def table_from_cursor(cursor):
from pytools import Table
tbl = Table()
tbl.add_row([column[0] for column in cursor.description])
for row in cursor:
tbl.add_row(row)
return tbl
def pretty_print(self,
abscissa_label="h",
error_label="Error",
gliding_mean=2,
abscissa_format="%s",
error_format="%s",
eoc_format="%s"):
from pytools import Table
tbl = Table()
tbl.add_row((abscissa_label, error_label, "Running EOC"))
gm_eoc = self.estimate_order_of_convergence(gliding_mean)
for i, (absc, err) in enumerate(self.history):
absc_str = abscissa_format % absc
err_str = error_format % err
if i < gliding_mean-1:
eoc_str = ""
else:
eoc_str = eoc_format % (gm_eoc[i - gliding_mean + 1, 1])
tbl.add_row((absc_str, err_str, eoc_str))
if len(self.history) > 1:
return "%s\n\nOverall EOC: %s" % (str(tbl),
self.estimate_order_of_convergence()[0, 1])
else:
return str(tbl)
def tabulate_ascii(rows, col_fmt):
del col_fmt
from pytools import Table
result = Table()
for row in rows:
result.add_row(row)
return str(result)
def table_from_cursor(cursor):
from pytools import Table
tbl = Table()
tbl.add_row([column[0] for column in cursor.description])
for row in cursor:
tbl.add_row(row)
return tbl
def __str__(self):
from pytools import Table
tbl = Table()
tbl.add_row(("p", "error"))
for p, err in zip(self.orders, self.errors):
tbl.add_row((str(p), str(err)))
return str(tbl)
def __str__(self):
from pytools import Table
tbl = Table()
tbl.add_row(("p", "error"))
for p, err in zip(self.orders, self.errors):
tbl.add_row((str(p), str(err)))
return str(tbl)
def main():
from pytools import Table
tbl = Table()
tbl.add_row(("type", "size [MiB]", "time [ms]", "mem.bw [GB/s]"))
from random import shuffle
for dtype_out in [numpy.float32, numpy.float64]:
for ex in range(15, 27):
sz = 1 << ex
print sz
from pycuda.curandom import rand as curand
a_gpu = curand((sz, ))
b_gpu = curand((sz, ))
assert sz == a_gpu.shape[0]
assert len(a_gpu.shape) == 1
from pycuda.reduction import get_sum_kernel, get_dot_kernel
krnl = get_dot_kernel(dtype_out, a_gpu.dtype)
elapsed = [0]
def wrap_with_timer(f):
def result(*args, **kwargs):
start = cuda.Event()
stop = cuda.Event()
start.record()
f(*args, **kwargs)
stop.record()
stop.synchronize()
elapsed[0] += stop.time_since(start)
return result
# warm-up
for i in range(3):
krnl(a_gpu, b_gpu)
cnt = 10
for i in range(cnt):
krnl(
a_gpu,
b_gpu,
#krnl(a_gpu,
kernel_wrapper=wrap_with_timer)
bytes = a_gpu.nbytes * 2 * cnt
secs = elapsed[0] * 1e-3
tbl.add_row((str(dtype_out), a_gpu.nbytes / (1 << 20),
elapsed[0] / cnt, bytes / secs / 1e9))
print tbl
def test_table():
import math
from pytools import Table
tbl = Table()
tbl.add_row(("i", "i^2", "i^3", "sqrt(i)"))
for i in range(8):
tbl.add_row((i, i**2, i**3, math.sqrt(i)))
print(tbl)
print()
print(tbl.latex())
def main():
from pytools import Table
tbl = Table()
tbl.add_row(("type", "size [MiB]", "time [ms]", "mem.bw [GB/s]"))
from random import shuffle
for dtype_out in [numpy.float32, numpy.float64]:
for ex in range(15,27):
sz = 1 << ex
print sz
from pycuda.curandom import rand as curand
a_gpu = curand((sz,))
b_gpu = curand((sz,))
assert sz == a_gpu.shape[0]
assert len(a_gpu.shape) == 1
from pycuda.reduction import get_sum_kernel, get_dot_kernel
krnl = get_dot_kernel(dtype_out, a_gpu.dtype)
elapsed = [0]
def wrap_with_timer(f):
def result(*args, **kwargs):
start = cuda.Event()
stop = cuda.Event()
start.record()
f(*args, **kwargs)
stop.record()
stop.synchronize()
elapsed[0] += stop.time_since(start)
return result
# warm-up
for i in range(3):
krnl(a_gpu, b_gpu)
cnt = 10
for i in range(cnt):
krnl(a_gpu, b_gpu,
#krnl(a_gpu,
kernel_wrapper=wrap_with_timer)
bytes = a_gpu.nbytes*2*cnt
