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Porting GA for all the operations

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Philippe Tillet
2014-10-03 09:29:45 +02:00
parent 2f6d41f661
commit 044419f9f0
6 changed files with 76 additions and 78 deletions
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42
autotune/python/autotune.py
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@@ -24,39 +24,35 @@ DATATYPES = { 'single' : vcl.float32,
'double' : vcl.float64 }
TYPES = { 'vector-axpy': {'template':vcl.atidlas.VectorAxpyTemplate,
'parameter-names':['simd-width', 'local-size-0', 'num-groups-0', 'fetch'],
'perf-index':lambda x: 3*x[0]*x[1][0]/x[2]*1e-9,
'perf-measure':'GB/s'},
'matrix-axpy': {'template':vcl.atidlas.MatrixAxpyTemplate,
'parameter-names':['simd-width', 'local-size-0', 'local-size-1', 'num-groups-0', 'num-groups-1', 'fetch'],
'perf-index':lambda x: 3*x[0]*x[1][0]*x[1][1]/x[2]*1e-9,
'perf-measure':'GB/s'},
'reduction': {'template':vcl.atidlas.ReductionTemplate,
'parameter-names':['simd-width', 'local-size-0', 'num-groups-0', 'fetch'],
'perf-index':lambda x: 2*x[0]*x[1][0]*x[1][1]/x[2]*1e-9,
'perf-measure':'GB/s'},
'row-wise-reduction': {'template':vcl.atidlas.RowWiseReductionTemplate,
'parameter-names':['simd-width', 'local-size-0', 'local-size-1', 'num-groups-0', 'fetch'],
'perf-index':lambda x: x[0]*x[1][0]*x[1][1]/x[2]*1e-9,
'perf-measure':'GB/s'},
'matrix-product': {'template':vcl.atidlas.MatrixProductTemplate,
'parameter-names':['simd-width', 'local-size-0', 'kL', 'local-size-1', 'mS', 'kS', 'nS', 'A-fetch-policy', 'B-fetch-policy', 'local-fetch-size-0', 'local-fetch-size-1'],
'perf-index': lambda x: 2*x[1][0]*x[1][1]*x[1][2]/x[2]*1e-9,
'perf-measure': 'GFLOP/s'} }
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]]))
def map_to_list(T, x):
return list(map(T, x if isinstance(x, list) else [x]))
for operation in ['vector-axpy', 'matrix-axpy', 'row-wise-reduction', 'matrix-product']:
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']))
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):
@@ -68,18 +64,23 @@ def do_tuning(config_fname, spec_fname, viennacl_root):
sys.stderr.write('Warning : The device ' + device.name + ' does not support double precision! Skipping ...')
continue
#Helper
def execute(statement, other_params, sizes, fname = os.devnull):
def execute(device, statement, other_params, sizes, fname = os.devnull, parameters = None):
if parameters:
TemplateType = TYPES[operation]['template']
return tools.benchmark(TemplateType(TemplateType.Parameters(*parameters),*other_params), statement, device)
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:
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']))
return optimize.genetic(statement, device, TYPES[operation]['template'], lambda p: TYPES[operation]['template'](p, *other_params),
lambda t: TYPES[operation]['perf-index']([datatype().itemsize, sizes, t]), TYPES[operation]['perf-measure'], archive)
#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)), ())
def execution_handler(sizes, fname=os.devnull, parameters=None):
x = vcl.Vector(sizes[0], context=ctx, dtype=datatype)
y = vcl.Vector(sizes[0], context=ctx, dtype=datatype)
return execute(device, vcl.Statement(vcl.ElementProd(vcl.exp(x + y),vcl.cos(x + y))), (), sizes, fname, parameters)
if 'size' in p:
profile = execution_handler(map_to_list(int, p['size']))
#Matrix AXPY
if operation=='matrix-axpy':
A = vcl.Matrix(s, context=ctx, dtype=datatype)
@@ -112,13 +113,12 @@ def do_tuning(config_fname, spec_fname, viennacl_root):
beta = vcl.HostScalar(1.0, context=ctx, dtype = datatype)
C = vcl.Matrix((sizes[0], sizes[2]), context=ctx, dtype = datatype, layout=vcl.COL_MAJOR)
statement = vcl.Statement(vcl.Assign(C,LHS*RHS*alpha + C*beta))
if parameters:
TemplateType = TYPES[operation]['template']
return tools.benchmark(TemplateType(TemplateType.Parameters(*parameters),A_trans,B_trans), statement, device)
else:
return execute(statement,(A_trans, B_trans), sizes, fname)
X, Y, profiles = generate_dataset(TYPES[operation]['template'], execution_handler)
train_model(X, Y, profiles)
return execute(device, statement,(A_trans, B_trans), sizes, fname, parameters)
if 'size' in p:
profile = execution_handler(map(int, p['size']))
else:
X, Y, profiles = generate_dataset(TYPES[operation]['template'], execution_handler)
train_model(X, Y, profiles)