[i/K,0]
X_Ky[i] = XK[i/K]*w[i%K]*surface.Area[i/K]*surface.normal[i/K,1]
X_Kz[i] = XK[i/K]*w[i%K]*surface.Area[i/K]*surface.normal[i/K,2]
X_Kc[i] = XK[i/K]
X_Vc[i] = XV[i/K]
toc = time.time()
time_set = toc - tic
sort = surface.sortSource
phir = cuda.to_device(numpy.zeros(Nq, dtype=REAL))
m_gpu = cuda.to_device(X_V[sort].astype(REAL))
mx_gpu = cuda.to_device(X_Kx[sort].astype(REAL))
my_gpu = cuda.to_device(X_Ky[sort].astype(REAL))
mz_gpu = cuda.to_device(X_Kz[sort].astype(REAL))
mKc_gpu = cuda.to_device(X_Kc[sort].astype(REAL))
mVc_gpu = cuda.to_device(X_Vc[sort].astype(REAL))
AI_int_gpu = cuda.to_device(numpy.zeros(Nq, dtype=numpy.int32))
xkDev = cuda.to_device(surface.xk.astype(REAL))
wkDev = cuda.to_device(surface.wk.astype(REAL))
get_phir = kernel.get_function("get_phir")
GSZ = int(numpy.ceil(float(Nq)/par_reac.BSZ))
get_phir(phir, field.xq_gpu, field.yq_gpu, field.zq_gpu, m_gpu, mx_gpu, my_gpu, mz_gpu, mKc_gpu, mVc_gpu,
surface.xjDev, surface.yjDev, surface.zjDev, surface.AreaDev, surface.kDev, surface.vertexDev,
numpy.int32(len(surface.xj)), numpy.int32(Nq), numpy.int32(par_reac.K), xkDev, wkDev, REAL(par_reac.threshold),
AI_int_gpu, numpy.int32(len(surface.xk)), surface.XskDev, surface.WskDev, block=(par_reac.BSZ,1,1), grid=(GSZ,1))
AI_aux = numpy.zeros(Nq, dtype=numpy.int32)
AI_aux = cuda.from_device(AI_int_gpu, Nq, dtype=numpy.int32)
AI_int = numpy.sum(AI_aux)
phir_cpu = numpy.zeros(Nq, dtype=REAL)
phir_cpu = cuda.from_device(phir, Nq, dtype=REAL)
return phir_cpu, AI_int
def __init__(self, code, point, struct_ptr):
self.code = cuda.to_device(code)
self.point = cuda.to_device(point)
self.code_shape, self.code_dtype = code.shape, code.dtype
self.point_shape, self.point_dtype = point.shape, point.dtype
cuda.memcpy_htod(int(struct_ptr), np.int32(code.size))
cuda.memcpy_htod(int(struct_ptr) + 8, np.intp(int(self.code)))
cuda.memcpy_htod(int(struct_ptr) + 8 + np.intp(0).nbytes, np.intp(int(self.point)))
def sync_to_device(self):
self.object_array = np.array([f.as_array()
for f in self.object_list])
self.d_object_array = cuda.to_device(self.object_array)
self.d_object_count = cuda.to_device(np.array([self.object_count],
dtype=np.int32))
self.device_ptr = cuda.to_device(np.array([self.d_object_array,
self.d_object_count],
dtype=np.intp))
return self.device_ptr
def nlargest(self, n):
"""Returns the per-individual threshold above which there are n outputs.
@param n: number of outputs which should be above the threshold
@type params: int
@return list of thresholds, in order of individuals, which delimit the top
n output values
"""
log.debug("enter nlargest with n=%d", n)
# Find one more output so that we can use strictly-less-than when counting
# and underestimate lift rather than overestimating it.
n = n + 1
passSizes = []
while n > 0:
nextSize = min(self.maxHeapFloats, n)
passSizes.append(nextSize)
n -= nextSize
log.debug("pass sizes: %r", passSizes)
thresholdsMat = np.ones(shape=(self.popSize,),
dtype=np.float32) * np.inf
self.thresholds = driver.to_device(thresholdsMat)
uintBytes = np.dtype(np.uint32).itemsize
thresholdCounts = np.zeros(shape=(self.popSize,),
dtype=np.uint32)
self.thresholdCounts = driver.to_device(thresholdCounts)
for passSize in passSizes:
log.debug("begin pass size %d", passSize)
self.nlargestKernel.prepared_call(self.nlargestGridDim,
self.outputs,
self.trainSet.size,
self.popSize,
passSize,
self.thresholds,
self.thresholdCounts)
driver.Context.synchronize()
if log.isEnabledFor(logging.DEBUG):
thresholdsMat = driver.from_device_like(self.thresholds, thresholdsMat)
log.debug("thresholds: %s", str(thresholdsMat))
thresholdCounts = driver.from_device_like(self.thresholdCounts, thresholdCounts)
log.debug("thresholdCounts: %s", str(thresholdCounts))
self.thresholdsMat = driver.from_device_like(self.thresholds, thresholdsMat)
return self.thresholdsMat
def P2PKt_gpu(surfSrc, surfTar, m, mKtc, Ktx_gpu, Kty_gpu, Ktz_gpu,
surf, LorY, w, param, timing, kernel):
if param.GPU==1:
tic = cuda.Event()
toc = cuda.Event()
else:
tic = Event()
toc = Event()
tic.record()
REAL = param.REAL
mDev = cuda.to_device(m.astype(REAL))
mKtcDev = cuda.to_device(mKtc.astype(REAL))
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc)*1e-3
tic.record()
GSZ = int(numpy.ceil(float(param.Nround)/param.NCRIT)) # CUDA grid size
directKt_gpu = kernel.get_function("P2PKt")
AI_int = cuda.to_device(numpy.zeros(param.Nround, dtype=numpy.int32))
# GPU arrays are flattened, need to point to first element
ptr_offset = surf*len(surfTar.offsetTwigs[surf]) # Pointer to first element of offset arrays
ptr_list = surf*len(surfTar.P2P_list[surf]) # Pointer to first element in lists arrays
directKt_gpu(Ktx_gpu, Kty_gpu, Ktz_gpu,
surfSrc.offSrcDev, surfTar.offTwgDev, surfTar.P2P_lstDev, surfTar.sizeTarDev,
surfSrc.kDev, surfSrc.xjDev, surfSrc.yjDev, surfSrc.zjDev, mDev, mKtcDev,
surfTar.xiDev, surfTar.yiDev, surfTar.ziDev, surfSrc.AreaDev,
surfSrc.vertexDev, numpy.int32(ptr_offset), numpy.int32(ptr_list),
numpy.int32(LorY), REAL(param.kappa), REAL(param.threshold),
numpy.int32(param.BlocksPerTwig), numpy.int32(param.NCRIT), AI_int,
surfSrc.XskDev, surfSrc.WskDev, block=(param.BSZ,1,1), grid=(GSZ,1))
toc.record()
toc.synchronize()
timing.time_P2P += tic.time_till(toc)*1e-3
tic.record()
AI_aux = numpy.zeros(param.Nround, dtype=numpy.int32)
AI_aux = cuda.from_device(AI_int, param.Nround, dtype=numpy.int32)
timing.AI_int += sum(AI_aux[surfTar.unsort])
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc)*1e-3
return Ktx_gpu, Kty_gpu, Ktz_gpu
def K(self, Q, P, angles, quadratures):
drv.memcpy_htod(self.mod_K.get_global("cos_phi")[0], cos(angles).astype(scipy.float32))
drv.memcpy_htod(self.mod_K.get_global("sin_phi")[0], sin(angles).astype(scipy.float32))
Nx = Q.shape[0]
Ny = int(floor(quadratures.size / 1024.))
K = scipy.empty((Nx,), dtype=scipy.float32)
Kb = drv.mem_alloc(4*Ny*Nx)
Q_gpu = drv.to_device(Q)
P_gpu = drv.to_device(P)
self.K_gpu(drv.In(quadratures), Q_gpu, P_gpu, Kb,
block=(1, 1024, 1), grid=(Nx, Ny), shared=1024*4)
self.reduction_gpu(Kb, drv.Out(K), block=(1, Ny, 1), grid=(Nx, 1), shared=Ny*4)
return K/self.L
def __init__(self):
self.stream = cuda.Stream()
self.pool = pycuda.tools.PageLockedMemoryPool()
self._clear()
