def _emit_build_profiling(self):
if not is_log_enabled_for('PERF'):
return
# Rounder to K decimal places
fround = lambda i, n=100: ceil(i * n) / n
timings = self._profiler.py_timers.copy()
tot = timings.pop('op-compile')
perf("Operator `%s` generated in %.2f s" % (self.name, fround(tot)))
max_hotspots = 3
threshold = 20.
def _emit_timings(timings, indent=''):
timings.pop('total', None)
entries = sorted(timings,
key=lambda i: timings[i]['total'],
reverse=True)
for i in entries[:max_hotspots]:
v = fround(timings[i]['total'])
perc = fround(v / tot * 100, n=10)
if perc > threshold:
perf("%s%s: %.2f s (%.1f %%)" %
(indent, i.lstrip('_'), v, perc))
_emit_timings(timings[i], ' ' * len(indent) + ' * ')
_emit_timings(timings, ' * ')
if self._profiler._ops:
ops = ['%d --> %d' % i for i in self._profiler._ops]
perf("Flops reduction after symbolic optimization: [%s]" %
' ; '.join(ops))
def _emit_build_profiling(self):
if not is_log_enabled_for('PERF'):
return
# Rounder to K decimal places
fround = lambda i, n=100: ceil(i * n) / n
timings = self._profiler.py_timers.copy()
tot = timings.pop('op-compile')
perf("Operator `%s` generated in %.2f s" % (self.name, fround(tot)))
max_hotspots = 3
for i in sorted(timings, key=timings.get, reverse=True)[:max_hotspots]:
v = fround(timings[i])
perc = fround(v / tot * 100, n=10)
if perc > 20.:
perf("- [Hotspot] %s: %.2f s (%.1f %%)" %
(i.lstrip('_'), v, perc))
def _emit_apply_profiling(self, args):
"""Produce a performance summary of the profiled sections."""
# Rounder to 2 decimal places
fround = lambda i: ceil(i * 100) / 100
info("Operator `%s` ran in %.2f s" %
(self.name, fround(self._profiler.py_timers['apply'])))
summary = self._profiler.summary(args,
self._dtype,
reduce_over='apply')
if not is_log_enabled_for('PERF'):
# Do not waste time
return summary
if summary.globals:
# Note that with MPI enabled, the global performance indicators
# represent "cross-rank" performance data
metrics = []
v = summary.globals.get('vanilla')
if v is not None:
metrics.append("OI=%.2f" % fround(v.oi))
metrics.append("%.2f GFlops/s" % fround(v.gflopss))
v = summary.globals.get('fdlike')
if v is not None:
metrics.append("%.2f GPts/s" % fround(v.gpointss))
if metrics:
perf("Global performance: [%s]" % ', '.join(metrics))
perf("Local performance:")
indent = " " * 2
else:
indent = ""
# Emit local, i.e. "per-rank" performance. Without MPI, this is the only
# thing that will be emitted
for k, v in summary.items():
rank = "[rank%d]" % k.rank if k.rank is not None else ""
oi = "OI=%.2f" % fround(v.oi)
gflopss = "%.2f GFlops/s" % fround(v.gflopss)
gpointss = "%.2f GPts/s" % fround(
v.gpointss) if v.gpointss else None
metrics = ", ".join(i for i in [oi, gflopss, gpointss]
if i is not None)
itershapes = [
",".join(str(i) for i in its) for its in v.itershapes
]
if len(itershapes) > 1:
itershapes = ",".join("<%s>" % i for i in itershapes)
elif len(itershapes) == 1:
itershapes = itershapes[0]
else:
itershapes = ""
name = "%s%s<%s>" % (k.name, rank, itershapes)
perf("%s* %s ran in %.2f s [%s]" %
(indent, name, fround(v.time), metrics))
for n, time in summary.subsections.get(k.name, {}).items():
perf("%s+ %s ran in %.2f s [%.2f%%]" %
(indent * 2, n, time, fround(time / v.time * 100)))
# Emit performance mode and arguments
perf_args = {}
for i in self.input + self.dimensions:
if not i.is_PerfKnob:
continue
try:
perf_args[i.name] = args[i.name]
except KeyError:
# Try with the aliases
for a in i._arg_names:
if a in args:
perf_args[a] = args[a]
break
perf("Performance[mode=%s] arguments: %s" %
(self._state['optimizations'], perf_args))
return summary
def _emit_apply_profiling(self, args):
"""Produce a performance summary of the profiled sections."""
# Rounder to 2 decimal places
fround = lambda i: ceil(i * 100) / 100
info("Operator `%s` run in %.2f s" %
(self.name, fround(self._profiler.py_timers['apply'])))
summary = self._profiler.summary(args,
self._dtype,
reduce_over='apply')
if not is_log_enabled_for('PERF'):
# Do not waste time
return summary
if summary.globals:
indent = " " * 2
perf("Global performance indicators")
# With MPI enabled, the 'vanilla' entry contains "cross-rank" performance data
v = summary.globals.get('vanilla')
if v is not None:
gflopss = "%.2f GFlops/s" % fround(v.gflopss)
gpointss = "%.2f GPts/s" % fround(
v.gpointss) if v.gpointss else None
metrics = ", ".join(i for i in [gflopss, gpointss]
if i is not None)
perf(
"%s* Operator `%s` with OI=%.2f computed in %.2f s [%s]" %
(indent, self.name, fround(v.oi), fround(v.time), metrics))
v = summary.globals.get('fdlike')
if v is not None:
perf("%s* Achieved %.2f FD-GPts/s" % (indent, v.gpointss))
perf("Local performance indicators")
else:
indent = ""
# Emit local, i.e. "per-rank" performance. Without MPI, this is the only
# thing that will be emitted
for k, v in summary.items():
rank = "[rank%d]" % k.rank if k.rank is not None else ""
gflopss = "%.2f GFlops/s" % fround(v.gflopss)
gpointss = "%.2f GPts/s" % fround(
v.gpointss) if v.gpointss else None
metrics = ", ".join(i for i in [gflopss, gpointss]
if i is not None)
itershapes = [
",".join(str(i) for i in its) for its in v.itershapes
]
if len(itershapes) > 1:
name = "%s%s<%s>" % (k.name, rank, ",".join(
"<%s>" % i for i in itershapes))
perf("%s* %s with OI=%.2f computed in %.2f s [%s]" %
(indent, name, fround(v.oi), fround(v.time), metrics))
elif len(itershapes) == 1:
name = "%s%s<%s>" % (k.name, rank, itershapes[0])
perf("%s* %s with OI=%.2f computed in %.2f s [%s]" %
(indent, name, fround(v.oi), fround(v.time), metrics))
else:
name = k.name
perf("%s* %s%s computed in %.2f s" %
(indent, name, rank, fround(v.time)))
# Emit relevant configuration values
perf("Configuration: %s" % self._state['optimizations'])
# Emit relevant performance arguments
perf_args = {}
for i in self.input + self.dimensions:
if not i.is_PerfKnob:
continue
try:
perf_args[i.name] = args[i.name]
except KeyError:
# Try with the aliases
for a in i._arg_names:
if a in args:
perf_args[a] = args[a]
break
perf("Performance arguments: %s" % perf_args)
return summary
