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Dataset generation

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This commit is contained in:
Philippe Tillet
2014-09-27 20:54:17 -04:00
parent 02d39ed71b
commit 693b8b67b0
4 changed files with 210 additions and 127 deletions
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152
autotune/python/autotune.py
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@@ -11,6 +11,7 @@ import pyviennacl as vcl
from pyviennacl import backend
from pyviennacl import opencl
from pyviennacl import atidlas
from dataset import generate_dataset
import utils
import vclio
@@ -45,99 +46,72 @@ TYPES = { 'vector-axpy': {'template':vcl.atidlas.VectorAxpyTemplate,
'perf-index': lambda x: 2*x[1][0]*x[1][1]*x[1][2]/x[2]*1e-9,
'perf-measure': 'GFLOP/s'} }
def parameter_space(operation):
simd = [1, 2, 4, 8]
pow2_1D = [2**k for k in range(12)]
pow2_2D = [2**i for i in range(8)]
pow2_2D_unrolled = [2**i for i in range(8)]
FetchingPolicy = vcl.atidlas.FetchingPolicy
fetch = [FetchingPolicy.FETCH_FROM_LOCAL, FetchingPolicy.FETCH_FROM_GLOBAL_CONTIGUOUS, FetchingPolicy.FETCH_FROM_GLOBAL_STRIDED]
if operation == 'vector-axpy': return [simd, pow2_1D, pow2_1D, fetch]
if operation == 'reduction': return [simd, pow2_1D, pow2_1D, fetch]
if operation == 'matrix-axpy': return [simd, pow2_2D, pow2_2D, pow2_2D, pow2_2D, fetch]
if operation == 'row-wise-reduction': return [simd, pow2_2D, pow2_2D, pow2_1D, fetch]
if operation == 'matrix-product': return [simd, pow2_2D, pow2_2D, pow2_2D, pow2_2D_unrolled, pow2_2D_unrolled, pow2_2D_unrolled, fetch, fetch, [0] + pow2_2D, [0] + pow2_2D]
def do_tuning(config_fname, spec_fname, viennacl_root):
config = ConfigObj(config_fname, configspec=spec_fname)
map_to_list = lambda T: list(map(T[0], T[1] if isinstance(T[1], list) else [T[1]]))
for operation in ['vector-axpy', 'matrix-axpy', 'row-wise-reduction', 'matrix-product']:
tmp_folder = config['tmp-folder'] if 'tmp-folder' in config else ""
if operation in config:
p = config[operation]
confdevices = p['devices']
devices = utils.DEVICES_PRESETS[confdevices] if confdevices in utils.DEVICES_PRESETS else [utils.all_devices[int(i)] for i in confdevices]
precisions = map_to_list((str, p['precision']))
datatypes = [DATATYPES[k] for k in precisions]
s = map_to_list((int, p['size']))
for datatype, device in itertools.product(datatypes, devices):
ctx = cl.Context([device])
ctx = vcl.backend.Context(ctx)
device = ctx.current_device
if datatype is vcl.float64 and not device.double_fp_config:
sys.stderr.write('Warning : The device ' + device.name + ' does not support double precision! Skipping ...')
continue
pairs = []
def execute(node, other_params):
print('-----')
print(' '.join(map(str, ("Now tuning:", datatype.__name__, '-', operation, '-'.join(other_params), '[' + device.name, '(' + device.platform.name + ')]'))))
tmp_file = os.path.join(tmp_folder, utils.sanitize_string(device.name) + "-" + datatype.__name__ + "-" + operation + '-'.join(other_params) + ".dat")
if tmp_folder:
print('Saving history to ' + tmp_file)
fname = tmp_file
else:
fname = os.devnull
with open(fname, "w+") as archive:
with vcl.Statement(node) as statement:
result = optimize.genetic(statement, ctx, TYPES[operation]['template'], lambda p: TYPES[operation]['template'](p, *other_params),
TYPES[operation]['parameter-names'], parameter_space(operation), lambda t: TYPES[operation]['perf-index']([datatype().itemsize, s, t]), TYPES[operation]['perf-measure'], archive)
if result and viennacl_root:
vclio.generate_viennacl_headers(viennacl_root, device, datatype, operation, other_params, result[1])
if operation=='vector-axpy':
x = vcl.Vector(s[0], context=ctx, dtype=datatype)
y = vcl.Vector(s[0], context=ctx, dtype=datatype)
execute(vcl.ElementProd(vcl.exp(x + y),vcl.cos(x + y)), ())
if operation=='matrix-axpy':
A = vcl.Matrix(s, context=ctx, dtype=datatype)
B = vcl.Matrix(s, context=ctx, dtype=datatype)
execute(A+B, ())
if operation=='row-wise-reduction':
layouts = map_to_list((str,p['layout']))
if 'all' in layouts:
layouts = ['N', 'T']
for A_trans in layouts:
A = vcl.Matrix(s if A_trans=='N' else s[::-1], context=ctx, dtype=datatype, layout=vcl.COL_MAJOR)
x = vcl.Vector(s[1] if A_trans=='N' else s[0], context=ctx, dtype=datatype)
LHS = A if A_trans=='N' else A.T
execute(LHS*x, ())
if operation=='matrix-product':
layouts = map_to_list((str,p['layout']))
if 'all' in layouts:
layouts = ['NN', 'NT', 'TN', 'TT']
for layout in layouts:
A_trans = layout[0]
B_trans = layout[1]
A = vcl.Matrix((s[0], s[1]) if A_trans=='N' else (s[1],s[0]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR);
B = vcl.Matrix((s[1], s[2]) if B_trans=='N' else (s[2],s[1]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR);
LHS = A if A_trans=='N' else A.T
RHS = B if B_trans=='N' else B.T
alpha = vcl.HostScalar(1.0, context=ctx, dtype = datatype)
beta = vcl.HostScalar(1.0, context=ctx, dtype = datatype)
C = vcl.Matrix((s[0], s[2]), context=ctx, dtype = datatype, layout=vcl.COL_MAJOR)
execute(vcl.Assign(C,LHS*RHS*alpha + C*beta),(A_trans, B_trans))
if operation in config:
p = config[operation]
confdevices = p['devices']
devices = utils.DEVICES_PRESETS[confdevices] if confdevices in utils.DEVICES_PRESETS else [utils.all_devices[int(i)] for i in confdevices]
precisions = map_to_list((str, p['precision']))
datatypes = [DATATYPES[k] for k in precisions]
#Iterate through the datatypes and the devices
for datatype, device in itertools.product(datatypes, devices):
ctx = cl.Context([device])
ctx = vcl.backend.Context(ctx)
device = ctx.current_device
#Check data-type
if datatype is vcl.float64 and not device.double_fp_config:
sys.stderr.write('Warning : The device ' + device.name + ' does not support double precision! Skipping ...')
continue
#Helper
def execute(node, other_params, sizes, fname = os.devnull):
print('-----')
print(' '.join(map(str, ("Now tuning:", datatype.__name__, '-', operation, '-'.join(other_params), '[' + device.name, '(' + device.platform.name + ')] for sizes', sizes))))
with open(fname, "w+") as archive:
with vcl.Statement(node) as statement:
return optimize.genetic(statement, ctx, TYPES[operation]['template'], lambda p: TYPES[operation]['template'](p, *other_params),
TYPES[operation]['parameter-names'], lambda t: TYPES[operation]['perf-index']([datatype().itemsize, sizes, t]), TYPES[operation]['perf-measure'], archive)
s = map_to_list((int, p['size']))
#Vector AXPY
if operation=='vector-axpy':
x = vcl.Vector(s[0], context=ctx, dtype=datatype)
y = vcl.Vector(s[0], context=ctx, dtype=datatype)
execute(vcl.ElementProd(vcl.exp(x + y),vcl.cos(x + y)), ())
#Matrix AXPY
if operation=='matrix-axpy':
A = vcl.Matrix(s, context=ctx, dtype=datatype)
B = vcl.Matrix(s, context=ctx, dtype=datatype)
execute(A+B, ())
#Row-wise reduction
if operation=='row-wise-reduction':
layouts = map_to_list((str,p['layout']))
if 'all' in layouts:
layouts = ['N', 'T']
for A_trans in layouts:
A = vcl.Matrix(s if A_trans=='N' else s[::-1], context=ctx, dtype=datatype, layout=vcl.COL_MAJOR)
x = vcl.Vector(s[1] if A_trans=='N' else s[0], context=ctx, dtype=datatype)
LHS = A if A_trans=='N' else A.T
execute(LHS*x, ())
#Matrix Product
if operation=='matrix-product':
layouts = map_to_list((str,p['layout']))
if 'all' in layouts:
layouts = ['NN', 'NT', 'TN', 'TT']
for layout in layouts:
def execution_handler(sizes, fname):
A_trans = layout[0]
B_trans = layout[1]
A = vcl.Matrix((sizes[0], sizes[1]) if A_trans=='N' else (sizes[1],sizes[0]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR);
B = vcl.Matrix((sizes[1], sizes[2]) if B_trans=='N' else (sizes[2],sizes[1]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR);
LHS = A if A_trans=='N' else A.T
RHS = B if B_trans=='N' else B.T
alpha = vcl.HostScalar(1.0, context=ctx, dtype = datatype)
beta = vcl.HostScalar(1.0, context=ctx, dtype = datatype)
C = vcl.Matrix((sizes[0], sizes[2]), context=ctx, dtype = datatype, layout=vcl.COL_MAJOR)
execute(vcl.Assign(C,LHS*RHS*alpha + C*beta),(A_trans, B_trans), sizes, fname)
generate_dataset(operation, execution_handler)
if __name__ == "__main__":