def __init__(self, job):
super(SlurmCgroupMemTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostcounts = {}
self._expectedcgroup = "/slurm/uid_{0}/job_{1}".format(
job.acct['uid'], job.job_id)
def __init__(self, job):
super(CgroupMemTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostcounts = {}
if job.acct['resource_manager'] == 'pbs':
self._expectedcgroup = "/torque/{0}".format(job.job_id)
elif job.acct['resource_manager'] == 'slurm':
self._expectedcgroup = "/slurm/uid_{0}/job_{1}".format(
job.acct['uid'], job.job_id)
else:
raise NotApplicableError
def __init__(self, job):
super(SimdInsTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
self._error = None
class SimdInsTimeseries(Plugin):
""" Generate the CPU usage as a timeseries data """
name = property(lambda x: "simdins")
mode = property(lambda x: "timeseries")
requiredMetrics = property(lambda x: [SNB_METRICS, NHM_METRICS])
optionalMetrics = property(lambda x: [])
derivedMetrics = property(lambda x: [])
def __init__(self, job):
super(SimdInsTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
self._error = None
def process(self, nodemeta, timestamp, data, description):
if len(data[0]) > 0 and data[0][0] == 0:
# If active == 0 then the PMDA was switched off due to user request
self._error = ProcessingError.RAW_COUNTER_UNAVAILABLE
return False
if len(data[1]) == 0:
# Ignore timesteps where data was not available
return True
hostidx = nodemeta.nodeindex
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[hostidx] = numpy.empty(
(TimeseriesAccumulator.MAX_DATAPOINTS, len(data[1])))
self._hostdevnames[hostidx] = dict(
(str(k), v)
for k, v in zip(description[1][0], description[1][1]))
if len(data) == len(NHM_METRICS):
flops = numpy.array(data[1])
else:
flops = 4.0 * data[1] + 2.0 * data[2] + data[3] + data[4]
insertat = self._data.adddata(hostidx, timestamp, numpy.sum(flops))
if insertat != None:
self._hostdata[hostidx][insertat] = flops
if insertat > 1:
if numpy.any(
flops - self._hostdata[hostidx][insertat - 1] < 0.0):
self._error = ProcessingError.PMDA_RESTARTED_DURING_JOB
return False
return True
def results(self):
if self._error != None:
return {"error": self._error}
values = self._data.get()
rates = numpy.diff(values[:, :, 1]) / numpy.diff(values[:, :, 0])
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
sortarr = numpy.argsort(rates.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], rates),
"max": self.collatedata(sortarr[:, -1], rates),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2],
rates),
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {"times": values[0, 1:, 0].tolist(), "hosts": {}}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = rates[hostidx, :].tolist()
retdata['hosts'][str(hostidx)]['dev'] = {}
for devid in self._hostdevnames[hostidx].iterkeys():
dpnts = len(values[hostidx, :, 0])
retdata['hosts'][str(hostidx)]['dev'][devid] = (
numpy.diff(self._hostdata[hostidx][:dpnts, devid]) /
numpy.diff(values[hostidx, :, 0])).tolist()
retdata['hosts'][str(
hostidx)]['names'] = self._hostdevnames[hostidx]
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
def __init__(self, job):
super(RateConvertingTimeseriesPlugin, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
class RateConvertingTimeseriesPlugin(Plugin):
"""
A base abstract class for generating a timeseries summary for values that should
be converted to rates, one per node.
The plugin name, list of required metrics and generator function must be provided by the implementation
"""
__metaclass__ = ABCMeta
mode = property(lambda x: "timeseries")
def __init__(self, job):
super(RateConvertingTimeseriesPlugin, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
@abstractmethod
def computetimepoint(self, data):
""" Called with the data for each timepoint on each host """
pass
def process(self, nodemeta, timestamp, data, description):
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[nodemeta.nodeindex] = 1
datum = self.computetimepoint(data)
if datum != None:
self._data.adddata(nodemeta.nodeindex, timestamp, datum)
def results(self):
if len(self._hostdata) != self._job.nodecount:
return {"error": ProcessingError.INSUFFICIENT_HOSTDATA}
values = self._data.get()
if len(values[0, :, 0]) < 3:
return {"error": ProcessingError.JOB_TOO_SHORT}
rates = numpy.diff(values[:, :, 1]) / numpy.diff(values[:, :, 0])
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
sortarr = numpy.argsort(rates.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], rates),
"max": self.collatedata(sortarr[:, -1], rates),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2],
rates),
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {"times": values[0, 1:, 0].tolist(), "hosts": {}}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = rates[hostidx, :].tolist()
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
def __init__(self, job):
super(CpuUserTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
class GpuUsageTimeseries(Plugin):
""" Generate the CPU usage as a timeseries data """
name = property(lambda x: "gpu_usage")
mode = property(lambda x: "timeseries")
requiredMetrics = property(lambda x: ["nvidia.gpuactive"])
optionalMetrics = property(lambda x: [])
derivedMetrics = property(lambda x: [])
def __init__(self, job):
super(GpuUsageTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
def process(self, nodemeta, timestamp, data, description):
hostidx = nodemeta.nodeindex
if len(data[0]) == 0:
# Skip data point with no data
return True
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[hostidx] = numpy.empty((TimeseriesAccumulator.MAX_DATAPOINTS, len(data[0])))
self._hostdevnames[hostidx] = dict((str(k), str(v)) for k, v in zip(description[0][0], description[0][1]))