secs = elapsed[0]*1e-3
tbl.add_row((str(dtype_out), a_gpu.nbytes/(1<<20), elapsed[0]/cnt, bytes/secs/1e9))
print tbl
def ascii_table(table_format, header, rows):
from pytools import Table
table = Table()
table.add_row(header)
for input_row in rows:
row = []
for item in input_row:
if item.startswith(r"\num{"):
# Strip \num{...} formatting
row.append(item[5:-1])
else:
row.append(item)
table.add_row(row)
return str(table)
def test_cost_model(ctx, calibration_params):
queue = cl.CommandQueue(ctx)
actx = PyOpenCLArrayContext(queue, force_device_scalars=True)
cost_model = QBXCostModel()
for lpot_source in test_geometries(actx):
lpot_source = lpot_source.copy(cost_model=cost_model)
from pytential import GeometryCollection
places = GeometryCollection(lpot_source)
density_discr = places.get_discretization(places.auto_source.geometry)
bound_op = get_bound_op(places)
sigma = get_test_density(actx, density_discr)
cost_S, _ = bound_op.cost_per_stage(calibration_params, sigma=sigma)
model_result = one(cost_S.values())
# Warm-up run.
bound_op.eval({"sigma": sigma}, array_context=actx)
temp_timing_results = []
for _ in range(RUNS):
timing_data = {}
bound_op.eval({"sigma": sigma},
array_context=actx,
timing_data=timing_data)
temp_timing_results.append(one(timing_data.values()))
timing_result = {}
for param in model_result:
timing_result[param] = (sum(
temp_timing_result[param]["process_elapsed"]
for temp_timing_result in temp_timing_results)) / RUNS
from pytools import Table
table = Table()
table.add_row(["stage", "actual (s)", "predicted (s)"])
for stage in model_result:
row = [
stage,
f"{timing_result[stage]:.2f}",
f"{model_result[stage]:.2f}",
]
table.add_row(row)
print(table)
def test_table():
import math
from pytools import Table
tbl = Table()
tbl.add_row(("i", "i^2", "i^3", "sqrt(i)"))
for i in range(8):
tbl.add_row((i, i**2, i**3, math.sqrt(i)))
print(tbl)
print()
print(tbl.latex())
# {{{ test merging
from pytools import merge_tables
tbl = merge_tables(tbl, tbl, tbl, skip_columns=(0, ))
print(tbl.github_markdown())
def _to_table(self, *,
abscissa_label="h",
error_label="Error",
gliding_mean=2,
abscissa_format="%s",
error_format="%s",
eoc_format="%s"):
from pytools import Table
tbl = Table()
tbl.add_row((abscissa_label, error_label, "Running EOC"))
gm_eoc = self.estimate_order_of_convergence(gliding_mean)
for i, (absc, err) in enumerate(self.history):
absc_str = abscissa_format % absc
err_str = error_format % err
if i < gliding_mean-1:
eoc_str = ""
else:
eoc_str = eoc_format % (gm_eoc[i - gliding_mean + 1, 1])
tbl.add_row((absc_str, err_str, eoc_str))
if len(self.history) > 1:
order = self.estimate_order_of_convergence()[0, 1]
tbl.add_row(("Overall", "", eoc_format % order))
return tbl
def pretty_print(self,
abscissa_label="N",
error_label="Error",
gliding_mean=2):
from pytools import Table
tbl = Table()
tbl.add_row((abscissa_label, error_label, "Running EOC"))
gm_eoc = self.estimate_order_of_convergence(gliding_mean)
for i, (absc, err) in enumerate(self.history):
if i < gliding_mean - 1:
tbl.add_row((str(absc), str(err), ""))
else:
tbl.add_row(
(str(absc), str(err), str(gm_eoc[i - gliding_mean + 1,
1])))
if len(self.history) > 1:
return str(
tbl
) + "\n\nOverall EOC: %s" % self.estimate_order_of_convergence()[0,
1]
else:
return str(tbl)
def test_cost_model(ctx, cost_model):
queue = cl.CommandQueue(ctx)
for lpot_source in test_geometries(queue):
lpot_source = lpot_source.copy(cost_model=cost_model)
bound_op = get_bound_op(lpot_source)
sigma = get_test_density(queue, lpot_source)
cost_S = bound_op.get_modeled_cost(queue, sigma=sigma)
model_result = (one(
cost_S.values()).get_predicted_times(merge_close_lists=True))