# These resources rely on the slots/ringbuffer mechanism for sharing,
# and so can be shared across any number of launches, genomes, and
# render kernels. Notably, seeds are self-synchronizing, so they're not
# attached to either stream object.
self.d_rb = cuda.to_device(np.array([0, 0], dtype=u32))
seeds = mwc.make_seeds(util.DEFAULT_RB_SIZE * 256)
self.d_seeds = cuda.to_device(seeds)
self._len_d_points = util.DEFAULT_RB_SIZE * 256 * 16
self.d_points = cuda.mem_alloc(self._len_d_points)
def M2P_gpu(surfSrc, surfTar, K_gpu, V_gpu, surf, ind0, param, LorY, timing, kernel):
if param.GPU==1:
tic = cuda.Event()
toc = cuda.Event()
else:
tic = Event()
toc = Event()
REAL = param.REAL
tic.record()
M2P_size = surfTar.offsetMlt[surf,len(surfTar.twig)]
MSort = numpy.zeros(param.Nm*M2P_size)
MdSort = numpy.zeros(param.Nm*M2P_size)
i = -1
for C in surfTar.M2P_list[surf,0:M2P_size]:
i+=1
MSort[i*param.Nm:i*param.Nm+param.Nm] = surfSrc.tree[C].M
MdSort[i*param.Nm:i*param.Nm+param.Nm] = surfSrc.tree[C].Md
# (free, total) = cuda.mem_get_info()
# print 'Global memory occupancy: %f%% free'%(free*100/total)
MDev = cuda.to_device(MSort.astype(REAL))
MdDev = cuda.to_device(MdSort.astype(REAL))
# (free, total) = cuda.mem_get_info()
# print 'Global memory occupancy: %f%% free'%(free*100/total)
# GPU arrays are flattened, need to point to first element
ptr_offset = surf*len(surfTar.offsetTwigs[surf]) # Pointer to first element of offset arrays
ptr_list = surf*len(surfTar.P2P_list[surf]) # Pointer to first element in lists arrays
GSZ = int(numpy.ceil(float(param.Nround)/param.NCRIT)) # CUDA grid size
multipole_gpu = kernel.get_function("M2P")
multipole_gpu(K_gpu, V_gpu, surfTar.offMltDev, surfTar.sizeTarDev,
surfTar.xcDev, surfTar.ycDev, surfTar.zcDev,
MDev, MdDev, surfTar.xiDev, surfTar.yiDev, surfTar.ziDev,
ind0.indexDev, numpy.int32(ptr_offset), numpy.int32(ptr_list), REAL(param.kappa),
numpy.int32(param.BlocksPerTwig), numpy.int32(param.NCRIT), numpy.int32(LorY),
block=(param.BSZ,1,1), grid=(GSZ,1))
toc.record()
toc.synchronize()
timing.time_M2P += tic.time_till(toc)*1e-3
return K_gpu, V_gpu
def test_multichannel_linear_texture(self):
mod = SourceModule("""
#define CHANNELS 4
texture<float4, 1, cudaReadModeElementType> mtx_tex;
__global__ void copy_texture(float *dest)
{
int i = threadIdx.x+blockDim.x*threadIdx.y;
float4 texval = tex1Dfetch(mtx_tex, i);
dest[i*CHANNELS + 0] = texval.x;
dest[i*CHANNELS + 1] = texval.y;
dest[i*CHANNELS + 2] = texval.z;
dest[i*CHANNELS + 3] = texval.w;
}
""")
copy_texture = mod.get_function("copy_texture")
mtx_tex = mod.get_texref("mtx_tex")
shape = (16, 16)
channels = 4
a = np.random.randn(*(shape+(channels,))).astype(np.float32)
a_gpu = drv.to_device(a)
mtx_tex.set_address(a_gpu, a.nbytes)
mtx_tex.set_format(drv.array_format.FLOAT, 4)
dest = np.zeros(shape+(channels,), dtype=np.float32)
copy_texture(drv.Out(dest),
block=shape+(1,),
texrefs=[mtx_tex]
)
#print a
#print dest
assert la.norm(dest-a) == 0
def index_list_backend(self, ilists):
from pytools import single_valued
ilist_length = single_valued(len(il) for il in ilists)
assert ilist_length == self.plan.dofs_per_face
from cgen import Typedef, POD
from pytools import flatten
flat_ilists_uncast = numpy.array(list(flatten(ilists)))
if numpy.max(flat_ilists_uncast) >= 256:
tp = numpy.uint16
else:
tp = numpy.uint8
flat_ilists = numpy.asarray(flat_ilists_uncast, dtype=tp)
assert (flat_ilists == flat_ilists_uncast).all()
return GPUIndexLists(
type=tp,
code=[Typedef(POD(tp, "index_list_entry_t"))],
device_memory=cuda.to_device(flat_ilists),
bytes=flat_ilists.size * flat_ilists.itemsize,
)
def batch_indexing(self, planes, data_points):
data_size = data_points.shape[0] / 128
self.benchmark_begin('preparing')
gpu_alloc_objs = []
# for data points
#addresses = []
#for point in data_points:
# point_addr = drv.to_device(point)
# gpu_alloc_objs.append(point_addr)
# addresses.append(int(point_addr))
#np_addresses = numpy.array(addresses).astype(numpy.uint64)
# 64 bit addressing space. each point costs 8 bytes
#arrays_gpu = drv.mem_alloc(np_addresses.shape[0] * 8)
#drv.memcpy_htod(arrays_gpu, np_addresses)
# for planes
planes_addresses = []
for plane in planes:
plane_addr = drv.to_device(plane)
gpu_alloc_objs.append(plane_addr)
planes_addresses.append(int(plane_addr))
planes_np_addresses = numpy.array(planes_addresses).astype(numpy.uint64)
# 64 bit addressing space. each point costs 8 bytes
planes_arrays_gpu = drv.mem_alloc(planes_np_addresses.shape[0] * 8)
drv.memcpy_htod(planes_arrays_gpu, planes_np_addresses)
# projections
projections = numpy.zeros(data_size).astype(numpy.uint64)
length = numpy.array([data_size]).astype(numpy.uint64)
print "total: " + str(data_size) + " data points to indexing."
self.benchmark_end('preparing')
self.benchmark_begin('cudaing')
self.indexing_kernel(
planes_arrays_gpu, drv.In(data_points), drv.Out(projections), drv.In(length),
block = self.block, grid = self.grid)
self.benchmark_end('cudaing')
#count = 0
#for pro in projections:
# print "count: " + str(count) + " " + str(pro)
# count += 1
#print projections.shape
return projections
def go_sort_old(count, stream=None):
data = np.fromstring(np.random.bytes(count), dtype=np.uint8)
ddata = cuda.to_device(data)
print 'Done seeding'
grids = count / 8192
pfxs = np.zeros((grids + 1, 256), dtype=np.int32)
dpfxs = cuda.to_device(pfxs)
launch('prefix_scan_8_0_shmem_shortseg', ddata, dpfxs,
block=(32, 16, 1), grid=(grids, 1), stream=stream, l1=1)
#dsplit = cuda.to_device(pfxs)
#launch('crappy_split', dpfxs, dsplit,
#block=(32, 8, 1), grid=(grids / 256, 1), stream=stream, l1=1)
dsplit = cuda.mem_alloc(grids * 256 * 4)
launch('better_split', dsplit, dpfxs,
block=(32, 1, 1), grid=(grids / 32, 1), stream=stream)
#if not stream:
#split = cuda.from_device_like(dsplit, pfxs)
#split_ = cuda.from_device_like(dsplit_, pfxs)
#print np.all(split == split_)
dshortseg_pfxs = cuda.mem_alloc(256 * 4)
dshortseg_sums = cuda.mem_alloc(256 * 4)
launch('prefix_sum', dpfxs, np.int32(grids * 256),
dshortseg_pfxs, dshortseg_sums,
block=(32, 8, 1), grid=(1, 1), stream=stream, l1=1)
dsorted = cuda.mem_alloc(count * 4)
launch('sort_8', ddata, dsorted, dpfxs,
block=(32, 16, 1), grid=(grids, 1), stream=stream, l1=1)
launch('sort_8_a', ddata, dsorted, dpfxs, dsplit,
block=(32, 32, 1), grid=(grids, 1), stream=stream)
if not stream:
sorted = cuda.from_device(dsorted, (count,), np.int32)
f = lambda r: ''.join(['\n\t%3d %4d %4d' % v for v in r])
sort_stat = f(rle(sorted))
with open('dev.txt', 'w') as fp: fp.write(sort_stat)
sorted_np = np.sort(data)
np_stat = f(rle(sorted_np))
with open('cpu.txt', 'w') as fp: fp.write(np_stat)
print 'is_sorted?', np.all(sorted == sorted_np)
def cls_init(self,kernel_nr,y_cls,cls1,cls2,cls1_n,cls2_n):
"""
Prepare cuda kernel call for kernel_nr, copy data for particular binary classifier, between class 1 vs 2.
Parameters
------------
kernel_nr : int
concurrent kernel number
y_cls : array-like
binary class labels (1,-1)
cls1: int
first class number
cls2: int
second class number
cls1_n : int
number of elements of class 1
cls2_n : int
number of elements of class 2
kernel_out : array-like
array for gpu kernel result, size=2*len(y_cls)
"""
warp=32
align_cls1_n = cls1_n+(warp-cls1_n%warp)%warp
align_cls2_n = cls2_n+(warp-cls2_n%warp)%warp
self.cls1_N_aligned=align_cls1_n
sum_cls= align_cls1_n+align_cls2_n
self.sum_cls[kernel_nr] = sum_cls
self.cls_count[kernel_nr] = np.array([cls1_n,cls2_n],dtype=np.int32)
self.cls[kernel_nr] = np.array([cls1,cls2],dtype=np.int32)
self.g_cls_count[kernel_nr] = cuda.to_device(self.cls_count[kernel_nr])
self.g_cls[kernel_nr] = cuda.to_device(self.cls[kernel_nr])
self.bpg[kernel_nr] =int( np.ceil( (self.threadsPerRow*sum_cls+0.0)/self.tpb ))
self.g_y[kernel_nr] = cuda.to_device(y_cls)
self.kernel_out[kernel_nr] = np.zeros(2*y_cls.shape[0],dtype=np.float32)
ker_out = self.kernel_out[kernel_nr]
self.g_out[kernel_nr] = cuda.to_device(ker_out) # cuda.mem_alloc_like(ker_out)
def make_superblocks(devdata, struct_name, single_item, multi_item, extra_fields={}):
from hedge.backends.cuda.tools import pad_and_join
# single_item = [([ block1, block2, ... ], decl), ...]
# multi_item = [([ [ item1, item2, ...], ... ], decl), ...]
multi_blocks = [
["".join(s) for s in part_data]
for part_data, part_decls in multi_item]
block_sizes = [
max(len(b) for b in part_blocks)
for part_blocks in multi_blocks]
from pytools import single_valued
block_count = single_valued(
len(si_part_blocks) for si_part_blocks, si_part_decl in single_item)
from cgen import Struct, ArrayOf
struct_members = []
for part_data, part_decl in single_item:
assert block_count == len(part_data)
single_valued(len(block) for block in part_data)
struct_members.append(part_decl)
for part_data, part_decl in multi_item:
struct_members.append(
ArrayOf(part_decl, max(len(s) for s in part_data)))
superblocks = []
for superblock_num in range(block_count):
data = ""
for part_data, part_decl in single_item:
data += part_data[superblock_num]
for part_blocks, part_size in zip(multi_blocks, block_sizes):
assert block_count == len(part_blocks)
data += pad(part_blocks[superblock_num], part_size)
superblocks.append(data)
superblock_size = devdata.align(
single_valued(len(sb) for sb in superblocks))
data = pad_and_join(superblocks, superblock_size)
assert len(data) == superblock_size*block_count
class SuperblockedDataStructure(Record):
pass
return SuperblockedDataStructure(
struct=Struct(struct_name, struct_members),
device_memory=cuda.to_device(data),
block_bytes=superblock_size,
data=data,
**extra_fields
)
def __init__(self, fields, str_f, pt0, pt1):
"""
"""
common.check_type('fields', fields, Fields)
common.check_type('str_f', str_f, (str, list, tuple), str)
common.check_type('pt0', pt0, (list, tuple), (int, float))
common.check_type('pt1', pt1, (list, tuple), (int, float))
pt0 = list( common.convert_indices(fields.ns, pt0) )
pt1 = list( common.convert_indices(fields.ns, pt1) )
# local variables
str_fs = common.convert_to_tuple(str_f)
dtype_str_list = fields.dtype_str_list
for strf in str_fs:
strf_list = ['ex', 'ey', 'ez', 'hx', 'hy', 'hz']
common.check_value('str_f', strf, strf_list)
for axis, n, p0, p1 in zip(['x', 'y', 'z'], fields.ns, pt0, pt1):
common.check_value('pt0 %s' % axis, p0, range(n))
common.check_value('pt1 %s' % axis, p1, range(n))
# program
macros = ['NMAX', 'XID', 'YID', 'ZID', \
'ARGS', \
'TARGET', 'SOURCE', 'OVERWRITE', \
'DTYPE']
values = common_gpu.macro_replace_list(pt0, pt1) + \
['DTYPE *source', \
'target[sub_idx]', 'source[idx]', '='] + \
dtype_str_list
ksrc = common.replace_template_code( \
open(common_gpu.src_path + 'copy.cu').read(), macros, values)
program = SourceModule(ksrc)
kernel_copy = program.get_function('copy')
# allocation
source_bufs = [fields.get_buf(str_f) for str_f in str_fs]
shape = common.shape_two_points(pt0, pt1, len(str_fs))
host_array = np.zeros(shape, fields.dtype)
split_host_array = np.split(host_array, len(str_fs))
split_host_array_dict = dict( zip(str_fs, split_host_array) )
target_buf = cuda.to_device(host_array)
# global variables
self.mainf = fields
self.kernel_copy = kernel_copy
self.source_bufs = source_bufs
self.target_buf = target_buf
self.host_array = host_array
self.split_host_array_dict = split_host_array_dict
def set_refsmiles(self,refsmilesmat,refcountsmat,reflengths,refmags=None): #{{{
"""Sets the reference SMILES set to use Lingo matrix *refsmilesmat*, count matrix *refcountsmat*,
and length vector *reflengths*. If *refmags* is provided, it will be used as the magnitude
vector; else, the magnitude vector will be computed (on the GPU) from the count matrix.
