def _concat(cls, objs):
head = objs[0]
for o in objs:
if not o.is_type_equivalent(head):
raise ValueError("All series must be of same type")
newsize = sum(map(len, objs))
# Concatenate data
mem = cuda.device_array(shape=newsize, dtype=head.data.dtype)
data = Buffer.from_empty(mem)
for o in objs:
data.extend(o.data.to_gpu_array())
# Concatenate mask if present
if all(o.has_null_mask for o in objs):
# FIXME: Inefficient
mem = cuda.device_array(shape=newsize, dtype=np.bool)
mask = Buffer.from_empty(mem)
null_count = 0
for o in objs:
mask.extend(o._get_mask_as_series().to_gpu_array())
null_count += o._null_count
mask = Buffer(utils.boolmask_to_bitmask(mask.to_array()))
else:
mask = None
null_count = 0
col = head.replace(data=data, mask=mask, null_count=null_count)
return col
def test_profiling(self):
with cuda._profiling():
a = cuda.device_array(10)
del a
with cuda._profiling():
a = cuda.device_array(100)
del a
def getGraphFromEdges_gpu(dest, weight, fe, od, edges, n_edges = None,
MAX_TPB = 512, stream = None):
"""
All input (except MAX_TPB and stream) are device arrays.
edges : array with the IDs of the edges that will be part of the new graph
n_edges : array of 1 element with the number of valid edges in the edges array;
if n_edges < size of edges, the last elements of the edges array are
not considered
"""
# check if number of valid edges was received
if n_edges is None:
edges_size = edges.size
n_edges = cuda.to_device(np.array([edges_size], dtype = np.int32))
else:
edges_size = int(n_edges.getitem(0))
# check if a stream was received, if not create one
if stream is None:
myStream = cuda.stream()
else:
myStream = stream
new_n_edges = edges_size * 2
# allocate memory for new graph
ndest = cuda.device_array(new_n_edges, dtype = dest.dtype,
stream = myStream)
nweight = cuda.device_array(new_n_edges, dtype = weight.dtype,
stream = myStream)
nfe = cuda.device_array_like(fe, stream = myStream)
nod = cuda.device_array_like(od, stream = myStream)
# fill new outdegree with zeros
vertexGrid = compute_cuda_grid_dim(nod.size, MAX_TPB)
memSet[vertexGrid, MAX_TPB, myStream](nod, 0)
# count all edges of new array and who they belong to
edgeGrid = compute_cuda_grid_dim(edges_size, MAX_TPB)
countEdges[edgeGrid, MAX_TPB, myStream](edges, n_edges, dest, fe, od, nod)
# get new first_edge array from new outdegree
nfe.copy_to_device(nod, stream=myStream)
ex_prefix_sum_gpu(nfe, MAX_TPB = MAX_TPB, stream = myStream)
# copy new first_edge to top_edge to serve as pointer in adding edges
top_edge = cuda.device_array_like(nfe, stream = myStream)
top_edge.copy_to_device(nfe, stream = myStream)
addEdges[edgeGrid, MAX_TPB, myStream](edges, n_edges, dest, weight, fe, od,
top_edge, ndest, nweight)
del top_edge
#del dest, weight, fe, od
return ndest, nweight, nfe, nod
def arange(start, stop=None, step=1, dtype=np.int64):
if stop is None:
start, stop = 0, start
size = (stop - start + (step - 1)) // step
out = cuda.device_array(size, dtype=dtype)
gpu_arange.forall(size)(start, size, step, out)
return out
def gather(data, index, out=None):
"""Perform ``out = data[index]`` on the GPU
"""
if out is None:
out = cuda.device_array(shape=index.size, dtype=data.dtype)
gpu_gather.forall(index.size)(data, index, out)
return out
def monte_carlo_pricer(paths, dt, interest, volatility):
n = paths.shape[0]
mm = MM(shape=n, dtype=np.double, prealloc=5)
blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK
gridsz = int(math.ceil(float(n) / blksz))
stream = cuda.stream()
prng = PRNG(PRNG.MRG32K3A, stream=stream)
# Allocate device side array
d_normdist = cuda.device_array(n, dtype=np.double, stream=stream)
c0 = interest - 0.5 * volatility ** 2
c1 = volatility * math.sqrt(dt)
d_last = cuda.to_device(paths[:, 0], to=mm.get())
for j in range(1, paths.shape[1]):
prng.normal(d_normdist, mean=0, sigma=1)
d_paths = cuda.to_device(paths[:, j], stream=stream, to=mm.get())
step(d_last, dt, c0, c1, d_normdist, out=d_paths, stream=stream)
d_paths.copy_to_host(paths[:, j], stream=stream)
mm.free(d_last)
d_last = d_paths
stream.synchronize()
def test_gufunc_stream(self):
#cuda.driver.flush_pending_free()
matrix_ct = 1001 # an odd number to test thread/block division in CUDA
A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2,
4)
B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4,
5)
ts = time()
stream = cuda.stream()
dA = cuda.to_device(A, stream)
dB = cuda.to_device(B, stream)
dC = cuda.device_array(shape=(1001, 2, 5), dtype=A.dtype, stream=stream)
dC = gufunc(dA, dB, out=dC, stream=stream)
C = dC.copy_to_host(stream=stream)
stream.synchronize()
tcuda = time() - ts
ts = time()
Gold = ut.matrix_multiply(A, B)
tcpu = time() - ts
stream_speedups.append(tcpu / tcuda)
self.assertTrue(np.allclose(C, Gold))
def prescan_test():
a = np.arange(2048).astype(np.int32)