# Warm-up run.
bound_op.eval(queue, {"sigma": sigma})
temp_timing_results = []
for _ in range(RUNS):
timing_data = {}
bound_op.eval(queue, {"sigma": sigma}, timing_data=timing_data)
temp_timing_results.append(one(timing_data.values()))
timing_result = {}
for param in model_result:
timing_result[param] = (sum(
temp_timing_result[param]["process_elapsed"]
for temp_timing_result in temp_timing_results)) / RUNS
from pytools import Table
table = Table()
table.add_row(["stage", "actual (s)", "predicted (s)"])
for stage in model_result:
row = [
stage,
"%.2f" % timing_result[stage],
"%.2f" % model_result[stage]
]
table.add_row(row)
print(table)
def main():
from pytools import Table
tbl = Table()
tbl.add_row(("size [MiB]", "time [s]", "mem.bw [GB/s]"))
import pycuda.gpuarray as gpuarray
# they're floats, i.e. 4 bytes each
for power in range(10, 28):
size = 1 << power
print(size)
a = gpuarray.empty((size, ), dtype=numpy.float32)
b = gpuarray.empty_like(a)
a.fill(1)
b.fill(2)
if power > 20:
count = 10
else:
count = 100
elapsed = [0]
def add_timer(_, time):
elapsed[0] += time()
for i in range(count):
a.mul_add(1, b, 2, add_timer)
bytes = a.nbytes * count * 3
bytes = a.nbytes * count * 3
tbl.add_row((a.nbytes / (1 << 20), elapsed[0] / count,
bytes / elapsed[0] / 1e9))
print(tbl)
def main():
from pytools import Table
tbl = Table()
tbl.add_row(("size [MiB]", "time [s]", "mem.bw [GB/s]"))
import pycuda.gpuarray as gpuarray
# they're floats, i.e. 4 bytes each
for power in range(10, 28):
size = 1<<power
print(size)
a = gpuarray.empty((size,), dtype=numpy.float32)
b = gpuarray.empty_like(a)
a.fill(1)
b.fill(2)
if power > 20:
count = 10
else:
count = 100
elapsed = [0]
def add_timer(_, time):
elapsed[0] += time()
for i in range(count):
a.mul_add(1, b, 2, add_timer)
bytes = a.nbytes*count*3
bytes = a.nbytes*count*3
tbl.add_row((a.nbytes/(1<<20), elapsed[0]/count, bytes/elapsed[0]/1e9))
print(tbl)
def pretty_print(self, abscissa_label="N", error_label="Error", gliding_mean=2):
from pytools import Table
tbl = Table()
tbl.add_row((abscissa_label, error_label, "Running EOC"))
gm_eoc = self.estimate_order_of_convergence(gliding_mean)
for i, (absc, err) in enumerate(self.history):
if i < gliding_mean-1:
tbl.add_row((str(absc), str(err), ""))
else:
tbl.add_row((str(absc), str(err), str(gm_eoc[i-gliding_mean+1,1])))
if len(self.history) > 1:
return str(tbl) + "\n\nOverall EOC: %s" % self.estimate_order_of_convergence()[0,1]
else:
return str(tbl)
def main():
import pycuda.gpuarray as gpuarray
sizes = []
times = []
flops = []
flopsCPU = []
timesCPU = []
for power in range(10, 25): # 24
size = 1<<power
print size
sizes.append(size)
a = gpuarray.zeros((size,), dtype=numpy.float32)
if power > 20:
count = 100
else:
count = 1000
#start timer
start = drv.Event()
end = drv.Event()
start.record()
#cuda operation which fills the array with random numbers
for i in range(count):
curandom.rand((size, ))
#stop timer
end.record()
end.synchronize()
#calculate used time
secs = start.time_till(end)*1e-3
times.append(secs/count)
flops.append(size)
#cpu operations which fills teh array with random data
a = numpy.array((size,), dtype=numpy.float32)
#start timer
start = drv.Event()
end = drv.Event()
start.record()
#cpu operation which fills the array with random data
for i in range(count):
numpy.random.rand(size).astype(numpy.float32)
#stop timer
end.record()
end.synchronize()
#calculate used time
secs = start.time_till(end)*1e-3
#add results to variable