Because of hardware limitations, the reference matrices (*refsmilesmat* and *refcountsmat*) must have
no more than 32,768 rows (molecules) and 65,536 columns (Lingos). Larger computations must be performed in tiles.
"""
# Set up lingo and count matrices on device #{{{
if self.usePycudaArray:
# Set up using PyCUDA CUDAArray support
self.gpu.rsmiles = cuda.matrix_to_array(refsmilesmat,order='C')
self.gpu.rcounts = cuda.matrix_to_array(refcountsmat,order='C')
self.gpu.tex2lr.set_array(self.gpu.rsmiles)
self.gpu.tex2cr.set_array(self.gpu.rcounts)
else:
# Manually handle setup
temprlmat = self._padded_array(refsmilesmat)
if temprlmat.shape[1] > 65536 or temprlmat.shape[0] > 32768:
raise ValueError("Error: reference matrix is not allowed to have more than 64K columns (LINGOs) or 32K rows (molecules) (both padded to multiple of 16). Dimensions = (%d,%d)."%temprlmat.shape)
self.gpu.rsmiles = cuda.mem_alloc(temprlmat.nbytes)
cuda.memcpy_htod_async(self.gpu.rsmiles,temprlmat,stream=self.gpu.stream)
temprcmat = self._padded_array(refcountsmat)
self.gpu.rcounts = cuda.mem_alloc(temprcmat.nbytes)
cuda.memcpy_htod_async(self.gpu.rcounts,temprcmat,stream=self.gpu.stream)
descriptor = cuda.ArrayDescriptor()
descriptor.width = temprcmat.shape[1]
descriptor.height = temprcmat.shape[0]
descriptor.format = cuda.array_format.UNSIGNED_INT32
descriptor.num_channels = 1
self.gpu.tex2lr.set_address_2d(self.gpu.rsmiles,descriptor,temprlmat.strides[0])
self.gpu.tex2cr.set_address_2d(self.gpu.rcounts,descriptor,temprcmat.strides[0])
self.gpu.stream.synchronize()
del temprlmat
del temprcmat
#}}}
self.rlengths = reflengths
self.rshape = refsmilesmat.shape
self.nref = refsmilesmat.shape[0]
# Copy reference lengths to GPU
self.gpu.rl_gpu = cuda.to_device(reflengths)
# Allocate buffers for query set magnitudes
self.gpu.rmag_gpu = cuda.mem_alloc(reflengths.nbytes)
if refmags is not None:
cuda.memcpy_htod(self.gpu.rmag_gpu,refmags)
else:
# Calculate query set magnitudes on GPU
magthreads = 256
self.gpu.refMagKernel(self.gpu.rmag_gpu,self.gpu.rl_gpu,numpy.int32(self.nref),block=(magthreads,1,1),grid=(30,1),shared=magthreads*4,texrefs=[self.gpu.tex2cr])
return
def get_heartbeat(d_lead, length, sampling_rate):
# Kernel Parameters
threads_per_block = 200
num_blocks = length / threads_per_block
# Get RR
reduce_by = 32
edge_signal = cuda.mem_alloc(4 * length)
edge_detect(edge_signal, d_lead,
grid=(num_blocks, 1), block=(threads_per_block, 1, 1))
indecies = numpy.zeros(length / reduce_by).astype(numpy.int32)
masks = cuda.to_device(numpy.zeros(length / reduce_by).astype(numpy.int32))
d_index = cuda.to_device(indecies)
index_of_peak(d_index, masks, edge_signal,
grid=(num_blocks, 1), block=(threads_per_block, 1, 1))
cd_index, c_length = compact_sparse_with_mask(d_index, masks, length / reduce_by)
# Allocate output
# full_rr_signal = numpy.zeros(c_length).astype(numpy.int32)
dev_rr = cuda.mem_alloc(c_length * 4)
num_blocks = (c_length / threads_per_block) + 1
get_compact_rr(dev_rr,
cd_index,
numpy.int32(sampling_rate),
numpy.int32(c_length),
grid=(num_blocks, 1), block=(threads_per_block, 1, 1))
clean_result(dev_rr, numpy.int32(120), numpy.int32(40),
numpy.int32(1), numpy.int32(c_length),
grid=(num_blocks, 1), block=(threads_per_block, 1, 1))
moving_average_filter(dev_rr, c_length, 250)
index = cuda.from_device(cd_index, (c_length,), numpy.int32)
rr = cuda.from_device(dev_rr, (c_length,), numpy.int32)
index[0] = index[1]
return rr, index / float(sampling_rate * 3600)
def __init__(self, array, struct_arr_ptr):
print "copying data to device"
self.data = cuda.to_device(array)
self.shape, self.dtype = array.shape, array.dtype
cuda.memcpy_htod(int(struct_arr_ptr),
numpy.getbuffer(numpy.int32(len(array[0]))))
cuda.memcpy_htod(int(struct_arr_ptr) + 8,
numpy.getbuffer(numpy.intp(int(self.data))))
def P2P_gpu(surfSrc, surfTar, m, mx, my, mz, mKc, mVc, K_gpu, V_gpu,
surf, LorY, K_diag, IorE, L, w, param, timing, kernel):
tic = cuda.Event()
toc = cuda.Event()
tic.record()
REAL = param.REAL
mDev = cuda.to_device(m.astype(REAL))
mxDev = cuda.to_device(mx.astype(REAL))
myDev = cuda.to_device(my.astype(REAL))
mzDev = cuda.to_device(mz.astype(REAL))
mKcDev = cuda.to_device(mKc.astype(REAL))
mVcDev = cuda.to_device(mVc.astype(REAL))
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc)*1e-3
tic.record()
GSZ = int(ceil(float(param.Nround)/param.NCRIT)) # CUDA grid size
direct_gpu = kernel.get_function("P2P")
AI_int = cuda.to_device(zeros(param.Nround, dtype=int32))
# GPU arrays are flattened, need to point to first element
ptr_offset = surf*len(surfTar.offsetTwigs[surf]) # Pointer to first element of offset arrays
ptr_list = surf*len(surfTar.P2P_list[surf]) # Pointer to first element in lists arrays
# Check if internal or external to send correct singular integral
if IorE==1:
sglInt = surfSrc.sglInt_intDev
else:
sglInt = surfSrc.sglInt_extDev
direct_gpu(K_gpu, V_gpu, surfSrc.offSrcDev, surfTar.offTwgDev, surfTar.P2P_lstDev, surfTar.sizeTarDev,
surfSrc.kDev, surfSrc.xjDev, surfSrc.yjDev, surfSrc.zjDev, mDev, mxDev, myDev, mzDev,
mKcDev, mVcDev, surfTar.xiDev, surfTar.yiDev, surfTar.ziDev, surfSrc.AreaDev, sglInt,
surfSrc.vertexDev, int32(ptr_offset), int32(ptr_list),
int32(LorY), REAL(param.kappa), REAL(param.threshold),
int32(param.BlocksPerTwig), int32(param.NCRIT), REAL(K_diag), AI_int,
surfSrc.XskDev, surfSrc.WskDev, block=(param.BSZ,1,1), grid=(GSZ,1))
toc.record()
toc.synchronize()
timing.time_P2P += tic.time_till(toc)*1e-3
tic.record()
AI_aux = zeros(param.Nround, dtype=int32)
AI_aux = cuda.from_device(AI_int, param.Nround, dtype=int32)
timing.AI_int += sum(AI_aux[surfTar.unsort])
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc)*1e-3
return K_gpu, V_gpu
def allocate_gpu(s): a = np.zeros((6,5),'f') if s.num_device == 0: a[-2,:] = 1.5 if s.num_device == 1: a[1,:] = 2.0 a[-2,:] = 2.5 if s.num_device == 2: a[1,:] = 3.0 s.arr_gpu = cuda.to_device(a) s.nx, s.ny = s.shape = a.shape s.dtype = a.dtype
def transfer_leads(*h_leads):
length = len(h_leads[0])
result = []
grid = ((length / 1024)+1, 1)
block = (1024, 1, 1)
for h_lead in h_leads:
d_lead16 = cuda.to_device(h_lead)
d_lead32 = cuda.mem_alloc(h_lead.nbytes * 2)
to_float(d_lead32, d_lead16, numpy.int32(length),
grid=grid, block=block)
result.append(d_lead32)
return tuple(result) + (length,)
def __init__(self, array, struct_arr_ptr):
self.data = cuda.to_device(array)
self.shape, self.dtype = array.shape, array.dtype
"""
numpy.getbuffer() needed due to lack of new-style buffer interface for
scalar numpy arrays as of numpy version 1.9.1
see: https://github.com/inducer/pycuda/pull/60
"""
cuda.memcpy_htod(int(struct_arr_ptr),
numpy.getbuffer(numpy.int32(array.size)))
cuda.memcpy_htod(int(struct_arr_ptr) + 8,
numpy.getbuffer(numpy.uintp(int(self.data))))
def local_to_device(self, early_free=True):
"""
Copies locally set array data to device memory.
Parameters
----------
early_free: boolean
When True, will automatically free all device memory
before allocation, preventing double-allocation for a short time.