reference = np.empty_like(a)
ref_sum = scan.exprefixsumNumba(a, reference)
a1 = np.arange(1024).astype(np.int32)
a2 = np.arange(1024, 2048).astype(np.int32)
ref1 = np.empty_like(a1)
ref2 = np.empty_like(a2)
ref_sum1 = scan.exprefixsumNumba(a1, ref1)
ref_sum2 = scan.exprefixsumNumba(a2, ref2)
dAux = cuda.device_array(2, dtype = np.int32)
dA = cuda.to_device(a)
sm_size = 1024 * a.dtype.itemsize
scan.prescan[2, 512, 0, sm_size](dA, dAux)
aux = dAux.copy_to_host()
a_gpu = dA.copy_to_host()
print "finish"
def sum_parts(data):
"""
Driver for ``gpu_single_block_sum`` kernel
"""
arr = np.asarray(data, dtype=np.float32)
out = cuda.device_array(1, dtype=np.float32)
gpu_single_block_sum[1, gpu_block_sum_max_blockdim](arr, out)
return out.copy_to_host()[0]
def astype(ary, dtype):
if ary.dtype == np.dtype(dtype):
return ary
else:
out = cuda.device_array(shape=ary.shape, dtype=dtype)
configured = gpu_copy.forall(out.size)
configured(ary, out)
return out
def apply_reduce(fn, inp):
# allocate output+temp array
outsz = libgdf.gdf_reduce_optimal_output_size()
out = cuda.device_array(outsz, dtype=inp.dtype)
# call reduction
fn(inp.cffi_view, unwrap_devary(out), outsz)
# return 1st element
return out[0]
def __init__(self, shape, dtype, prealloc):
self.device = cuda.get_current_device()
self.freelist = deque()
self.events = {}
for i in range(prealloc):
gpumem = cuda.device_array(shape=shape, dtype=dtype)
self.freelist.append(gpumem)
self.events[gpumem] = cuda.event(timing=False)
def monte_carlo_pricer(paths, dt, interest, volatility):
n = paths.shape[0]
num_streams = 2
part_width = int(math.ceil(float(n) / num_streams))
partitions = [(0, part_width)]
for i in range(1, num_streams):
begin, end = partitions[i - 1]
begin, end = end, min(end + (end - begin), n)
partitions.append((begin, end))
partlens = [end - begin for begin, end in partitions]
mm = MM(shape=part_width, dtype=np.double, prealloc=10 * num_streams)
device = cuda.get_current_device()
blksz = device.MAX_THREADS_PER_BLOCK
gridszlist = [int(math.ceil(float(partlen) / blksz))
for partlen in partlens]
strmlist = [cuda.stream() for _ in range(num_streams)]
prnglist = [PRNG(PRNG.MRG32K3A, stream=strm)
for strm in strmlist]
# Allocate device side array
d_normlist = [cuda.device_array(partlen, dtype=np.double, stream=strm)
for partlen, strm in zip(partlens, strmlist)]
c0 = interest - 0.5 * volatility ** 2
c1 = volatility * math.sqrt(dt)
# Configure the kernel
# Similar to CUDA-C: cu_monte_carlo_pricer<<<gridsz, blksz, 0, stream>>>
steplist = [cu_step[gridsz, blksz, strm]
for gridsz, strm in zip(gridszlist, strmlist)]
d_lastlist = [cuda.to_device(paths[s:e, 0], to=mm.get(stream=strm))
for (s, e), strm in zip(partitions, strmlist)]
for j in range(1, paths.shape[1]):
for prng, d_norm in zip(prnglist, d_normlist):
prng.normal(d_norm, mean=0, sigma=1)
d_pathslist = [cuda.to_device(paths[s:e, j], stream=strm,
to=mm.get(stream=strm))
for (s, e), strm in zip(partitions, strmlist)]
for step, args in zip(steplist, zip(d_lastlist, d_pathslist, d_normlist)):
d_last, d_paths, d_norm = args
step(d_last, d_paths, dt, c0, c1, d_norm)
for d_paths, strm, (s, e) in zip(d_pathslist, strmlist, partitions):
d_paths.copy_to_host(paths[s:e, j], stream=strm)
mm.free(d_last, stream=strm)
d_lastlist = d_pathslist
for strm in strmlist:
strm.synchronize()
def test_stream_bind(self):
stream = cuda.stream()
with stream.auto_synchronize():
arr = cuda.device_array(
(3, 3),
dtype=np.float64,
stream=stream)
self.assertEqual(arr.bind(stream).stream, stream)
self.assertEqual(arr.stream, stream)
def mask_assign_slot(size, mask):
# expand bits into bytes
dtype = (np.int32 if size < 2 ** 31 else np.int64)
expanded_mask = cuda.device_array(size, dtype=dtype)
numtasks = min(64 * 128, expanded_mask.size)
gpu_expand_mask_bits.forall(numtasks)(mask, expanded_mask)
# compute prefixsum
slots = prefixsum(expanded_mask)
sz = int(slots[slots.size - 1])
return slots, sz
def test_device_array_interface(self):
dary = cuda.device_array(shape=100)
devicearray.verify_cuda_ndarray_interface(dary)
ary = np.empty(100)
dary = cuda.to_device(ary)
devicearray.verify_cuda_ndarray_interface(dary)
ary = np.asarray(1.234)
dary = cuda.to_device(ary)
self.assertEquals(dary.ndim, 1)
devicearray.verify_cuda_ndarray_interface(dary)
def test_event_elapsed(self):
N = 32
dary = cuda.device_array(N, dtype=np.double)
evtstart = cuda.event()
evtend = cuda.event()
evtstart.record()
cuda.to_device(np.arange(N), to=dary)
evtend.record()
evtend.wait()
evtend.synchronize()
print(evtstart.elapsed_time(evtend))
def find_segments(arr):
"""Find beginning indices of runs of equal values.
Returns
-------
starting_indices : device array
The starting indices of start of segments.
Total segment count will be equal to the length of this.