timesCPU.append(secs/count)
flopsCPU.append(size)
#calculate pseudo flops
flops = [f/t for f, t in zip(flops,times)]
flopsCPU = [f/t for f, t in zip(flopsCPU,timesCPU)]
#print the data out
tbl = Table()
tbl.add_row(("Size", "Time GPU", "Size/Time GPU", "Time CPU","Size/Time CPU","GPU vs CPU speedup"))
for s, t, f,tCpu,fCpu in zip(sizes, times, flops,timesCPU,flopsCPU):
tbl.add_row((s,t,f,tCpu,fCpu,f/fCpu))
print tbl
def main():
import pycuda.gpuarray as gpuarray
sizes = []
times = []
flops = []
flopsCPU = []
timesCPU = []
for power in range(10, 25): # 24
size = 1 << power
print size
sizes.append(size)
a = gpuarray.zeros((size, ), dtype=numpy.float32)
if power > 20:
count = 100
else:
count = 1000
#start timer
start = drv.Event()
end = drv.Event()
start.record()
#cuda operation which fills the array with random numbers
for i in range(count):
curandom.rand((size, ))
#stop timer
end.record()
end.synchronize()
#calculate used time
secs = start.time_till(end) * 1e-3
times.append(secs / count)
flops.append(size)
#cpu operations which fills teh array with random data
a = numpy.array((size, ), dtype=numpy.float32)
#start timer
start = drv.Event()
end = drv.Event()
start.record()
#cpu operation which fills the array with random data
for i in range(count):
numpy.random.rand(size).astype(numpy.float32)
#stop timer
end.record()
end.synchronize()
#calculate used time
secs = start.time_till(end) * 1e-3
#add results to variable
timesCPU.append(secs / count)
flopsCPU.append(size)
#calculate pseudo flops
flops = [f / t for f, t in zip(flops, times)]
flopsCPU = [f / t for f, t in zip(flopsCPU, timesCPU)]
#print the data out
tbl = Table()
tbl.add_row(("Size", "Time GPU", "Size/Time GPU", "Time CPU",
"Size/Time CPU", "GPU vs CPU speedup"))
for s, t, f, tCpu, fCpu in zip(sizes, times, flops, timesCPU, flopsCPU):
tbl.add_row((s, t, f, tCpu, fCpu, f / fCpu))
print tbl
def main():
import pycuda.gpuarray as gpuarray
sizes = []
times_gpu = []
flops_gpu = []
flops_cpu = []
times_cpu = []
from pycuda.tools import bitlog2
max_power = bitlog2(drv.mem_get_info()[0]) - 2
# they're floats, i.e. 4 bytes each
for power in range(10, max_power):
size = 1 << power
print(size)
sizes.append(size)
a = gpuarray.zeros((size,), dtype=numpy.float32)
b = gpuarray.zeros((size,), dtype=numpy.float32)
b.fill(1)
if power > 20:
count = 100
else:
count = 1000
# gpu -----------------------------------------------------------------
start = drv.Event()
end = drv.Event()
start.record()
for i in range(count):
a + b
end.record()
end.synchronize()
secs = start.time_till(end) * 1e-3
times_gpu.append(secs / count)
flops_gpu.append(size)
del a
del b
# cpu -----------------------------------------------------------------
a_cpu = numpy.random.randn(size).astype(numpy.float32)
b_cpu = numpy.random.randn(size).astype(numpy.float32)
# start timer
from time import time
start = time()
for i in range(count):
a_cpu + b_cpu
secs = time() - start
times_cpu.append(secs / count)
flops_cpu.append(size)
# calculate pseudo flops
flops_gpu = [f / t for f, t in zip(flops_gpu, times_gpu)]
flops_cpu = [f / t for f, t in zip(flops_cpu, times_cpu)]
from pytools import Table
tbl = Table()
tbl.add_row(
(
"Size",
"Time GPU",
"Size/Time GPU",
"Time CPU",
"Size/Time CPU",
"GPU vs CPU speedup",
)
)
for s, t, f, t_cpu, f_cpu in zip(sizes, times_gpu, flops_gpu, times_cpu, flops_cpu):
tbl.add_row((s, t, f, t_cpu, f_cpu, f / f_cpu))
print(tbl)
def main():
drv.init()
