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 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 dot(a, b, dtype=None, stream=None, allocator=None):
from pycuda.reduction import get_dot_kernel
if dtype is None:
dtype = _get_common_dtype(a, b)
krnl = get_dot_kernel(dtype, a.dtype, b.dtype)
return krnl(a, b, stream=stream, allocator=allocator)
def dot(a, b, dtype=None, stream=None, allocator=None):
from pycuda.reduction import get_dot_kernel
if dtype is None:
dtype = _get_common_dtype(a, b)
krnl = get_dot_kernel(dtype, a.dtype, b.dtype)
return krnl(a, b, stream=stream, allocator=allocator)
def dot(a, b, dtype=None, stream=None):
from pycuda.reduction import get_dot_kernel
krnl = get_dot_kernel(dtype, a.dtype, b.dtype)
return krnl(a, b, stream=stream)
def dot(a, b, dtype=None, stream=None):
from pycuda.reduction import get_dot_kernel
krnl = get_dot_kernel(dtype, a.dtype, b.dtype)
return krnl(a, b, stream=stream)