"""
from . import _gdf
# Compute diffs of consecutive elements
markers = cuda.device_array(arr.size, dtype=np.int32)
gpu_mark_segment_begins.forall(markers.size)(arr, markers)
# Compute index of marked locations
slots = prefixsum(markers)
ct = slots[slots.size - 1]
scanned = slots[:-1]
# Compact segments
begins = cuda.device_array(shape=int(ct), dtype=np.intp)
gpu_scatter_segment_begins.forall(markers.size)(markers, scanned, begins)
return begins
def run_gather(self, arr, diffs):
h_out_idx = np.zeros(1, dtype=np.intp)
out_queue = cuda.device_array(shape=self._maxk, dtype=arr.dtype)
gpu_insert_if_masked.forall(arr.size)(arr, diffs, h_out_idx, out_queue)
qsz = h_out_idx[0]
if self._maxk >= 0:
if qsz > self._maxk:
msg = 'too many unique value: unique values ({}) > k ({})'
raise ValueError(msg.format(qsz, self._maxk))
end = min(qsz, self._maxk)
else:
raise NotImplementedError('k is unbounded')
vals = out_queue[:end]
return vals
def append(self, other):
"""Append another column
"""
if self.has_null_mask or other.has_null_mask:
raise NotImplementedError("append masked column is not supported")
newsize = len(self) + len(other)
# allocate memory
mem = cuda.device_array(shape=newsize, dtype=self.data.dtype)
newbuf = Buffer.from_empty(mem)
# copy into new memory
for buf in [self.data, other.data]:
newbuf.extend(buf.to_gpu_array())
# return new column
return self.replace(data=newbuf)
def test_event_elapsed_stream(self):
N = 32
stream = cuda.stream()
dary = cuda.device_array(N, dtype=np.double)
evtstart = cuda.event()
evtend = cuda.event()
evtstart.record(stream=stream)
cuda.to_device(np.arange(N), to=dary, stream=stream)
evtend.record(stream=stream)
evtend.wait(stream=stream)
evtend.synchronize()
# Exercise the code path
evtstart.elapsed_time(evtend)
def run(self, arr, k):
if k >= MAX_FAST_UNIQUE_K:
raise NotImplementedError('k >= {}'.format(MAX_FAST_UNIQUE_K))
# setup mem
outsz_ptr = cuda.device_array(shape=1, dtype=np.intp)
out = cuda.device_array_like(arr)
# kernel
self._kernel[1, 64](arr, k, out, outsz_ptr)
# copy to host
unique_ct = outsz_ptr.copy_to_host()[0]
if unique_ct < 0:
raise ValueError('too many unique value (hint: increase k)')
else:
hout = out.copy_to_host()
return hout[:unique_ct]
def _getOccupancyCUDA(coords, centers, channelsigmas, trunc=5, device=0, resD=None, asnumpy=True, threadsperblock=256):
#cuda.select_device(device)
if resD is None:
resD = cuda.device_array((centers.shape[0], channelsigmas.shape[1]), dtype=np.float32)
_memsetArray(resD, val=0)
natomblocks = int(np.ceil(coords.shape[0] / threadsperblock))
blockspergrid = (centers.shape[0], natomblocks)
centers = cuda.to_device(centers)
coords = cuda.to_device(coords)
channelsigmas = cuda.to_device(channelsigmas)
_getOccupancyCUDAkernel[blockspergrid, threadsperblock](resD, coords, centers, channelsigmas, trunc * trunc)
if asnumpy:
return resD.copy_to_host()
def test_ufunc_arg(self):
@vectorize(['f8(f8, f8)'], target='cuda')
def vadd(a, b):
return a + b
# Case 1: use custom array as argument
h_arr = np.random.random(10)
arr = MyArray(cuda.to_device(h_arr))
val = 6
out = vadd(arr, val)
np.testing.assert_array_equal(out.copy_to_host(), h_arr + val)
# Case 2: use custom array as return
out = MyArray(cuda.device_array(h_arr.shape))
returned = vadd(h_arr, val, out=out)
np.testing.assert_array_equal(returned.copy_to_host(), h_arr + val)
def test_gufunc_arg(self):
@guvectorize(['(f8, f8, f8[:])'], '(),()->()', target='cuda')
def vadd(inp, val, out):
out[0] = inp + val
# Case 1: use custom array as argument
h_arr = np.random.random(10)
arr = MyArray(cuda.to_device(h_arr))
val = np.float64(7)
out = vadd(arr, val)
np.testing.assert_array_equal(out.copy_to_host(), h_arr + val)
# Case 2: use custom array as return
out = MyArray(cuda.device_array(h_arr.shape))
returned = vadd(h_arr, val, out=out)
np.testing.assert_array_equal(returned.copy_to_host(), h_arr + val)
self.assertEqual(returned.device_ctypes_pointer.value,
out._arr.device_ctypes_pointer.value)
def test_gufunc_stream(self):
@guvectorize([void(float32[:, :], float32[:, :], float32[:, :])],
'(m,n),(n,p)->(m,p)',
target='cuda')
def matmulcore(A, B, C):
m, n = A.shape
n, p = B.shape
for i in range(m):
for j in range(p):
C[i, j] = 0
for k in range(n):
C[i, j] += A[i, k] * B[k, j]
gufunc = matmulcore
gufunc.max_blocksize = 512
#cuda.driver.flush_pending_free()
matrix_ct = 1001 # an odd number to test thread/block division in CUDA
A = np.arange(matrix_ct * 2 * 4, dtype=np.float32).reshape(matrix_ct, 2,
4)
B = np.arange(matrix_ct * 4 * 5, dtype=np.float32).reshape(matrix_ct, 4,
5)
ts = time()
stream = cuda.stream()
dA = cuda.to_device(A, stream)
dB = cuda.to_device(B, stream)
dC = cuda.device_array(shape=(1001, 2, 5), dtype=A.dtype, stream=stream)
dC = gufunc(dA, dB, out=dC, stream=stream)
C = dC.copy_to_host(stream=stream)
stream.synchronize()
tcuda = time() - ts
ts = time()
Gold = ut.matrix_multiply(A, B)
tcpu = time() - ts
stream_speedups.append(tcpu / tcuda)
self.assertTrue(np.allclose(C, Gold))
def cuda_dot2(a, b):
if a.shape[1] != b.shape[0]:
raise ValueError('shape %s does not match to shape %s' %
(str(a.shape), str(b.shape)))
try:
c = np.ones((a.shape[0], b.shape[1]), dtype=a.dtype)
TPB = 16
threadsperblock = (TPB, TPB)
blockspergrid_x = math.ceil(a.shape[0] / threadsperblock[0])
blockspergrid_y = math.ceil(b.shape[1] / threadsperblock[1])
blockspergrid = (blockspergrid_x, blockspergrid_y)
da = cuda.to_device(a)
db = cuda.to_device(b)
dc = cuda.device_array((a.shape[0], b.shape[1]), dtype=a.dtype)
cuda_dot_kernel2[blockspergrid, threadsperblock](da, db, dc)
c = dc.copy_to_host()
return c
except:
cuda.close()