"""
if self.device_ptr != None and early_free == True:
self.device_ptr.free()
self.device_ptr = drv.to_device(self.local_array)
def make_blocks(devdata, data):
from hedge.backends.cuda.tools import pad_and_join
blocks = ["".join(b) for b in data]
block_size = devdata.align(max(len(b) for b in blocks))
class BlockedDataStructure(Record):
pass
return BlockedDataStructure(
blocks=cuda.to_device(pad_and_join(blocks, block_size)),
max_per_block=max(len(b) for b in data),
block_size=block_size,
)
def alloc_eh_fields(s): f = np.zeros((s.nx, s.ny, s.nz), 'f') s.ex_gpu = cuda.to_device(f) s.ey_gpu = cuda.to_device(f) s.ez_gpu = cuda.to_device(f) s.hx_gpu = cuda.to_device(f) s.hy_gpu = cuda.to_device(f) s.hz_gpu = cuda.to_device(f) s.eh_fields = [s.ex_gpu, s.ey_gpu, s.ez_gpu, s.hx_gpu, s.hy_gpu, s.hz_gpu]
tpb = 256
bpg = (nx * ny * nz) / tpb
print 'dim (%d, %d, %d)' % (nx, ny, nz)
total_bytes = nx * ny * nz * 4 * 9
if total_bytes / (1024**3) == 0:
print 'mem %d MB' % (total_bytes / (1024**2))
else:
print 'mem %1.2f GB' % (float(total_bytes) / (1024**3))
# memory allocate
f = np.random.randn(nx * ny * nz).astype(np.float32).reshape((nx, ny, nz))
#f = np.zeros((nx,ny,nz), 'f')
ex_gpu = cuda.to_device(f)
ey_gpu = cuda.to_device(f)
ez_gpu = cuda.to_device(f)
hx_gpu = cuda.to_device(f)
hy_gpu = cuda.to_device(f)
hz_gpu = cuda.to_device(f)
cex = set_c(f, 'yz')
cey = set_c(f, 'zx')
cez = set_c(f, 'xy')
cex_gpu = cuda.to_device(cex)
cey_gpu = cuda.to_device(cey)
cez_gpu = cuda.to_device(cez)
descr = cuda.ArrayDescriptor3D()
def go_sort_old(count, stream=None):
data = np.fromstring(np.random.bytes(count), dtype=np.uint8)
ddata = cuda.to_device(data)
print 'Done seeding'
grids = count / 8192
pfxs = np.zeros((grids + 1, 256), dtype=np.int32)
dpfxs = cuda.to_device(pfxs)
launch('prefix_scan_8_0_shmem_shortseg',
ddata,
dpfxs,
block=(32, 16, 1),
grid=(grids, 1),
stream=stream,
l1=1)
#dsplit = cuda.to_device(pfxs)
#launch('crappy_split', dpfxs, dsplit,
#block=(32, 8, 1), grid=(grids / 256, 1), stream=stream, l1=1)
dsplit = cuda.mem_alloc(grids * 256 * 4)
launch('better_split',
dsplit,
dpfxs,
block=(32, 1, 1),
grid=(grids / 32, 1),
stream=stream)
#if not stream:
#split = cuda.from_device_like(dsplit, pfxs)
#split_ = cuda.from_device_like(dsplit_, pfxs)
#print np.all(split == split_)
dshortseg_pfxs = cuda.mem_alloc(256 * 4)
dshortseg_sums = cuda.mem_alloc(256 * 4)
launch('prefix_sum',
dpfxs,
np.int32(grids * 256),
dshortseg_pfxs,
dshortseg_sums,
block=(32, 8, 1),
grid=(1, 1),
stream=stream,
l1=1)
dsorted = cuda.mem_alloc(count * 4)
launch('sort_8',
ddata,
dsorted,
dpfxs,
block=(32, 16, 1),
grid=(grids, 1),
stream=stream,
l1=1)
launch('sort_8_a',
ddata,
dsorted,
dpfxs,
dsplit,
block=(32, 32, 1),
grid=(grids, 1),
stream=stream)
if not stream:
sorted = cuda.from_device(dsorted, (count, ), np.int32)
f = lambda r: ''.join(['\n\t%3d %4d %4d' % v for v in r])
sort_stat = f(rle(sorted))
with open('dev.txt', 'w') as fp:
fp.write(sort_stat)
sorted_np = np.sort(data)
np_stat = f(rle(sorted_np))
with open('cpu.txt', 'w') as fp:
fp.write(np_stat)
print 'is_sorted?', np.all(sorted == sorted_np)
def make_frame(frame_dir,
frame,
image_dir,
frames,
global_vars,
find_total_max=False,
save_density=False,
save_entanglement=False,
save_expectation_value=False,
exp_range=[0, 0],
**kwargs):
global yMax, rhoMax, rhoMin
frame_number = (frame.split("_")[1]).split(".")[0]
print "Plotting", frame_dir
QuantumState = np.load(frame_dir)
# for i in xrange(len(QuantumState)):
# # if QuantumState[i,0,0,0]*QuantumState[i,0,0,0].conjugate()>0.:
# print i, QuantumState[i,0,0,0]
# print "Info prob:",np.sum(QuantumState*QuantumState.conjugate())
# QuantumState+= 1.
fig = plt.figure(figsize=(15, 12))
oldQuantumState = None
oldQuantumState = QuantumState.copy()
xSize = global_vars["Qx"] / 2
ySize = global_vars["Qy"] / 2
zSize = 1
blockX = 256
x_nSize = xSize * ySize * (2 * xSize * ySize - 1)
while x_nSize % blockX != 0:
blockX /= 2
gridX = x_nSize / blockX
blockY = 1
blockZ = 1
gridY = 1
gridZ = 1
blockX_r1_r2 = 16
blockY_r1_r2 = 16
blockZ_r1_r2 = 1
while xSize % blockX_r1_r2 != 0:
blockX_r1_r2 /= 2
blockY_r1_r2 /= 2
gridX_r1_r2 = xSize * xSize / blockX_r1_r2
gridY_r1_r2 = ySize * ySize / blockY_r1_r2
gridZ_r1_r2 = 1
QuantumField_r1_r2_real = np.zeros((xSize, ySize, xSize, ySize),
dtype=np.float64)
QuantumField_r1_r2_imag = np.zeros((xSize, ySize, xSize, ySize),
dtype=np.float64)
Rho_projected_real = np.zeros((xSize, ySize), dtype=np.float64)
# AnalyticFieldReal = np.zeros((xSize, xSize), dtype = np.float64)
# AnalyticFieldImag = np.zeros((xSize, xSize), dtype = np.float64)
Lattice = np.zeros(3, dtype=np.int_)
Lattice[0], Lattice[1], Lattice[2] = xSize, ySize, zSize
Time = np.zeros(1, dtype=np.float64)
Time[0] = int(frame_number)
# purity = np.zeros(1, dtype = np.float64)
# gpuPurity = drv.to_device(purity)
gpuLattice = drv.to_device(Lattice)
gpuTime = drv.to_device(Time)
gpuQField = drv.to_device(QuantumState)
gpuQF_r1_r2_real = drv.to_device(QuantumField_r1_r2_real)
gpuQF_r1_r2_imag = drv.to_device(QuantumField_r1_r2_imag)
gpuRho_projected_real = drv.to_device(Rho_projected_real)
# gpuAnalyticFieldReal = drv.to_device(AnalyticFieldReal)
# gpuAnalyticFieldImag = drv.to_device(AnalyticFieldImag)
get_QS(gpuQField,
gpuQF_r1_r2_real,
gpuQF_r1_r2_imag,
gpuLattice,
block=(blockX, blockY, blockZ),
grid=(gridX, gridY, gridZ))
get_Rho_projected(gpuQF_r1_r2_real,
gpuQF_r1_r2_imag,
gpuRho_projected_real,
gpuLattice,
block=(blockX_r1_r2, blockY_r1_r2, blockZ_r1_r2),
grid=(gridX_r1_r2, gridY_r1_r2, gridZ_r1_r2))
# if save_entanglement:
# calcPurity(gpuQF_r1_r2_real, gpuQF_r1_r2_imag, gpuPurity, gpuLattice, block=(blockX_r1_r2,blockY_r1_r2,blockZ_r1_r2), grid=(gridX_r1_r2,gridY_r1_r2,gridZ_r1_r2))
# get_Analytic(gpuAnalyticFieldReal, gpuAnalyticFieldImag, gpuAn, gpuBn, gpuAn2, gpuBn2, gpuLattice, gpuTime, block=(blockX,blockY,blockZ), grid=(gridX,gridY,gridZ))
QuantumField_r1_r2_real = drv.from_device(gpuQF_r1_r2_real,
QuantumField_r1_r2_real.shape,
np.float64)
QuantumField_r1_r2_imag = drv.from_device(gpuQF_r1_r2_imag,
QuantumField_r1_r2_imag.shape,
np.float64)
Rho_projected_real = drv.from_device(gpuRho_projected_real,
Rho_projected_real.shape, np.float64)
# AnalyticFieldReal = drv.from_device(gpuAnalyticFieldReal, AnalyticFieldReal.shape, np.float64)
# AnalyticFieldImag = drv.from_device(gpuAnalyticFieldImag, AnalyticFieldImag.shape, np.float64)
RhoFieldProjected = np.zeros((xSize, ySize), dtype=DTYPE)
# RhoFieldProjectedAnalytic = np.zeros((xSize), dtype = DTYPE)
rho_total = np.sum(Rho_projected_real)
# print rho_total
# print rho_total
for x in xrange(xSize):
for y in xrange(ySize):
RhoFieldProjected[x, y] = Rho_projected_real[x, y] / rho_total
#Get Rho and Phase from QField
# RhoField = (QuantumField_r1_r2_real+1.j*QuantumField_r1_r2_imag)*(QuantumField_r1_r2_real+1.j*QuantumField_r1_r2_imag).conjugate()
# PhaseField = np.arctan2(QuantumField_r1_r2_imag,QuantumField_r1_r2_real)+np.pi
if int(frame_number) == 0:
# rhoMin, rhoMax = np.amin(RhoField).real, np.amax(RhoField).real
yMax = np.amax(RhoFieldProjected.real)
time = int(frame_number)
# purity = drv.from_device(gpuPurity, purity.shape, np.float64)
# entanglement = 1-2.*purity[0]/(rho_total*rho_total)
xAxis = setXaxis(xSize)
yAxis = setXaxis(ySize)
time_text = plt.suptitle(r'$\tau = $' + str(time),
fontsize=14,
horizontalalignment='center',
verticalalignment='top')
# if save_entanglement:
# time_text = plt.suptitle(r'$\tau = $' + str(time) + ' ' + r'$\mathcal{E} = $' + str(entanglement) ,fontsize=14,horizontalalignment='center',verticalalignment='top')
# full_text = plt.suptitle(r'$\tau = $' + str(time) + ' ' + r'$P_{f} = $' + str('{:1.15f}'.format(prob)) + ' ' + r'$P_{p} = $' + str('{:1.15f}'.format(probP)),fontsize=14,horizontalalignment='center',verticalalignment='top')
# gs = gridspec.GridSpec(1,1)
# ax = fig.add_subplot(gs[0,0], xlim=(0,xSize), xlabel=r'$x(\ell)$', ylim=(-0.0000001, 1.1*yMax), ylabel=r'${| \Psi |}^{2}$')
############################### PLOTTING ####################################################
# print "Rho: ", RhoFieldProjected
if yMax == 0.:
yMax += 1.