assert drv.Device.count() >= 1
ctx = drv.Device(0).make_context()
import pycuda.gpuarray as gpuarray
# make sure all the kernels are compiled
gpuarray.GPUArray.compile_kernels()
print "done compiling"
sizes = []
times = []
flops = []
flopsCPU = []
timesCPU = []
for power in range(10, 25): # 24
size = 1 << power
print size
sizes.append(size)
a = gpuarray.zeros((size,), dtype=numpy.float32)
b = gpuarray.zeros((size,), dtype=numpy.float32)
b.fill(1)
if power > 20:
count = 100
else:
count = 1000
# start timer
start = drv.Event()
end = drv.Event()
start.record()
# cuda operation which adds two arrays over count time to get an average
for i in range(count):
a + b
# stop timer
end.record()
end.synchronize()
# calculate used time
secs = start.time_till(end) * 1e-3
times.append(secs / count)
flops.append(size)
# cpu operations which adds two arrays
aCpu = numpy.random.randn(size).astype(numpy.float32)
bCpu = numpy.random.randn(size).astype(numpy.float32)
# start timer
start = drv.Event()
end = drv.Event()
start.record()
# cpu operation which adds two arrays over count time to get an average
for i in range(count):
aCpu + bCpu
# stop timer
end.record()
end.synchronize()
# calculate used time
secs = start.time_till(end) * 1e-3
# add results to variable
timesCPU.append(secs / count)
flopsCPU.append(size)
# calculate pseudo flops
flops = [f / t for f, t in zip(flops, times)]
flopsCPU = [f / t for f, t in zip(flopsCPU, timesCPU)]
# print the data out
from pytools import Table
tbl = Table()
tbl.add_row(("Size", "Time GPU", "Size/Time GPU", "Time CPU", "Size/Time CPU", "GPU vs CPU speedup"))
for s, t, f, tCpu, fCpu in zip(sizes, times, flops, timesCPU, flopsCPU):
tbl.add_row((s, t, f, tCpu, fCpu, f / fCpu))
print tbl
def __call__(self, eval_dependency, lift_plan):
discr = self.discr
fplan = self.plan
given = fplan.given
elgroup, = discr.element_groups
all_fluxes_on_faces = [gpuarray.empty(
given.matmul_preimage_shape(lift_plan),
dtype=given.float_type,
allocator=discr.pool.allocate)
for i in range(len(self.fluxes))]
fdata = self.flux_face_data_block(elgroup)
ilist_data = self.index_list_data()
block, gather, texref_map = self.get_kernel(fdata, ilist_data,
for_benchmark=False)
for dep_expr in self.all_deps:
dep_field = eval_dependency(dep_expr)
from hedge.tools import is_zero
if is_zero(dep_field):
if dep_expr in self.dep_to_tag:
dep_field = discr.boundary_zeros(self.dep_to_tag[dep_expr])
else:
dep_field = discr.volume_zeros()
assert dep_field.dtype == given.float_type
dep_field.bind_to_texref_ext(texref_map[dep_expr],
allow_double_hack=True)
if set(["cuda_flux", "cuda_debugbuf"]) <= discr.debug:
debugbuf = gpuarray.zeros((10000,), dtype=given.float_type)
else:
from hedge.backends.cuda.tools import FakeGPUArray
debugbuf = FakeGPUArray()
if discr.instrumented:
discr.flux_gather_timer.add_timer_callable(gather.prepared_timed_call(
(len(discr.blocks), 1), block,
debugbuf.gpudata,
fdata.device_memory,
*tuple(fof.gpudata for fof in all_fluxes_on_faces)
))
discr.gmem_bytes_gather.add(
len(discr.blocks) * fdata.block_bytes
+
given.float_size()
* (
# fetch
len(self.fluxes)
* 2*fdata.fp_count
* fplan.dofs_per_face
# store
+ len(discr.blocks)
* len(self.fluxes)
* fplan.microblocks_per_block()
* fplan.aligned_face_dofs_per_microblock()
))
else:
gather.prepared_call(
(len(discr.blocks), 1), block,
debugbuf.gpudata,
fdata.device_memory,
*tuple(fof.gpudata for fof in all_fluxes_on_faces)
)
if set(["cuda_flux", "cuda_debugbuf"]) <= discr.debug:
from hedge.tools import get_rank, wait_for_keypress
if get_rank(discr) == 0:
copied_debugbuf = debugbuf.get()
print "DEBUG", len(discr.blocks)
numpy.set_printoptions(linewidth=130)
#print numpy.reshape(copied_debugbuf, (32, 16))
print copied_debugbuf[:50]
#for i in range(len(discr.blocks)*6):
#print i, copied_debugbuf[i*16:(i+1)*16]
#print i, [x-10000 for x in sorted(copied_debugbuf[i*16:(i+1)*16]) if x != 0]
wait_for_keypress(discr)
if "cuda_flux" in discr.debug:
from hedge.tools import get_rank, wait_for_keypress
if get_rank(discr) == 0:
numpy.set_printoptions(linewidth=130, precision=2, threshold=10**6)
if True:
cols = []
for k in range(len(all_fluxes_on_faces)):
my_fof = all_fluxes_on_faces[k].get()
def sstruc(a):
result = ""
for i in a:
if i == 0:
result += "0"
elif abs(i) < 1e-10:
result += "-"
elif numpy.isnan(i):
result += "N"
elif i == 17:
result += "*"
else:
result += "#"
return result
useful_sz = given.block_count \
* given.microblocks_per_block \
* lift_plan.aligned_preimage_dofs_per_microblock
my_col = []
i = 0
while i < useful_sz:
my_col.append(sstruc(my_fof[i:i+16]))
i += 16
cols.append(my_col)
from pytools import Table
tbl = Table()
tbl.add_row(["num"]+range(len(cols)))
i = 0
for row in zip(*cols):
tbl.add_row((i,)+row)
i += 1
print tbl
else:
for i in range(len(all_fluxes_on_faces)):
print i
print all_fluxes_on_faces[i].get()
wait_for_keypress(discr)
#print "B", [la.norm(fof.get()) for fof in all_fluxes_on_faces]
return all_fluxes_on_faces
def __call__(self, eval_dependency, lift_plan):
discr = self.discr
fplan = self.plan
given = fplan.given
elgroup, = discr.element_groups
all_fluxes_on_faces = [
gpuarray.empty(given.matmul_preimage_shape(lift_plan),
dtype=given.float_type,
allocator=discr.pool.allocate)
for i in range(len(self.fluxes))
]
fdata = self.flux_face_data_block(elgroup)
ilist_data = self.index_list_data()
block, gather, texref_map = self.get_kernel(fdata,
ilist_data,
for_benchmark=False)
for dep_expr in self.all_deps:
dep_field = eval_dependency(dep_expr)
from hedge.tools import is_zero
if is_zero(dep_field):
if dep_expr in self.dep_to_tag:
dep_field = discr.boundary_zeros(self.dep_to_tag[dep_expr])
else:
dep_field = discr.volume_zeros()
assert dep_field.dtype == given.float_type, "Wrong types: %s: %s, %s: %s" % (
dep_expr, dep_field.dtype, given, given.float_type)
dep_field.bind_to_texref_ext(texref_map[dep_expr],
allow_double_hack=True)
if set(["cuda_flux", "cuda_debugbuf"]) <= discr.debug:
debugbuf = gpuarray.zeros((10000, ), dtype=given.float_type)
else:
from hedge.backends.cuda.tools import FakeGPUArray
debugbuf = FakeGPUArray()
if discr.instrumented:
discr.flux_gather_timer.add_timer_callable(
gather.prepared_timed_call(
(len(discr.blocks), 1), block, debugbuf.gpudata,
fdata.device_memory,
*tuple(fof.gpudata for fof in all_fluxes_on_faces)))
discr.gmem_bytes_gather.add(
len(discr.blocks) * fdata.block_bytes + given.float_size() * (
# fetch
len(self.fluxes) * 2 * fdata.fp_count * fplan.dofs_per_face
# store
+ len(discr.blocks) * len(self.fluxes) *
fplan.microblocks_per_block() *
fplan.aligned_face_dofs_per_microblock()))
else:
gather.prepared_call(
(len(discr.blocks), 1), block, debugbuf.gpudata,
fdata.device_memory,
*tuple(fof.gpudata for fof in all_fluxes_on_faces))
if set(["cuda_flux", "cuda_debugbuf"]) <= discr.debug:
from hedge.tools import get_rank, wait_for_keypress
if get_rank(discr) == 0:
copied_debugbuf = debugbuf.get()
print "DEBUG", len(discr.blocks)