raise
def __init__(self, data, block_size, n_iter, seed=0):
self.data = cuda.to_device(data)
self.n_iter = n_iter
# Parameters for the kernel launch
self.block_size = block_size
self.n_samples = data.shape[0]
self.n_blocks = self.n_samples // block_size
# Allocate an output array on the GPU
self.output = cuda.device_array((n_iter,self.n_blocks,2))
# Create random number generators for each thread
# NOTE: The threads within the same block should generate the same random numbers
rng_states = np.empty(self.n_samples, dtype=xoroshiro128p_dtype)
for i in range(self.n_samples):
init_xoroshiro128p_state(rng_states, i, seed) # Init to a fixed state
for j in range(i//block_size): # Jump forward block_index*2^64 steps
xoroshiro128p_jump(rng_states, i)
self.rng_states = cuda.to_device(rng_states) # Copy it to the GPU
def test_sum(dtype, nelem):
data = gen_rand(dtype, nelem)
d_data = cuda.to_device(data)
d_result = cuda.device_array(libgdf.gdf_reduce_optimal_output_size(),
dtype=d_data.dtype)
col_data = new_column()
gdf_dtype = get_dtype(dtype)
libgdf.gdf_column_view(col_data, unwrap_devary(d_data), ffi.NULL, nelem,
gdf_dtype)
libgdf.gdf_sum_generic(col_data, unwrap_devary(d_result), d_result.size)
got = d_result.copy_to_host()[0]
expect = data.sum()
print('expect:', expect)
print('got:', got)
np.testing.assert_array_almost_equal(expect, got)
def neighbour_list(self):
with cuda.gpus[self.gpu]:
while True:
cu_set_to_int[self.bpg, self.tpb](self.d_nc, 0)
# reset situation while build nlist
self.cu_nlist[self.bpg, self.tpb](
self.system.d_x, self.d_last_x, self.system.d_box,
self.r_cut2, self.clist.d_cell_map, self.clist.d_cell_list,
self.clist.d_cell_counts, self.clist.d_cells, self.d_nl,
self.d_nc, self.d_n_max, self.d_situation)
self.d_n_max.copy_to_host(self.p_n_max)
cuda.synchronize()
# n_max = np.array([120])
if self.p_n_max[0] > self.n_guess:
self.n_guess = self.p_n_max[0]
self.n_guess = self.n_guess + 8 - (self.n_guess & 7)
self.d_nl = cuda.device_array(
(self.system.N, self.n_guess), dtype=np.int32)
else:
break
def cuda_conv(x,K) :
B=x.shape[0]
H=x.shape[1]
W=x.shape[2]
k=K.shape[0]
Cout=K.shape[-1]
A_global_mem = cuda.to_device(x)
B_global_mem = cuda.to_device(K)
C_global_mem = cuda.device_array((B,H-k+1,W-k+1,Cout))
# threadsperblock = (,,1)
# blockspergrid =(1,1,)
threadsperblock = (16,16,4)
blockspergrid =(int(math.ceil((H-k+1) / threadsperblock[0])),
int(math.ceil((W-k+1) / threadsperblock[1])),
int(math.ceil(Cout / threadsperblock[2])))
conv[blockspergrid, threadsperblock](A_global_mem, B_global_mem, C_global_mem)
return C_global_mem.copy_to_host()
def process(self, inputs):
df = inputs['points_df_in']
# DEBUGGING
# try:
# from dask.distributed import get_worker
# worker = get_worker()
# print('worker{} process NODE "{}" worker: {}'.format(
# worker.name, self.uid, worker))
# except (ValueError, ImportError):
# pass
number_of_threads = 16
number_of_blocks = ((len(df) - 1) // number_of_threads) + 1
# Inits device array by setting 0 for each index.
darr = cuda.device_array(len(df))
distance_kernel[(number_of_blocks, ),
(number_of_threads, )](df['x'], df['y'], darr, len(df))
df['distance_numba'] = darr
return {'distance_df': df}
def cuda_operation():
"""Performs Vectorized Operations on GPU"""
x = real_estate_array()
y = real_estate_array()
print("Moving calculations to GPU memory")
x_device = cuda.to_device(x)
y_device = cuda.to_device(y)
out_device = cuda.device_array(
shape=(x_device.shape[0],x_device.shape[1]), dtype=np.float32)
print(x_device)
print(x_device.shape)
print(x_device.dtype)
print("Calculating on GPU")
add_ufunc(x_device,y_device, out=out_device)
out_host = out_device.copy_to_host()
print(f"Calculations from GPU {out_host}")
def update(self):
with cuda.gpus[self.gpu]:
while True:
cu_set_to_int[self.bpg_cell, self.tpb](self.d_cell_counts, 0)
cu_cell_list[self.bpg,
self.tpb](self.system.d_x, self.system.d_box,
self.d_ibox, self.d_cell_list,
self.d_cell_counts, self.d_cells,
self.d_cell_max)
self.d_cell_max.copy_to_host(self.p_cell_max)
cuda.synchronize()
if self.p_cell_max[0] > self.cell_guess:
self.cell_guess = self.p_cell_max[0]
self.cell_guess = self.cell_guess + 8 - (self.cell_guess
& 7)
self.d_cell_list = cuda.device_array(
(self.n_cell, self.cell_guess, self.system.n_dim + 1),
dtype=self.system.dtype)
else:
break
def forward(self, input):
batch_num, _, height, width = input.shape
H_out = int((height + 2 * self.padding - self.weight.shape[2]) /
self.stride) + 1
W_out = int((width + 2 * self.padding - self.weight.shape[3]) /
self.stride) + 1
output_shape = (batch_num, self.out_channels, H_out, W_out)
d_output = cuda.device_array(output_shape, dtype=np.float32)
blockdim = (10, 10, 10)
griddim_0 = ceil(batch_num * self.out_channels / blockdim[0])
griddim_1 = ceil(H_out / blockdim[1])
griddim_2 = ceil(W_out / blockdim[2])
griddim = (griddim_0, griddim_1, griddim_2)
conv_step_gpu[griddim,
blockdim](input, d_output, self.d_weight, self.stride,
self.mod, self.filter_size, self.padding)
return d_output
def prefixsum(vals):
"""Compute the full prefixsum.
Given the input of N. The output size is N + 1.
The first value is always 0. The last value is the sum of *vals*.
"""
from . import _gdf
# Allocate output
slots = cuda.device_array(shape=vals.size + 1,
dtype=vals.dtype)
# Fill 0 to slot[0]
gpu_fill_value[1, 1](slots[:1], 0)
# Compute prefixsum on the mask
_gdf.apply_prefixsum(_gdf.columnview_from_devary(vals),
_gdf.columnview_from_devary(slots[1:]),
inclusive=True)
return slots
def query_execute(df, expr, callenv):
"""Compile & execute the query expression
Note: the expression is compiled and cached for future reuse.