# Density
plt.subplot(111)
plt.imshow(((RhoFieldProjected.real).T),
extent=(np.amin(xAxis), np.amax(xAxis), np.amin(yAxis),
np.amax(yAxis)),
origin='lower',
cmap=colorMapRho,
norm=colors.SymLogNorm(linthresh=linThresh * yMax,
linscale=linScale,
vmin=0.,
vmax=yMax))
putLabels(r'$x\ \ (\ell)$', r'$y\ \ (\ell)$',
r'$\rho \ \ (\frac{1}{\ell^2})$')
ax = plt.gca()
ax.set_aspect('equal')
# #Phase
# plt.subplot(122)
# plt.imshow((PhaseField.real), extent=(np.amin(xAxis), np.amax(xAxis), np.amin(xAxis), np.amax(xAxis)), origin = 'lower',
# cmap=colorMapPhase, norm=colors.Normalize(vmin=0.,vmax=2.*np.pi))
# putLabels(r'$x_{1}\ \ (\ell)$', r'$x_{2}\ \ (\ell)$', r'$\theta \ \ (Radians)$')
# # Any value whose absolute value is > .0001 will have zero transparency
# alphas = Normalize(0, rhoMax, clip=True)((rhoMax-RhoField.real))
# # alphas = colors.SymLogNorm(linthresh=linThresh*rhoMax,linscale=linScale,vmin=0.,vmax=10., clip=True)((rhoMax-RhoField.real).T)
# alphas = np.clip(alphas**4, 0.0, 1) # alpha value clipped at the bottom at .4
# # Normalize the colors b/w 0 and 1, we'll then pass an MxNx4 array to imshow
# cmap = plt.cm.gist_gray
# colorsMap = colors.SymLogNorm(linthresh=linThresh*rhoMax,linscale=linScale,vmin=0.,vmax=rhoMax)((0.*RhoField.real))
# colorsMap = cmap(colorsMap)
# # Now set the alpha channel to the one we created above
# colorsMap[..., -1] = alphas
# plt.imshow(colorsMap, extent=(np.amin(xAxis), np.amax(xAxis), np.amin(xAxis), np.amax(xAxis)), origin = 'lower')
# ax = plt.gca()
# ax.set_aspect('equal')
# Screen density
fig.tight_layout(pad=0.4, w_pad=5.0, h_pad=1.0, rect=[.05, .05, .95, .95])
if save_density:
rho_dir = frame_dir.split("Data")[0] + "Density"
if not os.path.exists(rho_dir + '/'):
os.makedirs(rho_dir + '/')
print "Saving density to", rho_dir + '/Frame_' + frame_number + '.npy'
np.save(rho_dir + '/Frame_' + frame_number, RhoFieldProjected.real)
# if save_entanglement:
# ent_dir = frame_dir.split("Data")[0] + "Entanglement"
# if not os.path.exists(ent_dir+ '/'):
# os.makedirs(ent_dir + '/')
# print "Saving entanglement to", ent_dir + '/entanglement.npy'
# if time==0:
# entanglementArray = np.asarray([[entanglement, time]])
# else:
# entArrayOld = np.load(ent_dir + '/entanglement.npy' )
# entArrayNew = np.asarray([[entanglement, time]])
# entanglementArray = np.append(entArrayOld, entArrayNew, axis=0)
# np.save(ent_dir + '/entanglement' , entanglementArray)
# if save_expectation_value:
# exp_dir = frame_dir.split("Data")[0] + "expectation"
# if not os.path.exists(exp_dir+ '/'):
# os.makedirs(exp_dir + '/')
# print "Saving expectation to", exp_dir + '/expectation.npy'
# exp_val = 0.
# if exp_range==[0,0]:
# for x in xrange(xSize):
# exp_val += x*RhoFieldProjected[x].real
# else:
# for x in xrange(int(exp_range[0]*xSize),int(exp_range[1]*xSize),1):
# exp_val += x*RhoFieldProjected[x].real
# if time==0:
# expectationArray = np.asarray([exp_val])
# else:
# expArrayOld = np.load(exp_dir + '/expectation.npy' )
# expArrayNew = np.asarray([exp_val])
# expectationArray = np.append(expArrayOld, expArrayNew, axis=0)
# np.save(exp_dir + '/expectation' , expectationArray)
#Free memory
gpuLattice.free()
gpuQField.free()
gpuQF_r1_r2_real.free()
gpuQF_r1_r2_imag.free()
gpuRho_projected_real.free()
gpuTime.free()
# gpuPurity.free()
# gpuAnalyticFieldReal.free()
# gpuAnalyticFieldImag.free()
if not os.path.exists(image_dir + '/'):
os.makedirs(image_dir + '/')
fig.savefig(image_dir + '/Frame_' + frame_number + ".png")
plt.close(fig)
import numpy
import numpy.linalg as la
from pycuda.compiler import SourceModule
from six.moves import range
thread_strides = 16
block_size = 256
macroblock_count = 33
total_size = thread_strides * block_size * macroblock_count
dtype = numpy.float32
a = numpy.random.randn(total_size).astype(dtype)
b = numpy.random.randn(total_size).astype(dtype)
a_gpu = cuda.to_device(a)
b_gpu = cuda.to_device(b)
c_gpu = cuda.mem_alloc(a.nbytes)
from cgen import FunctionBody, \
FunctionDeclaration, POD, Value, \
Pointer, Module, Block, Initializer, Assign
from cgen.cuda import CudaGlobal
mod = Module([
FunctionBody(
CudaGlobal(
FunctionDeclaration(Value("void", "add"),
arg_decls=[
Pointer(POD(dtype, name))
for name in ["tgt", "op1", "op2"]
def get_phir_gpu(XK, XV, surface, field, par_reac, kernel):
"""
It computes the reaction potential on the GPU and it brings the data
to the cpu.
Arguments
----------
XK : array, input for the double layer potential.
XV : array, input for the single layer potential.
surface : class, surface where we are computing the reaction potential.
field : class, information about the different regions in the molecule.
par_reac: class, fine parameters related to the surface.
Returns
--------
phir_cpu: array, reaction potential brought from the GPU to the cpu.
AI_int : int, counter of the amount of near singular integrals solved.
"""
REAL = par_reac.REAL
Nq = len(field.xq)
N = len(XK)
AI_int = 0
# Setup vector
K = par_reac.K
tic = time.time()
w = getWeights(K)
X_V = numpy.zeros(N * K)
X_Kx = numpy.zeros(N * K)
X_Ky = numpy.zeros(N * K)
X_Kz = numpy.zeros(N * K)
X_Kc = numpy.zeros(N * K)
X_Vc = numpy.zeros(N * K)
for i in range(N * K):
X_V[i] = XV[i // K] * w[i % K] * surface.area[i // K]
X_Kx[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 0]
X_Ky[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 1]
X_Kz[i] = XK[i // K] * w[i % K] * surface.area[
i // K] * surface.normal[i // K, 2]
X_Kc[i] = XK[i // K]
X_Vc[i] = XV[i // K]
toc = time.time()
sort = surface.sortSource
phir = cuda.to_device(numpy.zeros(Nq, dtype=REAL))
m_gpu = cuda.to_device(X_V[sort].astype(REAL))
mx_gpu = cuda.to_device(X_Kx[sort].astype(REAL))
my_gpu = cuda.to_device(X_Ky[sort].astype(REAL))
mz_gpu = cuda.to_device(X_Kz[sort].astype(REAL))
mKc_gpu = cuda.to_device(X_Kc[sort].astype(REAL))
mVc_gpu = cuda.to_device(X_Vc[sort].astype(REAL))
AI_int_gpu = cuda.to_device(numpy.zeros(Nq, dtype=numpy.int32))
xkDev = cuda.to_device(surface.xk.astype(REAL))
wkDev = cuda.to_device(surface.wk.astype(REAL))
get_phir = kernel.get_function("get_phir")
GSZ = int(numpy.ceil(float(Nq) / par_reac.BSZ))
get_phir(phir,
field.xq_gpu,
field.yq_gpu,
field.zq_gpu,
m_gpu,
mx_gpu,
my_gpu,
mz_gpu,
mKc_gpu,
mVc_gpu,
surface.xjDev,
surface.yjDev,
surface.zjDev,
surface.AreaDev,
surface.kDev,
surface.vertexDev,
numpy.int32(len(surface.xj)),
numpy.int32(Nq),
numpy.int32(par_reac.K),
xkDev,
wkDev,
REAL(par_reac.threshold),
AI_int_gpu,
numpy.int32(len(surface.xk)),
surface.XskDev,
surface.WskDev,
block=(par_reac.BSZ, 1, 1),
grid=(GSZ, 1))
AI_aux = numpy.zeros(Nq, dtype=numpy.int32)
AI_aux = cuda.from_device(AI_int_gpu, Nq, dtype=numpy.int32)
AI_int = numpy.sum(AI_aux)
phir_cpu = numpy.zeros(Nq, dtype=REAL)
phir_cpu = cuda.from_device(phir, Nq, dtype=REAL)
return phir_cpu, AI_int
if( idx < nx ) f[ijk] += sin(0.1*tn); } """ if __name__ == '__main__': nx, ny, nz = 320, 320, 320 print 'dim (%d, %d, %d)' % (nx, ny, nz) print 'mem %1.2f GB' % ( nx*ny*nz*4*12./(1024**3) ) # memory allocate f = np.zeros((nx,ny,nz), 'f', order='F') cf = np.ones_like(f)*0.5 ex_gpu = cuda.to_device(f) ey_gpu = cuda.to_device(f) ez_gpu = cuda.to_device(f) hx_gpu = cuda.to_device(f) hy_gpu = cuda.to_device(f) hz_gpu = cuda.to_device(f) cex_gpu = cuda.to_device(cf) cey_gpu = cuda.to_device(cf) cez_gpu = cuda.to_device(cf) chx_gpu = cuda.to_device(cf) chy_gpu = cuda.to_device(cf) chz_gpu = cuda.to_device(cf) # prepare kernels from pycuda.compiler import SourceModule
sys.exit() if (nx*ny)%Dy != 0: print "Error: nx*ny is not multiple of %d" % (Dy) sys.exit() Bx, By = nz/Dx, nx*ny/Dy # number of block MBy = MAX_BLOCK/Bx bpg_list = [(Bx,MBy) for i in range(By/MBy)] if By%MBy != 0: bpg_list.append( (Bx,By%MBy) ) #print bpg_list # memory allocate f = np.zeros((nx,ny,nz), 'f') cf = np.ones_like(f)*0.5 ex_gpu = cuda.to_device(f) ey_gpu = cuda.to_device(f) ez_gpu = cuda.to_device(f) hx_gpu = cuda.to_device(f) hy_gpu = cuda.to_device(f) hz_gpu = cuda.to_device(f) cex = set_c(f,'yz') cey = set_c(f,'zx') cez = set_c(f,'xy') descr = cuda.ArrayDescriptor3D() descr.width = nz descr.height = ny descr.depth = nx descr.format = cuda.dtype_to_array_format(f.dtype)