numpy.set_printoptions(linewidth=130)
#print numpy.reshape(copied_debugbuf, (32, 16))
print copied_debugbuf[:50]
#for i in range(len(discr.blocks)*6):
#print i, copied_debugbuf[i*16:(i+1)*16]
#print i, [x-10000 for x in sorted(copied_debugbuf[i*16:(i+1)*16]) if x != 0]
wait_for_keypress(discr)
if "cuda_flux" in discr.debug:
from hedge.tools import get_rank, wait_for_keypress
if get_rank(discr) == 0:
numpy.set_printoptions(linewidth=130,
precision=2,
threshold=10**6)
if True:
cols = []
for k in range(len(all_fluxes_on_faces)):
my_fof = all_fluxes_on_faces[k].get()
def sstruc(a):
result = ""
for i in a:
if i == 0:
result += "0"
elif abs(i) < 1e-10:
result += "-"
elif numpy.isnan(i):
result += "N"
elif i == 17:
result += "*"
else:
result += "#"
return result
useful_sz = given.block_count \
* given.microblocks_per_block \
* lift_plan.aligned_preimage_dofs_per_microblock
my_col = []
i = 0
while i < useful_sz:
my_col.append(sstruc(my_fof[i:i + 16]))
i += 16
cols.append(my_col)
from pytools import Table
tbl = Table()
tbl.add_row(["num"] + range(len(cols)))
i = 0
for row in zip(*cols):
tbl.add_row((i, ) + row)
i += 1
print tbl
else:
for i in range(len(all_fluxes_on_faces)):
print i
print all_fluxes_on_faces[i].get()
wait_for_keypress(discr)
#print "B", [la.norm(fof.get()) for fof in all_fluxes_on_faces]
return all_fluxes_on_faces
#generate our output table, one for gpu, one for cpu
tblCPU = Table()
tblGPU = Table()
tblSPD = Table()
#contains all the method names
methods = ["size"]
for name in dir(cuma):
if (name.startswith("__") and name.endswith("__")) == False:
method = getattr(cuma, name)
if type(method) == types.FunctionType:
methods.append(name)
tblCPU.add_row(methods)
tblGPU.add_row(methods)
tblSPD.add_row(methods)
#generate arrays with differnt sizes
for power in range(1,20):
size = 1<<power
#temp variables
rowCPU = [size]
rowGPU = [size]
rowSPD = [size]
print "calculating: ", size
for name in dir(cuma):
def main():
import pycuda.gpuarray as gpuarray
sizes = []
times_gpu = []
flops_gpu = []
flops_cpu = []
times_cpu = []
from pycuda.tools import bitlog2
max_power = bitlog2(drv.mem_get_info()[0]) - 2
# they're floats, i.e. 4 bytes each
for power in range(10, max_power):
size = 1<<power
print size
sizes.append(size)
a = gpuarray.zeros((size,), dtype=numpy.float32)
b = gpuarray.zeros((size,), dtype=numpy.float32)
b.fill(1)
if power > 20:
count = 100
else:
count = 1000
# gpu -----------------------------------------------------------------
start = drv.Event()
end = drv.Event()
start.record()
for i in range(count):
a+b
end.record()
end.synchronize()
secs = start.time_till(end)*1e-3
times_gpu.append(secs/count)
flops_gpu.append(size)
del a
del b
# cpu -----------------------------------------------------------------
a_cpu = numpy.random.randn(size).astype(numpy.float32)
b_cpu = numpy.random.randn(size).astype(numpy.float32)
#start timer
from time import time
start = time()
for i in range(count):
a_cpu + b_cpu
secs = time() - start
times_cpu.append(secs/count)
flops_cpu.append(size)
# calculate pseudo flops
flops_gpu = [f/t for f, t in zip(flops_gpu,times_gpu)]
flops_cpu = [f/t for f, t in zip(flops_cpu,times_cpu)]
from pytools import Table
tbl = Table()
tbl.add_row(("Size", "Time GPU", "Size/Time GPU",
"Time CPU","Size/Time CPU","GPU vs CPU speedup"))
for s, t, f, t_cpu, f_cpu in zip(sizes, times_gpu, flops_gpu, times_cpu, flops_cpu):
tbl.add_row((s, t, f, t_cpu, f_cpu, f/f_cpu))
print tbl