Parameters
----------
df : DataFrame
expr : str
boolean expression
callenv : dict
Contains keys 'locals' and 'globals' which are both dict.
They represent the local and global dictionaries of the caller.
"""
# compile
compiled = query_compile(expr)
kernel = compiled['kernel']
# process env args
envargs = []
envdict = callenv['globals'].copy()
envdict.update(callenv['locals'])
for name in compiled['refnames']:
name = name[len(ENVREF_PREFIX):]
try:
val = envdict[name]
if isinstance(val, dt.datetime):
val = np.datetime64(val)
except KeyError:
msg = '{!r} not defined in the calling environment'
raise NameError(msg.format(name))
else:
envargs.append(val)
# prepare col args
colarrays = [df[col].to_gpu_array() for col in compiled['colnames']]
# allocate output buffer
nrows = len(df)
out = cuda.device_array(nrows, dtype=np.bool_)
# run kernel
args = [out] + colarrays + envargs
kernel.forall(nrows)(*args)
return out
def test_sum_masked(nelem):
dtype = np.float64
data = gen_rand(dtype, nelem)
mask = gen_rand(np.int8, (nelem + 8 - 1) // 8)
d_data = cuda.to_device(data)
d_mask = cuda.to_device(mask)
d_result = cuda.device_array(libgdf.gdf_reduce_optimal_output_size(),
dtype=d_data.dtype)
col_data = new_column()
gdf_dtype = get_dtype(dtype)
libgdf.gdf_column_view(col_data, unwrap_devary(d_data),
unwrap_devary(d_mask), nelem, gdf_dtype)
libgdf.gdf_sum_generic(col_data, unwrap_devary(d_result), d_result.size)
got = d_result.copy_to_host()[0]
boolmask = buffer_as_bits(mask)[:nelem]
expect = data[boolmask].sum()
np.testing.assert_almost_equal(expect, got)
def main(image1, image2):
data_image1 = np.array(image1)
data_image2 = np.array(image2)
print(data_image1.shape, data_image2.shape)
threadsperblock = (16, 16, 4)
blocksper_x = int(math.ceil(data_image1.shape[0] // threadsperblock[0]))
blocksper_y = int(math.ceil(data_image1.shape[1] // threadsperblock[1]))
blocksper_z = int(math.ceil(data_image1.shape[2] // threadsperblock[2]))
blockspergrid = (blocksper_x, blocksper_y, blocksper_z)
input1 = cuda.to_device(data_image1)
input2 = cuda.to_device(data_image2)
output = cuda.device_array(data_image1.shape)
sumImages[blockspergrid, threadsperblock](input1, input2, output)
out = output.copy_to_host()
out = out.astype('uint8')
out = Image.fromarray(out)
out.save("out.png")
def distance_cal(x, y):
Nx = x.size
Ny = y.size
points = [[1, 0], [-1, 0], [0, 1]]
d_x = cuda.to_device(x)
d_y = cuda.to_device(y)
d_points = cuda.to_device(np.array(points))
d_out = cuda.device_array((Nx, Ny))
TPBX = 8
TPBY = 8
gridDims = ((Nx + TPBX - 1) // TPBX, (Ny + TPBY - 1) // TPBY)
blockDims = (TPBX, TPBY)
distance_kernel[gridDims, blockDims](d_x, d_y, d_points, d_out)
return d_out.copy_to_host()
def cu_mat_2d_to_4d(A, dim, from_host=False, to_host=False):
"""
:param A:
:param from_host:
:param to_host:
:return: A:2d matrix, shape: A's shape
"""
if from_host:
A = cuda.to_device(A.astype(np.float32))
assert len(A.shape) == 2 and A.shape[0] == dim[0]
res = cuda.device_array(shape=(dim[0], dim[1], dim[2], dim[3]),
dtype=np.float32)
grid_dim, block_dim = auto_detect(A.shape)
_cu_mat_2d_to_4d[grid_dim, block_dim](A, res)
if to_host:
return res.copy_to_host(), A.shape
else:
return res, A.shape
def WT_as_f(x, k, c, L):
# fund. frequency
k1 = 2.0 * np.pi / L
# Set up kernel
blockSize = (TPB, TPB)
numBlocksX = (x.shape[0] + blockSize[0] - 1) // blockSize[0]
numBlocksK = (k.shape[0] + blockSize[1] - 1) // blockSize[1]
numBlocks = (numBlocksX, numBlocksK)
# output on device
dW = cuda.device_array((x.shape[0], k.shape[0]), np.complex128)
# input
dC = cuda.to_device(np.ascontiguousarray(c))
dx = cuda.to_device(x)
dk = cuda.to_device(k)
# call kernel
wignerKernel[numBlocks, blockSize](dC, dx, dk, dW, k1)
return 2.0 * dW.copy_to_host()
def relu(input, device=None):
if device is (None or 'gpu'):
if cuda.is_available():
device = 'gpu'
else:
device = 'cpu'
if device is 'gpu':
batch_num, channels, height, width = input.shape
d_output = cuda.device_array(input.shape, dtype=np.float32)
blockdim = (10, 10, 10)
griddim_0 = ceil(batch_num * channels / blockdim[0])
griddim_1 = ceil(height / blockdim[1])
griddim_2 = ceil(width / blockdim[2])
griddim = (griddim_0, griddim_1, griddim_2)
relu_gpu[griddim, blockdim](input, d_output)
return d_output
else:
return relu_cpu(input)
def NNGPU_class(X, W, config):
maxNeuronas = max(config)
config = np.append(X.shape[0], config[:])
# Se inicializa la matriz de pesos
maxPesos = max(config)
threadsPerBlock = maxNeuronas # en cada bloque se calcula una neurona
blocksPerGrid = maxNeuronas
# Se mueven los datos necesarios para el GPU
Wg = cuda.to_device(W)
Xg = cuda.to_device(X)
configG = cuda.to_device(config)
output = cuda.device_array([config.shape[0], config.max()])
NNCUDA_class[blocksPerGrid, threadsPerBlock](Xg, Wg, configG, output)
ret = output.copy_to_host()
#P = Wg.copy_to_host()
#print (P)
return ret[-1]
def apply_segsort(col_keys, col_vals, segments, descending=False):
"""Inplace segemented sort
Parameters
----------
col_keys : Column
col_vals : Column
segments : device array
"""
# prepare
nelem = len(col_keys)
seg_dtype = np.uint32
d_fullsegs = cuda.device_array(segments.size + 1, dtype=seg_dtype)
d_begins = d_fullsegs[:-1]
d_ends = d_fullsegs[1:]
# Note: .astype is required below because .copy_to_device
# is just a plain memcpy
d_begins.copy_to_device(cudautils.astype(segments, dtype=seg_dtype))
d_ends[-1:].copy_to_device(np.require([nelem], dtype=seg_dtype))
begin_bit = 0
end_bit = col_keys.dtype.itemsize * 8
sizeof_key = col_keys.data.dtype.itemsize
sizeof_val = col_vals.data.dtype.itemsize
# sort
plan = libgdf.gdf_segmented_radixsort_plan(nelem, descending, begin_bit,
end_bit)
try:
libgdf.gdf_segmented_radixsort_plan_setup(plan, sizeof_key, sizeof_val)
libgdf.gdf_segmented_radixsort_generic(plan, col_keys.cffi_view,
col_vals.cffi_view,
segments.size,
unwrap_devary(d_begins),
unwrap_devary(d_ends))
finally:
libgdf.gdf_segmented_radixsort_plan_free(plan)
def condense(u, buffer):
#u = input voxel model
#buffer = number of layers of voxels around the boundaries that are left empty
#Outputs a new matrix that is fitted to the input voxel model, removing layers
#that don't store geometry.