plt.imshow(J)
plt.show()
cu.init()
d = cu.Device(1)
ctx = d.make_context()
kernel_size = 3
block_size = (16, 16)
#grid_size = calculate_grid_size((height, width), block_size)
grid_size = (32, 32)
#print(I.shape)
#print(grid_size)
I_gpu = cu.to_device(I.astype('float32'))
J_gpu = cu.mem_alloc(J.nbytes)
source = cu.module_from_file("sobel.cubin")
kernel_naive = source.get_function("sobel_filter")
kernel_naive.prepare(['P', 'P', 'Q', 'Q', 'Q', 'Q'])
time = kernel_naive.prepared_timed_call(grid_size, block_size, I_gpu, J_gpu, height, width, kernel_size, 4)
J1 = cu.from_device(J_gpu, shape=J.shape, dtype="float32")
print("Time spent in kernel1: {}s".format(time() * 1e-3))
print("L1 norm: {}".format( np.sum(np.sum( np.abs(J - J1) )) ))
plt.imshow(J1)
plt.show()
total_bytes = nx*ny*nz*8*9
if total_bytes/(1024**3) == 0:
print '%d MB' % ( total_bytes/(1024**2) )
else:
print '%1.2f GB' % ( float(total_bytes)/(1024**3) )
if nz%32 != 0:
print "Error: nz is not multiple of 32"
sys.exit()
# memory allocate
f = np.zeros((nx,ny,nz), dtype=np.float64)
cf = np.ones_like(f)*0.5
eh_gpus = ex_gpu, ey_gpu, ez_gpu, hx_gpu, hy_gpu, hz_gpu = [cuda.to_device(f) for i in range(6)]
ce_gpus = cex_gpu, cey_gpu, cez_gpu = [cuda.to_device(cf) for i in range(3)]
# prepare kernels
tpb = 256
for bpg in xrange(65535, 0, -1):
if (nx * ny * nz / tpb) % bpg == 0: break
print 'tpb = %d, bpg = %g' % (tpb, bpg)
from pycuda.compiler import SourceModule
mod = SourceModule( kernels.replace('Dx',str(tpb)).replace('nxyz',str(nx*ny*nz)).replace('nyz',str(ny*nz)).replace('nx',str(nx)).replace('ny',str(ny)).replace('nz',str(nz)), options=['-m 64'] )
update_h = mod.get_function("update_h")
update_e = mod.get_function("update_e")
update_src = mod.get_function("update_src")
def test_memory(self):
z = np.random.randn(400).astype(np.float32)
new_z = drv.from_device_like(drv.to_device(z), z)
assert la.norm(new_z - z) == 0
"""
kernels = """"""
import numpy as np
import sys
import pycuda.driver as cuda
import pycuda.autoinit
if __name__ == '__main__':
size = 100 # MBtye
nx = size * (1024**2) / 4
nloop = 50
# memory allocate
fw_gpu = cuda.to_device(np.zeros(nx, 'f'))
fr_gpu = cuda.to_device(np.random.randn(nx).astype(np.float32))
# prepare kernels
from pycuda.compiler import SourceModule
body = 'fr[idx]'
for i in range(nloop):
if (i > 0): body += ' + fr[idx]'
kernels += kernel_template.replace('NAME', 'func%.2d' % i).replace(
'BODY', body)
print kernels
mod = SourceModule(kernels)
kern_list = []
for i in range(nloop):
kern_list.append(mod.get_function("func%.2d" % i))
if pt[2] != None: cf[:, :, pt[2]] = 0
return cf
if __name__ == '__main__':
nx, ny, nz = 320, 320, 320
print 'dim (%d, %d, %d)' % (nx, ny, nz)
print 'mem %1.2f GB' % (nx * ny * nz * 4 * 12. / (1024**3))
# memory allocate
f = np.zeros((nx, ny, nz), 'f')
cf = np.zeros_like(f)
ex_gpu = cuda.to_device(f)
ey_gpu = cuda.to_device(f)
ez_gpu = cuda.to_device(f)
hx_gpu = cuda.to_device(f)
hy_gpu = cuda.to_device(f)
hz_gpu = cuda.to_device(f)
cex_gpu = cuda.to_device(set_c(cf, (None, -1, -1)))
cey_gpu = cuda.to_device(set_c(cf, (-1, None, -1)))
cez_gpu = cuda.to_device(set_c(cf, (-1, -1, None)))
chx_gpu = cuda.to_device(set_c(cf, (None, 0, 0)))
chy_gpu = cuda.to_device(set_c(cf, (0, None, 0)))
chz_gpu = cuda.to_device(set_c(cf, (0, 0, None)))
# prepare kernels
from pycuda.compiler import SourceModule
if __name__ == '__main__':
digit_level = 25 #################################可変パラメーター############################# 最大25
digitN = 1 << digit_level
gsz, lsz, gsz2, lsz2 = Creategszlsz() #GPUカーネルのgridサイズblockサイズ計算
print("A,Bの要素数=", digitN)
FMT_P0 = FMTClass(MODP=469762049,
MODP_WnSqrt=CreateWnSqrt(60733, 469762049, 26))
FMT_P1 = FMTClass(MODP=1811939329,
MODP_WnSqrt=CreateWnSqrt(59189, 1811939329, 26))
FMT_P2 = FMTClass(MODP=2013265921,
MODP_WnSqrt=CreateWnSqrt(52278, 2013265921, 27))
host_E = np.zeros(digitN * 2).astype(np.uint32) #結果格納用
E = drv.to_device(host_E) # gpuメモリ確保。かならず0に初期化しておかないといけない
print("初期値生成")
host_A, host_B = InitializeAB()
A0 = drv.to_device(host_A) # gpuメモリ確保&転送
B0 = drv.to_device(host_B) # gpuメモリ確保&転送
A1 = drv.to_device(host_A) # gpuメモリ確保&転送
B1 = drv.to_device(host_B) # gpuメモリ確保&転送
A2 = drv.to_device(host_A) # gpuメモリ確保&転送
B2 = drv.to_device(host_B) # gpuメモリ確保&転送
print("GPU計算開始")
stime = time.time()
E0 = FMT_P0.Convolution(A0, B0)
E1 = FMT_P1.Convolution(A1, B1)
E2 = FMT_P2.Convolution(A2, B2)
def go_sort(count, stream=None):
grids = count / 8192
keys = np.fromstring(np.random.bytes(count * 2), dtype=np.uint16)
#keys = np.arange(count, dtype=np.uint16)
#np.random.shuffle(keys)
mkeys = np.reshape(keys, (grids, 8192))
vals = np.arange(count, dtype=np.uint32)
dkeys = cuda.to_device(keys)
dvals = cuda.to_device(vals)
print 'Done seeding'
dpfxs = cuda.mem_alloc(grids * 256 * 4)
doffsets = cuda.mem_alloc(count * 2)
launch('prefix_scan_8_0',
doffsets,
dpfxs,
dkeys,
block=(512, 1, 1),
grid=(grids, 1),
stream=stream,
l1=1)
dsplit = cuda.mem_alloc(grids * 256 * 4)
launch('better_split',
dsplit,
dpfxs,
block=(32, 1, 1),
grid=(grids / 32, 1),
stream=stream)
# This stage will be rejiggered along with the split
launch('prefix_sum',
dpfxs,
np.int32(grids * 256),
block=(256, 1, 1),
grid=(1, 1),
stream=stream,
l1=1)
launch('convert_offsets',
doffsets,
dsplit,
dkeys,
i32(0),
block=(1024, 1, 1),
grid=(grids, 1),
stream=stream)
if not stream:
offsets = cuda.from_device(doffsets, (grids, 8192), np.uint16)
split = cuda.from_device(dsplit, (grids, 256), np.uint32)
pfxs = cuda.from_device(dpfxs, (grids, 256), np.uint32)
tkeys = py_radix_sort_maybe(mkeys, offsets, pfxs, split, 0)
#print frle(tkeys & 0xff)
d_skeys = cuda.mem_alloc(count * 2)
d_svals = cuda.mem_alloc(count * 4)
if not stream:
cuda.memset_d32(d_skeys, 0, count / 2)
cuda.memset_d32(d_svals, 0xffffffff, count)
launch('radix_sort_maybe',
d_skeys,
d_svals,
dkeys,
dvals,
doffsets,
dpfxs,
dsplit,
i32(0),
block=(1024, 1, 1),
grid=(grids, 1),
stream=stream,
l1=1)
if not stream:
skeys = cuda.from_device_like(d_skeys, keys)
svals = cuda.from_device_like(d_svals, vals)
# Test integrity of sort (keys and values kept together):
# skeys[i] = keys[svals[i]] for all i
print 'Integrity: ',
if np.all(svals < len(keys)) and np.all(skeys == keys[svals]):
print 'pass'
else:
print 'FAIL'
dkeys, d_skeys = d_skeys, dkeys
dvals, d_svals = d_svals, dvals
if not stream:
cuda.memset_d32(d_skeys, 0, count / 2)
cuda.memset_d32(d_svals, 0xffffffff, count)
launch('prefix_scan_8_8',
doffsets,
dpfxs,
dkeys,
block=(512, 1, 1),
grid=(grids, 1),
stream=stream,
l1=1)
launch('better_split',
dsplit,
dpfxs,
block=(32, 1, 1),
grid=(grids / 32, 1),
stream=stream)
launch('prefix_sum',
dpfxs,
np.int32(grids * 256),
block=(256, 1, 1),
grid=(1, 1),
stream=stream,
l1=1)
if not stream:
pre_offsets = cuda.from_device(doffsets, (grids, 8192), np.uint16)
launch('convert_offsets',
doffsets,
dsplit,
dkeys,
i32(8),
block=(1024, 1, 1),
grid=(grids, 1),
stream=stream)
if not stream:
offsets = cuda.from_device(doffsets, (grids, 8192), np.uint16)
split = cuda.from_device(dsplit, (grids, 256), np.uint32)
pfxs = cuda.from_device(dpfxs, (grids, 256), np.uint32)
tkeys = np.reshape(tkeys, (grids, 8192))
new_offs = py_convert_offsets(pre_offsets, split, tkeys, 8)
print np.nonzero(new_offs != offsets)
fkeys = py_radix_sort_maybe(tkeys, new_offs, pfxs, split, 8)
#print frle(fkeys)
launch('radix_sort_maybe',
d_skeys,
d_svals,
dkeys,
dvals,
doffsets,
dpfxs,
dsplit,
i32(8),
block=(1024, 1, 1),
grid=(grids, 1),
stream=stream,
l1=1)
if not stream:
#print cuda.from_device(doffsets, (4, 8192), np.uint16)
#print cuda.from_device(dkeys, (4, 8192), np.uint16)
#print cuda.from_device(d_skeys, (4, 8192), np.uint16)
skeys = cuda.from_device_like(d_skeys, keys)
svals = cuda.from_device_like(d_svals, vals)
print 'Integrity: ',
if np.all(svals < len(keys)) and np.all(skeys == keys[svals]):
print 'pass'
else:
print 'FAIL'
sorted_keys = np.sort(keys)
