def tune_gausstransform():
#setup test input
size = numpy.int32(2000)
ndim = numpy.int32(2)
A = numpy.random.randn(size * ndim).astype(numpy.float64)
B = numpy.random.randn(size * ndim).astype(numpy.float64)
scale = numpy.float64(10.0)
grad = numpy.zeros(size * ndim).astype(numpy.float64)
cost = numpy.zeros(size).astype(numpy.float64)
#time the reference function
arguments = [cost, A, B, size, size, ndim, scale, grad]
with open(get_kernel_path() + 'gausstransform_c.cpp', 'r') as f:
kernel_string = f.read()
tune_params = {"block_size_x": [1]}
print("CPU timing")
tune_kernel("time_GaussTransform",
kernel_string,
size,
arguments,
tune_params,
lang="C",
compiler_options=['-I' + get_kernel_path(), '-O3'])
#tune the GPU function
print("GPU timing")
with open(get_kernel_path() + 'kernels.cu', 'r') as f:
kernel_string = f.read()
scale_sq = (scale * scale).astype(numpy.float64)
arguments = [A, B, size, size, scale_sq, grad, cost]
cp = ['-O3']
tune_params = {"block_size_x": [32, 64, 128, 256, 512, 1024]}
kernel1 = tune_kernel("GaussTransform",
kernel_string,
size,
arguments,
tune_params,
grid_div_x=[],
compiler_options=cp)
arguments = [numpy.zeros(1).astype(numpy.float64), cost, size, size, size]
kernel2 = tune_kernel("reduce_cross_term",
kernel_string,
1,
arguments,
tune_params,
grid_div_x=[],
compiler_options=cp)
best_config1 = min(kernel1[0], key=lambda x: x['time'])
best_config2 = min(kernel2[0], key=lambda x: x['time'])
print("best GPU configuration, total time=",
best_config1['time'] + best_config2['time'])
print(best_config1)
print(best_config2)
def tune_expdist():
tune_params = OrderedDict()
tune_params["block_size_x"] = [32] #[2**i for i in range(5,10)]
tune_params["block_size_y"] = [2**i for i in range(6)]
tune_params["tile_size_x"] = [2**i for i in range(4)]
tune_params["tile_size_y"] = [2**i for i in range(4)]
tune_params["use_shared_mem"] = [1] #[0, 1]
#setup test input
alloc_size = 22000
size = numpy.int32(20000)
max_blocks = numpy.int32( numpy.ceil(size / float(numpy.amin(tune_params["block_size_x"]))) )
ndim = numpy.int32(2)
A = numpy.random.randn(alloc_size*ndim).astype(numpy.float64)
B = A+0.00001*numpy.random.randn(alloc_size*ndim).astype(numpy.float64)
scale_A = numpy.absolute(0.01*numpy.random.randn(alloc_size).astype(numpy.float64))
scale_B = numpy.absolute(0.01*numpy.random.randn(alloc_size).astype(numpy.float64))
cost = numpy.zeros((max_blocks)).astype(numpy.float64)
#tune the GPU function
with open(get_kernel_path()+'kernels.cu', 'r') as f:
kernel_string = f.read()
arguments = [A, B, size, size, scale_A, scale_B, cost]
cp = ['-O3']
grid_div_x = ["block_size_x", "tile_size_x"]
kernel1, env = tune_kernel("ExpDist_column", kernel_string, size, arguments, tune_params,
compiler_options=cp, grid_div_x=grid_div_x)
devname = "".join(env["device_name"].split()) #device name without whitespace
with open("expdist_column_" + devname + ".json", 'w') as fp:
json.dump([kernel1, env], fp)
best_config1 = min(kernel1, key=lambda x:x['time'])
nblocks = numpy.int32( numpy.ceil(size / float(best_config1["block_size_x"]*best_config1["tile_size_x"])) )
tune_params = OrderedDict()
tune_params["block_size_x"] = [32*i for i in range(1,33)]
arguments = [numpy.zeros(1).astype(numpy.float64), cost, size, size, nblocks]
kernel2 = tune_kernel("reduce_cross_term", kernel_string, 1, arguments, tune_params,
grid_div_x=[], compiler_options=cp)
best_config2 = min(kernel2[0], key=lambda x:x['time'])
print("best GPU configuration, total time=", best_config1['time'] + best_config2['time'])
print(best_config1)
print(best_config2)
def tune_expdist():
device = 2
tune_params = OrderedDict()
tune_params["block_size_x"] = [32] #[2**i for i in range(5,10)]
tune_params["block_size_y"] = [2**i for i in range(6)]
tune_params["tile_size_x"] = [2**i for i in range(4)]
tune_params["tile_size_y"] = [2**i for i in range(4)]
tune_params["use_shared_mem"] = [1] #[0, 1]
#setup test input
alloc_size = 22000
size = numpy.int32(20000)
max_blocks = numpy.int32(
numpy.ceil(size / float(numpy.amin(tune_params["block_size_x"]))) *
numpy.ceil(size / float(numpy.amin(tune_params["block_size_y"]))))
ndim = numpy.int32(2)
A = numpy.random.randn(alloc_size * ndim).astype(numpy.float64)
B = A + 0.00001 * numpy.random.randn(alloc_size * ndim).astype(
numpy.float64)
scale_A = numpy.absolute(
0.01 * numpy.random.randn(alloc_size).astype(numpy.float64))
scale_B = numpy.absolute(
0.01 * numpy.random.randn(alloc_size).astype(numpy.float64))
cost = numpy.zeros((max_blocks)).astype(numpy.float64)
#time the reference function
#arguments = [cost, A, B, size, size, ndim, scale_A, scale_B]
#with open(get_kernel_path()+'expdist_c.cpp', 'r') as f:
# kernel_string = f.read()
#print("CPU timing")
#tune_kernel("time_expdist", kernel_string, size, arguments, {"block_size_x": [1]},
# lang="C", compiler_options=['-I'+get_kernel_path(), '-O3'], device=2)
#tune the GPU function
print("GPU timing")
with open(get_kernel_path() + 'kernels.cu', 'r') as f:
kernel_string = f.read()
arguments = [A, B, size, size, scale_A, scale_B, cost]
cp = ['-O3']
grid_div_x = ["block_size_x", "tile_size_x"]
grid_div_y = ["block_size_y", "tile_size_y"]
kernel1 = tune_kernel("ExpDist",
kernel_string, (size, size),
arguments,
tune_params,
compiler_options=cp,
grid_div_x=grid_div_x,
grid_div_y=grid_div_y,
device=2)
with open("expdist.json", 'w') as fp:
json.dump(kernel1, fp)
best_config1 = min(kernel1[0], key=lambda x: x['time'])
nblocks = numpy.int32(
numpy.ceil(size / float(
best_config1["block_size_x"] * best_config1["tile_size_x"])) *
numpy.ceil(size / float(
best_config1["block_size_y"] * best_config1["tile_size_y"])))
tune_params = OrderedDict()
tune_params["block_size_x"] = [32 * i for i in range(1, 33)]
arguments = [
numpy.zeros(1).astype(numpy.float64), cost, size, size, nblocks
]
kernel2 = tune_kernel("reduce_cross_term",
kernel_string,
1,
arguments,
tune_params,
grid_div_x=[],
compiler_options=cp,
device=2)
best_config2 = min(kernel2[0], key=lambda x: x['time'])
print("best GPU configuration, total time=",
best_config1['time'] + best_config2['time'])
print(best_config1)
print(best_config2)