m, n, p = u.shape
TPBX, TPBY, TPBZ = TPB, TPB, TPB
minX, maxX, minY, maxY, minZ, maxZ = -1, -1, -1, -1, -1, -1
i, j, k = 0, 0, 0
while minX < 0:
if np.amin(u[i, :, :]) < 0: minX = i
else: i += 1
while minY < 0:
if np.amin(u[:, j, :]) < 0: minY = j
else: j += 1
while minZ < 0:
if np.amin(u[:, :, k]) < 0: minZ = k
else: k += 1
i, j, k = 1, 1, 1
while maxX < 0:
if np.amin(u[m - i, :, :]) < 0: maxX = m - i
else: i += 1
while maxY < 0:
if np.amin(u[:, n - j, :]) < 0: maxY = n - j
else: j += 1
while maxZ < 0:
if np.amin(u[:, :, p - k]) < 0: maxZ = p - k
else: k += 1
xSize = (np.ceil((2 * buffer + maxX - minX) / TPB) * TPB).astype(int)
ySize = (np.ceil((2 * buffer + maxY - minY) / TPB) * TPB).astype(int)
zSize = (np.ceil((2 * buffer + maxZ - minZ) / TPB) * TPB).astype(int)
d_u = cuda.to_device(u)
d_uCondensed = cuda.device_array(shape=[xSize, ySize, zSize],
dtype=np.float32)
gridDims = (xSize + TPBX - 1) // TPBX, (ySize + TPBY - 1) // TPBY, (
zSize + TPBZ - 1) // TPBZ
blockDims = TPBX, TPBY, TPBZ
condenseKernel[gridDims, blockDims](d_u, d_uCondensed, buffer, minX, minY,
minZ)
return d_uCondensed.copy_to_host()
def AveragesOnShellsInnerLogicCCuda(\
retNowR_global_mem,\
retNowI_global_mem,\
n1ofR_global_mem,\
n2ofR_global_mem,\
NumAtROutPre_global_mem,\
End,\
Start,\
NumOnSurf,\
r):
threadsperblock = (1024,1,1)
blockspergrid_x = int(math.ceil(retNowR_global_mem[r][:NumOnSurf].shape[0]/threadsperblock[0]))
blockspergrid = (blockspergrid_x,1,1)
# set up stream
stream = cuda.stream()
device_array_start = time.time()
reduced_global_mem = cuda.device_array((4,(End-Start)))
print("Shape of NumAtROutPre_global_mem is ",NumAtROutPre_global_mem.shape)
print("Shape of reduced_global_mem is ",reduced_global_mem.shape)
#print("Time to create cuda.device_array is ",time.time() - device_array_start)
filter_time = time.time()
cuda_kernels.filter_and_sum[threadsperblock,blockspergrid](\
retNowR_global_mem,\
retNowI_global_mem,\
n1ofR_global_mem,\
n2ofR_global_mem,\
NumAtROutPre_global_mem,\
reduced_global_mem,\
End,\
Start,\
NumOnSurf,\
r)
stream.synchronize()
print("Time to complete filter_and_sum is ",time.time() - filter_time)
reduced_start = time.time()
reduced = reduced_global_mem.copy_to_host()
print("Time to transfer reduced to host is ",time.time() - reduced_start)
return reduced
def cuda_deposition_arrays(Nz = None, Nr = None, fieldtype = None):
"""
Create empty arrays on the GPU for the charge and
current deposition in each of the 4 possible direction.
###########################################
# Needs to be moved to the fields package!
###########################################
Parameters
----------
Nz : int
Number of cells in z.
Nr : int
Number of cells in r.
fieldtype : string
Either 'rho' or 'J'.
"""
# Create empty arrays to store the four different possible
# cell directions a particle can deposit to.
if fieldtype == 'rho':
# Rho - third dimension represents 2 modes
rho0 = cuda.device_array(shape = (Nz, Nr, 2), dtype = np.complex128)
rho1 = cuda.device_array(shape = (Nz, Nr, 2), dtype = np.complex128)
rho2 = cuda.device_array(shape = (Nz, Nr, 2), dtype = np.complex128)
rho3 = cuda.device_array(shape = (Nz, Nr, 2), dtype = np.complex128)
return rho0, rho1, rho2, rho3
if fieldtype == 'J':
# J - third dimension represents 2 modes
# times 3 dimensions (r, t, z)
J0 = cuda.device_array(shape = (Nz, Nr, 6), dtype = np.complex128)
J1 = cuda.device_array(shape = (Nz, Nr, 6), dtype = np.complex128)
J2 = cuda.device_array(shape = (Nz, Nr, 6), dtype = np.complex128)
J3 = cuda.device_array(shape = (Nz, Nr, 6), dtype = np.complex128)
return J0, J1, J2, J3
def copy_to_dense(data, mask, out=None):
"""Copy *data* with validity bits in *mask* into *out*.