# Test that ordering is correct. (Note that we don't need 100%
# correctness, so this test should be made "soft".)
print 'Order: ', 'pass' if np.all(skeys == sorted_keys) else 'FAIL'
if __name__ == '__main__':
nx, ny, nz = 240, 256, 256
tpb = 256
bpg = (nx * ny * nz) / tpb
print 'dim (%d, %d, %d)' % (nx, ny, nz)
total_bytes = nx * ny * nz * 4 * 9
if total_bytes / (1024**3) == 0:
print 'mem %d MB' % (total_bytes / (1024**2))
else:
print 'mem %1.2f GB' % (float(total_bytes) / (1024**3))
# memory allocate
f = np.zeros((nx, ny, nz), 'f', order='F')
ex_gpu = cuda.to_device(f)
ey_gpu = cuda.to_device(f)
ez_gpu = cuda.to_device(f)
hx_gpu = cuda.to_device(f)
hy_gpu = cuda.to_device(f)
hz_gpu = cuda.to_device(f)
cex_gpu = cuda.to_device(set_c(f, 'yz'))
cey_gpu = cuda.to_device(set_c(f, 'zx'))
cez_gpu = cuda.to_device(set_c(f, 'xy'))
# prepare kernels
from pycuda.compiler import SourceModule
mod = SourceModule(
kernels.replace('TPB', str(tpb)).replace('nxy', str(nx * ny)).replace(
'nx', str(nx)).replace('ny', str(ny)).replace('nz', str(nz)))
def test_two_directions(self):
IMAGE_DIR = "Backpack-perfect"
im1 = cv2.imread(os.path.join("../data", IMAGE_DIR, "im1.png"))
im2 = cv2.imread(os.path.join("../data", IMAGE_DIR, "im0.png"))
stereo = SemiGlobalMatching(im1,
im2,
os.path.join("../data", IMAGE_DIR,
"calib.txt"),
window_size=3,
resize=(640, 480))
params = {
"p1": 5,
"p2": 90000,
"census_kernel_size": 7,
"reversed": True
}
stereo.set_params(params)
stereo.params['ndisp'] = 50
t1 = time()
assert stereo.p1 is not None, "parameters have not been set"
t1 = time()
cim1 = stereo.census_transform(stereo.im1)
cim2 = stereo.census_transform(stereo.im2)
#print(f"census transform time {time() - t1}")
if not stereo.reversed:
D = range(int(stereo.params['ndisp']))
else:
D = reversed(range(int(-stereo.params['ndisp']), 1))
cost_images = stereo.compute_disparity_img(cim1, cim2, D)
cost_images = np.float32(cost_images)
m, n, D = cost_images.shape
# direction == (1,0)
stereo.directions = [(1, 0), (-1, 0), (1, 1), (-1, 1), (1, -1),
(-1, -1)]
#stereo.directions = [(0,1)]
t1 = time()
L = stereo.aggregate_cost(cost_images)
print("python aggregate cost %f" % (time() - t1))
L = L.transpose((2, 0, 1))
cost_images = cost_images.transpose((2, 0, 1))
cost_images = np.ascontiguousarray(cost_images, dtype=np.float32)
d, rows, cols = cost_images.shape
d_step = 1
rows = np.int32(rows)
cols = np.int32(cols)
compiler_constants = {
'D_STEP': d_step,
'D': d,
'ARR_SIZE': math.floor(d / d_step),
'P1': 5,
'P2': 90000,
'SHMEM_SIZE': 64
}
build_options = [format_compiler_constants(compiler_constants)]
mod = SourceModule(open("../lib/sgbm_helper.cu").read(),
options=build_options)
shmem_size = 16
vertical_blocks = int(math.ceil(rows / shmem_size))
#r_aggregate = mod.get_function('r_aggregate')
vertical_aggregate_down = mod.get_function('vertical_aggregate_down')
vertical_aggregate_up = mod.get_function('vertical_aggregate_up')
diagonal_br_tl_aggregate = mod.get_function('diagonal_br_tl_aggregate')
diagonal_tl_br_aggregate = mod.get_function('diagonal_tl_br_aggregate')
diagonal_tr_bl_aggregate = mod.get_function('diagonal_tr_bl_aggregate')
diagonal_bl_tr_aggregate = mod.get_function('diagonal_bl_tr_aggregate')
#l_aggregate = mod.get_function('l_aggregate')
t1 = time()
cost_images_ptr = drv.to_device(cost_images)
dp_ptr = drv.mem_alloc(cost_images.nbytes)
vertical_aggregate_down(dp_ptr,
cost_images_ptr,
rows,
cols,
block=(256, 1, 1),
grid=(1, 1))
vertical_aggregate_up(dp_ptr,
cost_images_ptr,
rows,
cols,
block=(256, 1, 1),
grid=(1, 1))
#r_aggregate(dp_ptr, cost_images_ptr, rows, cols, block = (shmem_size, shmem_size, 1), grid = (1, vertical_blocks))
#l_aggregate(dp_ptr, cost_images_ptr, rows, cols, block = (shmem_size, shmem_size, 1), grid = (1, vertical_blocks))
diagonal_tl_br_aggregate(dp_ptr,
cost_images_ptr,
rows,
cols,
block=(256, 1, 1),
grid=(1, 1))
diagonal_bl_tr_aggregate(dp_ptr,
cost_images_ptr,
rows,
cols,
block=(256, 1, 1),
grid=(1, 1))
diagonal_tr_bl_aggregate(dp_ptr,
cost_images_ptr,
rows,
cols,
block=(256, 1, 1),
grid=(1, 1))
diagonal_br_tl_aggregate(dp_ptr,
cost_images_ptr,
rows,
cols,
block=(256, 1, 1),
grid=(1, 1))
print("cuda aggregate cost %f" % (time() - t1))
drv.stop_profiler()
agg_image = drv.from_device(dp_ptr,
cost_images.shape,
dtype=np.float32)
s1 = np.sum(np.float64(L))
s2 = np.sum(np.float64(agg_image))
print("L sum: %f" % s1)
print("out sum: %f" % s2)
self.assertTrue(np.all(np.isclose(agg_image, L)))
"""
kernels = """"""
import numpy as np
import sys
import pycuda.driver as cuda
import pycuda.autoinit
if __name__ == '__main__':
size = 100 # MBtye
nx = size * (1024**2) / 4
nloop = 30
# memory allocate
fw_gpu = cuda.to_device(np.zeros(nx, 'f'))
f = np.random.randn(nx).astype(np.float32)
fr_gpu_list = []
for i in range(nloop):
fr_gpu_list.append(cuda.to_device(f))
# prepare kernels
from pycuda.compiler import SourceModule
args = 'float *fr00'
body = 'f = 0.543*fr00[idx];\n\t__syncthreads();\n'
for i in range(nloop):
if (i > 0):
args += ', float *fr%.2d' % i
body += '\tf += %1.3f*fr%.2d[idx];\n\t__syncthreads();\n' % (
np.random.ranf(), i)
kernels += kernel_template.replace('NAME', 'func%.2d' % i).replace(
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np
from magma_wrapper import magma_spotrf_gpu_wrap, magma_get_spotrf_nb_wrap
n = 1000
# Create matrix to be factored
A = np.eye(n, dtype='float32') + np.ones((n,n))*.1
A_gpu = cuda.to_device(A)
# Allocate pagelocked work array
nwork = magma_get_spotrf_nb_wrap(n)
work_gpu = cuda.pagelocked_empty((nb,nb), dtype='float32')
# Do Cholesky factorization
info = magma_spotrf_gpu_wrap('U', n, A_gpu, n, work_gpu)
# Copy back the Cholesy factor and check for correctness
L = cuda.from_device(A_gpu, (n,n), 'float32')
print np.abs(np.dot(L,L.T)-A).max()
gsz = 1 << (digit_level - 1) # gpu global_work_size
lsz = min(gsz, 256) # gpu local_work_size
gsz2 = digitN
lsz2 = min(gsz2, 256)
#print("A,Bの要素数=",digitN)
FMT_P0 = FMTClass(MODP=469762049,
MODP_WnSqrt=CreateWnSqrt(60733, 469762049, 26))
FMT_P1 = FMTClass(MODP=1811939329,
MODP_WnSqrt=CreateWnSqrt(59189, 1811939329, 26))
FMT_P2 = FMTClass(MODP=2013265921,
MODP_WnSqrt=CreateWnSqrt(52278, 2013265921, 27))
host_E = np.zeros(digitN * 2).astype(np.uint32) #結果格納用
E = drv.to_device(host_E) # gpuメモリ確保。かならず0に初期化しておかないといけない
#print("初期値生成")
host_A, host_B = InitializeAB()
A0 = drv.to_device(host_A % np.uint32(FMT_P0.MODP)) # gpuメモリ確保&転送
B0 = drv.to_device(host_B % np.uint32(FMT_P0.MODP)) # gpuメモリ確保&転送
A1 = drv.to_device(host_A % np.uint32(FMT_P1.MODP)) # gpuメモリ確保&転送
B1 = drv.to_device(host_B % np.uint32(FMT_P1.MODP)) # gpuメモリ確保&転送
A2 = drv.to_device(host_A % np.uint32(FMT_P2.MODP)) # gpuメモリ確保&転送
B2 = drv.to_device(host_B % np.uint32(FMT_P2.MODP)) # gpuメモリ確保&転送
#print("GPU計算開始")
stime = time.time()
E0 = FMT_P0.Convolution(A0, B0)
E1 = FMT_P1.Convolution(A1, B1)
E2 = FMT_P2.Convolution(A2, B2)
def project_Kt(XKt, LorY, surfSrc, surfTar, Kt_diag, self, param, ind0, timing,
kernel):
"""
It computes the adjoint double layer potential.
