def from_csv(path, group_by=None, filter_by=None):
datafilter = {
"suite": suite_filter
}.get(filter_by, None)
getgroup = {
"class": getclass,
"suite": getsuite,
"prog": getprog,
"benchmark": getbenchmark,
}.get(group_by, lambda x: "None")
data = pd.read_csv(smith.assert_exists(path))
if datafilter:
# TODO: data filter
pass
# Add group column.
data["Group"] = [getgroup(d) for d in data.to_dict(orient='records')]
# If data is labelled.
if "F1:transfer/(comp+mem)" in data:
# Add normalized feature columns.
data["F1_norm"] = normalize(data["F1:transfer/(comp+mem)"])
data["F2_norm"] = normalize(data["F2:coalesced/mem"])
data["F3_norm"] = normalize(data["F3:(localmem/mem)*avgws"])
data["F4_norm"] = normalize(data["F4:comp/mem"])
# Add eigenvectors.
data["E1"], data["E2"], data["E3"], data["E4"] = _eigenvectors(data)
return data
def from_csv(path):
data = pd.read_csv(smith.assert_exists(path),
names=["benchmark", "dataset", "kernel",
"wgsize", "transfer", "runtime", "ci"])
return data
def opt():
return smith.assert_exists(llvm_path(), "build", "bin", "opt",
exception=ConfigException)
def clang():
return smith.assert_exists(llvm_path(), "build", "bin", "clang",
exception=ConfigException)
def rewriter():
return smith.assert_exists(phd_root(), "src", "smith", "native", "rewriter",
exception=ConfigException)
def llvm_path():
return smith.assert_exists(phd_root(), "tools", "llvm",
exception=ConfigException)
def libclc():
return smith.assert_exists(phd_root(), "extern", "libclc",
exception=ConfigException)
def clsmith():
return smith.assert_exists(clsmith_path(), "build", "CLSmith",
exception=ConfigException)
def clsmith_path():
return smith.assert_exists("~/phd/extern/clsmith",
exception=ConfigException)
def verify_parboil(path):
smith.assert_exists(path, exception=ConfigException)
smith.assert_exists(path, 'benchmarks', exception=ConfigException)
smith.assert_exists(path, 'datasets', exception=ConfigException)
smith.assert_exists(path, 'parboil', exception=ConfigException)
return path
def torch_rnn_path():
return smith.assert_exists("~/src/torch-rnn", exception=ConfigException)
def phd_root():
return smith.assert_exists("~/phd", exception=ConfigException)