def main(params, njobs, baseline, paths_to_models, chunk_size):
"""
Update parameter file with new optimal parameter predictions given newly trained decision trees
"""
# ===============================================================================
# Load GPU and autotuning properties
assert (os.path.basename(params) in gpu_architectures.keys()
), "Cannot find compute version for file " + str(params)
arch_code = gpu_architectures[os.path.basename(params)]
with open("../kernels/gpu_properties.json") as f:
gpu_properties = json.load(f)[arch_code]
with open("../kernels/autotuning_properties.json") as f:
autotuning_properties = json.load(f)
# Load autotuned kernel parameters
with open(params) as f:
all_kernels = [
params_dict_to_kernel(**params) for params in json.load(f)
]
print("libsmm_acc: Found %d existing parameter sets." % len(all_kernels))
autotuned_mnks = [(k.m, k.n, k.k) for k in all_kernels if k.autotuned]
autotuned_kernels_ = [k for k in all_kernels if k.autotuned]
autotuned_kernels = dict(zip(autotuned_mnks, autotuned_kernels_))
# ===============================================================================
# Construct the list of (m,n,k)-triplets for which parameter sets should be made available to libcusmm
mnks = combinations(list(range(4, 46)))
mnks = set.union(set(mnks), set(autotuned_kernels.keys()))
# ===============================================================================
# Compute parameter sets
mnks_to_predict = list()
kernels_to_print = dict()
for m, n, k in mnks:
if (m, n, k) in autotuned_kernels.keys():
kernels_to_print[(m, n, k)] = autotuned_kernels[(m, n, k)]
else:
mnks_to_predict.append((m, n, k))
if baseline:
kernels = get_baseline_kernels(mnks_to_predict, gpu_properties,
autotuning_properties)
else:
kernels = get_optimal_kernels(
mnks_to_predict,
njobs,
chunk_size,
paths_to_models,
gpu_properties,
autotuning_properties,
1,
)
kernels_to_print.update(kernels)
# ===============================================================================
# Write to file
with open(params, "w") as f:
s = json.dumps([
kernels_to_print[kernel].as_dict_for_parameters_json
for kernel in sorted(kernels_to_print.keys())
])
s = s.replace("}, ", "},\n")
s = s.replace("[", "[\n")
s = s.replace("]", "\n]")
f.write(s)
print("Wrote new predicted parameters to file", params)
def main(
param_fn,
compiler,
cpus_per_node,
max_num_nodes,
blocksizes,
blocks_from_param_file,
tune_dir,
):
# Read existing parameters
assert (os.path.basename(param_fn) in gpu_architectures.keys(
)), "Cannot find GPU architecture for file " + os.path.basename(param_fn)
arch_code = gpu_architectures[os.path.basename(param_fn)]
with open("../kernels/gpu_properties.json") as f:
gpu_properties = json.load(f)[arch_code]
with open("../kernels/autotuning_properties.json") as f:
autotuning_properties = json.load(f)
with open(param_fn) as f:
all_kernels = [
params_dict_to_kernel(**params) for params in json.load(f)
]
print("Reading parameters from %s" % param_fn)
autotuned_kernels = [k for k in all_kernels if k.autotuned]
predicted_kernels = [k for k in all_kernels if not k.autotuned]
print(
"libsmm_acc: found %d existing parameter sets, of which %d are autotuned and %d are predicted."
% (len(all_kernels), len(autotuned_kernels), len(predicted_kernels)))
# Get blocksizes to be autotuned
if blocks_from_param_file: # open and read file
with open(blocksizes) as f:
all_kernels_ref = [
params_dict_to_kernel(**params) for params in json.load(f)
]
print("Reading parameters to autotune from %s" % blocksizes)
triples = [(k.m, k.n, k.k) for k in all_kernels_ref if k.autotuned]
else:
assert len(set(blocksizes)) == len(blocksizes)
blocksizes.sort()
# Get (m, n, k) triplets to be autotuned
triples = combinations(*blocksizes)
print("Requested to autotune %d triplets" % len(triples))
for (m, n, k) in triples:
existing = [
kern for kern in autotuned_kernels if kern.can_handle(m, n, k)
]
if existing:
print(
"Found existing autotuned parameter set for %dx%dx%d, skipping."
% (m, n, k))
continue
outdir = os.path.join(tune_dir, "tune_%dx%dx%d/" % (m, n, k))
if os.path.exists(outdir):
print("Directory %s exists already, skipping." % outdir)
continue
os.mkdir(outdir)
gen_benchmark(outdir, gpu_properties, autotuning_properties, compiler,
m, n, k)
gen_jobfile(outdir, compiler, m, n, k, cpus_per_node, max_num_nodes)
gen_makefile(outdir, compiler, arch_code)