The output array can be specified in `out`.
Return a 2-tuple of:
* number of non-null element
* a dense gpu array given the data and mask gpu arrays.
"""
slots, sz = mask_assign_slot(size=data.size, mask=mask)
if out is None:
# output buffer is not provided
# allocate one
alloc_shape = sz
out = cuda.device_array(shape=alloc_shape, dtype=data.dtype)
else:
# output buffer is provided
# check it
if sz >= out.size:
raise ValueError('output array too small')
gpu_copy_to_dense.forall(data.size)(data, mask, slots, out)
return (sz, out)
def apply_label(arr, cats, dtype, na_sentinel):
"""
Parameters
----------
arr : device array
data
cats : device array
Unique category value
dtype : np.dtype
output array dtype
na_sentinel : int
Value to indicate missing value
Returns
-------
result : device array
"""
encs = np.asarray(list(range(cats.size)))
d_encs = to_device(encs)
out = cuda.device_array(shape=arr.size, dtype=dtype)
configured = gpu_label.forall(out.size)
configured(arr, cats, d_encs, na_sentinel, out)
return out
def test_gufunc_stream(self):
@guvectorize(
[void(float32[:, :], float32[:, :], float32[:, :])],
"(m,n),(n,p)->(m,p)",
target="cuda",
)
def matmulcore(A, B, C):
m, n = A.shape
n, p = B.shape
for i in range(m):
for j in range(p):
C[i, j] = 0
for k in range(n):
C[i, j] += A[i, k] * B[k, j]
gufunc = matmulcore
gufunc.max_blocksize = 512
# cuda.driver.flush_pending_free()
matrix_ct = 1001 # an odd number to test thread/block division in CUDA
A = np.arange(matrix_ct * 2 * 4,
dtype=np.float32).reshape(matrix_ct, 2, 4)
B = np.arange(matrix_ct * 4 * 5,
dtype=np.float32).reshape(matrix_ct, 4, 5)
stream = cuda.stream()
dA = cuda.to_device(A, stream)
dB = cuda.to_device(B, stream)
dC = cuda.device_array(shape=(1001, 2, 5),
dtype=A.dtype,
stream=stream)
dC = gufunc(dA, dB, out=dC, stream=stream)
C = dC.copy_to_host(stream=stream)
stream.synchronize()
Gold = ut.matrix_multiply(A, B)
self.assertTrue(np.allclose(C, Gold))
def softmax_backprop_kernel_wrapper(d_d_L_d_out,
d_weight,
d_maxpoolOutput,
d_postSoftmax,
numImage,
d_d_L_d_input,
d_d_L_d_w,
d_d_L_d_b,
blockSize=(32, 32)):
# Tính d_d_L_d_preSoftmax cũng là tính d_d_L_d_b vì nó trỏ cùng 1 vùng nhớ
d_d_L_d_preSoftmax = d_d_L_d_b
gridSize = math.ceil(numImage / blockSize[1])
softmax_backprop_kernel[gridSize,
blockSize[1]](d_d_L_d_out, d_postSoftmax, numImage,
d_d_L_d_preSoftmax)
# Tính d_d_L_d_w
d_d_L_d_preSoftmaxReshape = d_d_L_d_preSoftmax[:numImage].reshape(
numImage, d_d_L_d_preSoftmax.shape[1], 1)
d_maxpoolOutputReshape = d_maxpoolOutput[:numImage].reshape(
numImage, 1, d_weight.shape[1])
gridSize = (math.ceil(d_maxpoolOutputReshape.shape[2] / blockSize[0]),
math.ceil(d_d_L_d_preSoftmaxReshape.shape[1] / blockSize[1]),
d_d_L_d_preSoftmaxReshape.shape[0])
dot_3D_kernel[gridSize, blockSize](d_d_L_d_preSoftmaxReshape,
d_maxpoolOutputReshape, d_d_L_d_w)
# Tính d_d_L_d_input
d_d_L_d_input_temp = cuda.device_array((numImage, 1, d_weight.shape[1]),
dtype=float)
d_d_L_d_preSoftmaxReshape = d_d_L_d_preSoftmax[:numImage].reshape(
numImage, 1, d_d_L_d_preSoftmax.shape[1])
gridSize = (math.ceil(d_d_L_d_input_temp.shape[2] / blockSize[0]),
math.ceil(d_d_L_d_input_temp.shape[1] / blockSize[1]),
d_d_L_d_input_temp.shape[0])
dot_3D2D_kernel[gridSize, blockSize](d_d_L_d_preSoftmaxReshape, d_weight,
d_d_L_d_input_temp)
d_d_L_d_input[0, :numImage] = d_d_L_d_input_temp[:numImage].reshape(
d_maxpoolOutput[:numImage].shape)
def matmul(A, B, matmultype='forward'):
global PARALLELIZE
if not PARALLELIZE:
# NORMAL
gradient = np.zeros((A.shape[0], B.shape[1]))
for i in range(A.shape[0]):
for j in range(B.shape[1]):
for k in range(A.shape[1]):
gradient[i][j] += A[i][k] * B[k][j]
return gradient
# PARALLELIZED
global global_feat
global global_feat_val
global global_feat_transpose
B_global_mem = cuda.to_device(B)
C_global_mem = cuda.device_array((A.shape[0], B.shape[1]))
# Configure the blocks
threadsperblock = (TPB, TPB)
blockspergrid_x = int(math.ceil(A.shape[0] / threadsperblock[1]))
blockspergrid_y = int(math.ceil(B.shape[1] / threadsperblock[0]))
blockspergrid = (blockspergrid_x, blockspergrid_y)
# Start the kernel
if matmultype == 'forward':
matmul_kernel[blockspergrid,
threadsperblock](global_feat, B_global_mem, C_global_mem)
elif matmultype == 'validation':
matmul_kernel[blockspergrid,
threadsperblock](global_feat_val, B_global_mem,
C_global_mem)
elif matmultype == 'backward':
matmul_kernel[blockspergrid,
threadsperblock](global_feat_transpose, B_global_mem,
C_global_mem)
res = C_global_mem.copy_to_host()
return res
def main():
n = 20000000
x = np.arange(n).astype(np.int32)
y = 2 * x
x_device = cuda.to_device(x)
y_device = cuda.to_device(y)
out_device = cuda.device_array(n)
threads_per_block = 1024
blocks_per_grid = math.ceil(n / threads_per_block)
start = time()
gpu_add[blocks_per_grid, threads_per_block](x_device, y_device, out_device,
n)
#gpu_result = out_device.copy_to_host()
cuda.synchronize()
print("gpu vector add time " + str(time() - start))
start = time()
gpu_add_stride[80, threads_per_block](x_device, y_device, out_device, n)
#gpu_result = out_device.copy_to_host()
cuda.synchronize()
print("gpu stride vector add time " + str(time() - start))
def query_execute(df, expr, callenv):
"""Compile & execute the query expression
Note: the expression is compiled and cached for future reuse.