Arguments
----------
XKt : array, input for the adjoint double layer potential.
LorY : int, Laplace (1) or Yukawa (2).
surfSrc: class, source surface, the one that contains the gauss points.
surfTar: class, target surface, the one that contains the collocation points.
Kt_diag: array, diagonal elements of the adjoint double layer integral
operator.
self : int, position in the surface array of the source surface.
param : class, parameters related to the surface.
ind0 : array, it contains the indices related to the treecode computation.
timing : class, it contains timing information for different parts of the
code.
kernel : pycuda source module.
Returns
--------
Kt_lyr: array, adjoint double layer potential.
"""
if param.GPU == 1:
tic = cuda.Event()
toc = cuda.Event()
else:
tic = Event()
toc = Event()
REAL = param.REAL
Ns = len(surfSrc.triangle)
tic.record()
K = param.K
w = getWeights(K)
X_Kt = numpy.zeros(Ns * K)
X_Ktc = numpy.zeros(Ns * K)
NsK = numpy.arange(Ns * K)
X_Kt[:] = XKt[NsK // K] * w[NsK % K] * surfSrc.area[NsK // K]
X_Ktc[:] = XKt[NsK // K]
toc.record()
toc.synchronize()
timing.time_mass += tic.time_till(toc) * 1e-3
tic.record()
C = 0
X_aux = numpy.zeros(Ns * K)
getMultipole(surfSrc.tree, C, surfSrc.xj, surfSrc.yj, surfSrc.zj, X_Kt,
X_aux, X_aux, X_aux, ind0, param.P, param.NCRIT)
toc.record()
toc.synchronize()
timing.time_P2M += tic.time_till(toc) * 1e-3
tic.record()
for C in reversed(range(1, len(surfSrc.tree))):
PC = surfSrc.tree[C].parent
upwardSweep(surfSrc.tree, C, PC, param.P, ind0.II, ind0.JJ, ind0.KK,
ind0.index, ind0.combII, ind0.combJJ, ind0.combKK,
ind0.IImii, ind0.JJmjj, ind0.KKmkk, ind0.index_small,
ind0.index_ptr)
toc.record()
toc.synchronize()
timing.time_M2M += tic.time_till(toc) * 1e-3
tic.record()
X_Kt = X_Kt[surfSrc.sortSource]
X_Ktc = X_Ktc[surfSrc.sortSource]
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
param.Nround = len(surfTar.twig) * param.NCRIT
Ktx_aux = numpy.zeros(param.Nround)
Kty_aux = numpy.zeros(param.Nround)
Ktz_aux = numpy.zeros(param.Nround)
### CPU code
if param.GPU == 0:
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
Ktx_aux, Kty_aux, Ktz_aux = M2PKt_sort(surfSrc, surfTar, Ktx_aux,
Kty_aux, Ktz_aux, self,
ind0.index_large, param,
LorY, timing)
Ktx_aux, Kty_aux, Ktz_aux = P2PKt_sort(surfSrc, surfTar, X_Kt, X_Ktc,
Ktx_aux, Kty_aux, Ktz_aux, self,
LorY, w, param, timing)
### GPU code
elif param.GPU == 1:
Ktx_gpu = cuda.to_device(Ktx_aux.astype(REAL))
Kty_gpu = cuda.to_device(Kty_aux.astype(REAL))
Ktz_gpu = cuda.to_device(Ktz_aux.astype(REAL))
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
Ktx_gpu, Kty_gpu, Ktz_gpu = M2PKt_gpu(surfSrc, surfTar, Ktx_gpu,
Kty_gpu, Ktz_gpu, self, ind0,
param, LorY, timing, kernel)
Ktx_gpu, Kty_gpu, Ktz_gpu = P2PKt_gpu(surfSrc, surfTar, X_Kt, X_Ktc,
Ktx_gpu, Kty_gpu, Ktz_gpu, self,
LorY, w, param, timing, kernel)
tic.record()
Ktx_aux = cuda.from_device(Ktx_gpu, len(Ktx_aux), dtype=REAL)
Kty_aux = cuda.from_device(Kty_gpu, len(Kty_aux), dtype=REAL)
Ktz_aux = cuda.from_device(Ktz_gpu, len(Ktz_aux), dtype=REAL)
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc) * 1e-3
tic.record()
Kt_lyr = (Ktx_aux[surfTar.unsort] * surfTar.normal[:, 0] +
Kty_aux[surfTar.unsort] * surfTar.normal[:, 1] +
Ktz_aux[surfTar.unsort] * surfTar.normal[:, 2])
if abs(Kt_diag) > 1e-12: # if same surface
Kt_lyr += Kt_diag * XKt
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
return Kt_lyr
def project(XK, XV, LorY, surfSrc, surfTar, K_diag, V_diag, IorE, self, param,
ind0, timing, kernel):
"""
It computes the single and double layer potentials.
Arguments
----------
XK : array, input for the double layer potential.
XV : array, input for the single layer potential.
LorY : int, Laplace (1) or Yukawa (2).
surfSrc: class, source surface, the one that contains the gauss points.
surfTar: class, target surface, the one that contains the collocation
points.
K_diag : array, diagonal elements of the double layer integral operator.
V_diag : array, diagonal elements of the single layer integral operator.
IorE : int, internal (1) or external (2).
self : int, position in the surface array of the source surface.
param : class, parameters related to the surface.
ind0 : array, it contains the indices related to the treecode computation.
timing : class, it contains timing information for different parts of the
code.
kernel : pycuda source module.
Returns
--------
K_lyr : array, double layer potential.
V_lyr : array, single layer potential.
"""
if param.GPU == 1:
tic = cuda.Event()
toc = cuda.Event()
else:
tic = Event()
toc = Event()
REAL = param.REAL
Ns = len(surfSrc.triangle)
L = numpy.sqrt(2 * surfSrc.area) # Representative length
tic.record()
K = param.K
w = getWeights(K)
X_V = numpy.zeros(Ns * K)
X_Kx = numpy.zeros(Ns * K)
X_Ky = numpy.zeros(Ns * K)
X_Kz = numpy.zeros(Ns * K)
X_Kc = numpy.zeros(Ns * K)
X_Vc = numpy.zeros(Ns * K)
NsK = numpy.arange(Ns * K)
X_V[:] = XV[NsK // K] * w[NsK % K] * surfSrc.area[NsK // K]
X_Kx[:] = XK[NsK // K] * w[NsK % K] * surfSrc.area[
NsK // K] * surfSrc.normal[NsK // K, 0]
X_Ky[:] = XK[NsK // K] * w[NsK % K] * surfSrc.area[
NsK // K] * surfSrc.normal[NsK // K, 1]
X_Kz[:] = XK[NsK // K] * w[NsK % K] * surfSrc.area[
NsK // K] * surfSrc.normal[NsK // K, 2]
X_Kc[:] = XK[NsK // K]
X_Vc[:] = XV[NsK // K]
toc.record()
toc.synchronize()
timing.time_mass += tic.time_till(toc) * 1e-3
tic.record()
C = 0
getMultipole(surfSrc.tree, C, surfSrc.xj, surfSrc.yj, surfSrc.zj, X_V,
X_Kx, X_Ky, X_Kz, ind0, param.P, param.NCRIT)
toc.record()
toc.synchronize()
timing.time_P2M += tic.time_till(toc) * 1e-3
tic.record()
for C in reversed(range(1, len(surfSrc.tree))):
PC = surfSrc.tree[C].parent
upwardSweep(surfSrc.tree, C, PC, param.P, ind0.II, ind0.JJ, ind0.KK,
ind0.index, ind0.combII, ind0.combJJ, ind0.combKK,
ind0.IImii, ind0.JJmjj, ind0.KKmkk, ind0.index_small,
ind0.index_ptr)
toc.record()
toc.synchronize()
timing.time_M2M += tic.time_till(toc) * 1e-3
tic.record()
X_V = X_V[surfSrc.sortSource]
X_Kx = X_Kx[surfSrc.sortSource]
X_Ky = X_Ky[surfSrc.sortSource]
X_Kz = X_Kz[surfSrc.sortSource]
X_Kc = X_Kc[surfSrc.sortSource]
X_Vc = X_Vc[surfSrc.sortSource]
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
param.Nround = len(surfTar.twig) * param.NCRIT
K_aux = numpy.zeros(param.Nround)
V_aux = numpy.zeros(param.Nround)
### CPU code
if param.GPU == 0:
K_aux, V_aux = M2P_sort(surfSrc, surfTar, K_aux, V_aux, self,
ind0.index_large, param, LorY, timing)
K_aux, V_aux = P2P_sort(surfSrc, surfTar, X_V, X_Kx, X_Ky, X_Kz, X_Kc,
X_Vc, K_aux, V_aux, self, LorY, K_diag, V_diag,
IorE, L, w, param, timing)
### GPU code
elif param.GPU == 1:
K_gpu = cuda.to_device(K_aux.astype(REAL))
V_gpu = cuda.to_device(V_aux.astype(REAL))
if surfTar.offsetMlt[self, len(surfTar.twig)] > 0:
K_gpu, V_gpu = M2P_gpu(surfSrc, surfTar, K_gpu, V_gpu, self, ind0,
param, LorY, timing, kernel)
K_gpu, V_gpu = P2P_gpu(surfSrc, surfTar, X_V, X_Kx, X_Ky, X_Kz, X_Kc,
X_Vc, K_gpu, V_gpu, self, LorY, K_diag, IorE, L,
w, param, timing, kernel)
tic.record()
K_aux = cuda.from_device(K_gpu, len(K_aux), dtype=REAL)
V_aux = cuda.from_device(V_gpu, len(V_aux), dtype=REAL)
toc.record()
toc.synchronize()
timing.time_trans += tic.time_till(toc) * 1e-3
tic.record()
K_lyr = K_aux[surfTar.unsort]
V_lyr = V_aux[surfTar.unsort]
toc.record()
toc.synchronize()
timing.time_sort += tic.time_till(toc) * 1e-3
return K_lyr, V_lyr