Parameters
----------
df : DataFrame
expr : str
boolean expression
callenv : dict
Contains keys 'locals' and 'globals' which are both dict.
They represent the local and global dictionaries of the caller.
"""
# compile
compiled = query_compile(expr)
kernel = compiled['kernel']
# process env args
envargs = []
envdict = callenv['globals'].copy()
envdict.update(callenv['locals'])
for name in compiled['refnames']:
name = name[len(ENVREF_PREFIX):]
try:
val = envdict[name]
except KeyError:
msg = '{!r} not defined in the calling environment'
raise NameError(msg.format(name))
else:
envargs.append(val)
# prepare col args
colarrays = [df[col].to_gpu_array() for col in compiled['colnames']]
# allocate output buffer
nrows = len(df)
out = cuda.device_array(nrows, dtype=np.bool_)
# run kernel
args = [out] + colarrays + envargs
kernel.forall(nrows)(*args)
return out
def copy_to_dense(data, mask, out=None):
"""Copy *data* with validity bits in *mask* into *out*.
The output array can be specified in `out`.
Return a 2-tuple of:
* number of non-null element
* a dense gpu array given the data and mask gpu arrays.
"""
slots, sz = mask_assign_slot(size=data.size, mask=mask)
if out is None:
# output buffer is not provided
# allocate one
alloc_shape = sz
out = cuda.device_array(shape=alloc_shape, dtype=data.dtype)
else:
# output buffer is provided
# check it
if sz >= out.size:
raise ValueError('output array too small')
gpu_copy_to_dense.forall(data.size)(data, mask, slots, out)
return (sz, out)
def matrix_mult(m1, m2):
A_global_mem = cuda.to_device(m1)
B_global_mem = cuda.to_device(m2)
# Allocate memory on the device for the result
C_global_mem = cuda.device_array(
(A_global_mem.shape[0], B_global_mem.shape[1]))
# Configure the blocks
threadsperblock = (32, 32)
blockspergrid_x = int(math.ceil(m1.shape[0] / threadsperblock[0]))
blockspergrid_y = int(math.ceil(m2.shape[1] / threadsperblock[1]))
blockspergrid = (blockspergrid_x, blockspergrid_y)
# # Start the kernel
matmul[blockspergrid, threadsperblock](A_global_mem, B_global_mem,
C_global_mem)
# fast_matmul[blockspergrid, threadsperblock](A_global_mem, B_global_mem, C_global_mem)
#out = matmul_cuda(A_global_mem, B_global_mem)
# Copy the result back to the host
return C_global_mem.copy_to_host()
def host_naive(A, B):
'''host code for calling naive kernal
'''
A = np.array(A)
B = np.array(B)
m = A.shape[0]
n = B.shape[1]
C = np.full((m, n), 0, dtype=np.float64)
d_A = cuda.to_device(A) # d_ --> device
d_B = cuda.to_device(B)
d_C = cuda.device_array(C.shape, np.float64)
threadsperblock = (TPB, TPB)
blockspergrid_x = math.ceil(A.shape[0] / threadsperblock[0])
blockspergrid_y = math.ceil(B.shape[1] / threadsperblock[1])
blockspergrid = (blockspergrid_x, blockspergrid_y)
mat_mul_naive_kernal[blockspergrid, threadsperblock](d_A, d_B, d_C)
return d_C.copy_to_host()
def __init__(self, shape, blocks, coupling=None, temperature=0, field=0):
"""
:param shape: number of spins along each axis
:param blocks: size of subdivisions along each axis, for parallel
computation
:param coupling: coupling array, must be (2*r+1)x...x(2*r+1) with
(r,...,r) denoting the center.
:param temperature: unitless temperature
:param field: unitless applied field
"""
self.num_dim = len(shape)
self.shape = np.array(shape, dtype=np.int32)
self.num_spins = np.prod(self.shape)
self.blocks = np.array(blocks, dtype=np.int32)
if np.any(self.shape & (self.shape - 1)):
raise ValueError("Shape must consist of powers of 2,")
if self.shape[-1] % 8:
raise ValueError("Last shape must be multiple of 8")
if np.any(self.shape % self.blocks):
raise ValueError("Blocks do not evenly divide shape")
self.shape_shifts = cuda.to_device(ncil.log2(self.shape))
self.block_shifts = cuda.to_device(ncil.log2(self.blocks))
self.spins = cuda.device_array((self.num_spins // 8, ), np.uint8)
self.spins[:] = np.random.randint(0,
256,
size=self.num_spins >> 3,
dtype=np.uint8)
if coupling is None:
coupling = np.empty(np.zeros(self.num_dim), dtype=np.float64)
self.coupling_indices = cuda.to_device(
np.vstack(np.where(coupling != 0.0)).T - (coupling.shape[0] // 2))
self.coupling_constants = cuda.to_device(
coupling[np.where(coupling != 0.0)])
self.temperature = temperature
self.field = field
def monte_carlo_pricer(paths, dt, interest, volatility):
n = paths.shape[0]
blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK
gridsz = int(math.ceil(float(n) / blksz))
# Instantiate cuRAND PRNG
prng = PRNG(PRNG.MRG32K3A)
# Allocate device side array
d_normdist = cuda.device_array(n, dtype=np.double)
c0 = interest - 0.5 * volatility ** 2
c1 = volatility * math.sqrt(dt)
# Simulation loop
d_last = cuda.to_device(paths[:, 0])
for j in range(1, paths.shape[1]):
prng.normal(d_normdist, mean=0, sigma=1)
d_paths = cuda.to_device(paths[:, j])
step(d_last, dt, c0, c1, d_normdist, out=d_paths)
d_paths.copy_to_host(paths[:, j])
d_last = d_paths