avg_usage = numpy.mean(data[0])
insertat = self._data.adddata(hostidx, timestamp, avg_usage)
if insertat != None:
self._hostdata[hostidx][insertat] = data[0]
return True
def results(self):
values = self._data.get()
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
memdata = values[:, :, 1]
sortarr = numpy.argsort(memdata.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], memdata),
"max": self.collatedata(sortarr[:, -1], memdata),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2], memdata),
"times": values[0, :, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {
"times": values[0, :, 0].tolist(),
"hosts": {}
}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = values[hostidx, :, 1].tolist()
retdata['hosts'][str(hostidx)]['dev'] = {}
for devid in self._hostdevnames[hostidx].iterkeys():
dpnts = len(values[hostidx, :, 0])
retdata['hosts'][str(hostidx)]['dev'][devid] = self._hostdata[hostidx][:dpnts, int(devid)].tolist()
retdata['hosts'][str(hostidx)]['names'] = self._hostdevnames[hostidx]
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
class SimdInsTimeseries(Plugin):
""" Generate the CPU usage as a timeseries data """
name = property(lambda x: "simdins")
mode = property(lambda x: "timeseries")
requiredMetrics = property(lambda x: [SNB_METRICS, NHM_METRICS])
optionalMetrics = property(lambda x: [])
derivedMetrics = property(lambda x: [])
def __init__(self, job):
super(SimdInsTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
self._error = None
def process(self, nodemeta, timestamp, data, description):
if len(data[0]) > 0 and data[0][0] == 0:
# If active == 0 then the PMDA was switched off due to user request
self._error = ProcessingError.RAW_COUNTER_UNAVAILABLE
return False
if len(data[1]) == 0:
# Ignore timesteps where data was not available
return True
hostidx = nodemeta.nodeindex
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[hostidx] = numpy.empty((TimeseriesAccumulator.MAX_DATAPOINTS, len(data[1])))
self._hostdevnames[hostidx] = dict((str(k), v) for k, v in zip(description[1][0], description[1][1]))
if len(data) == len(NHM_METRICS):
flops = numpy.array(data[1])
else:
flops = 4.0 * data[1] + 2.0 * data[2] + data[3] + data[4]
insertat = self._data.adddata(hostidx, timestamp, numpy.sum(flops))
if insertat != None:
self._hostdata[hostidx][insertat] = flops
if insertat > 1:
if numpy.any(flops - self._hostdata[hostidx][insertat-1] < 0.0):
self._error = ProcessingError.PMDA_RESTARTED_DURING_JOB
return False
return True
def results(self):
if self._error != None:
return {"error": self._error}
values = self._data.get()
rates = numpy.diff(values[:, :, 1]) / numpy.diff(values[:, :, 0])
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
sortarr = numpy.argsort(rates.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], rates),
"max": self.collatedata(sortarr[:, -1], rates),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2], rates),
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = rates[hostidx, :].tolist()
retdata['hosts'][str(hostidx)]['dev'] = {}
for devid in self._hostdevnames[hostidx].iterkeys():
dpnts = len(values[hostidx, :, 0])
retdata['hosts'][str(hostidx)]['dev'][devid] = (numpy.diff(self._hostdata[hostidx][:dpnts, devid]) / numpy.diff(values[hostidx, :, 0])).tolist()
retdata['hosts'][str(hostidx)]['names'] = self._hostdevnames[hostidx]
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
def __init__(self, job):
super(RateConvertingTimeseriesPlugin, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
class RateConvertingTimeseriesPlugin(Plugin):
"""
A base abstract class for generating a timeseries summary for values that should
be converted to rates, one per node.
The plugin name, list of required metrics and generator function must be provided by the implementation
"""
__metaclass__ = ABCMeta
mode = property(lambda x: "timeseries")
def __init__(self, job):
super(RateConvertingTimeseriesPlugin, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
@abstractmethod
def computetimepoint(self, data):
""" Called with the data for each timepoint on each host """
pass
def process(self, nodemeta, timestamp, data, description):
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[nodemeta.nodeindex] = 1
self._data.adddata(nodemeta.nodeindex, timestamp, self.computetimepoint(data))
def results(self):
if len(self._hostdata) != self._job.nodecount:
return {"error": ProcessingError.INSUFFICIENT_HOSTDATA}
values = self._data.get()
if len(values[0, :, 0]) < 3:
return {"error": ProcessingError.JOB_TOO_SHORT}
rates = numpy.diff(values[:, :, 1]) / numpy.diff(values[:, :, 0])
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
sortarr = numpy.argsort(rates.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], rates),
"max": self.collatedata(sortarr[:, -1], rates),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2], rates),
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = rates[hostidx, :].tolist()
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
class CgroupMemTimeseries(Plugin):
""" Generate timeseries summary for memory usage viewed from CGroup
This code is SLURM-specific because of the SLURM cgroup naming convention.
"""
name = property(lambda x: "process_mem_usage")
mode = property(lambda x: "timeseries")
requiredMetrics = property(lambda x: ["cgroup.memory.usage"])
optionalMetrics = property(lambda x: [])
derivedMetrics = property(lambda x: [])
def __init__(self, job):
super(CgroupMemTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostcounts = {}
if job.acct['resource_manager'] == 'pbs':
self._expectedcgroup = "/torque/{0}".format(job.job_id)
elif job.acct['resource_manager'] == 'slurm':
self._expectedcgroup = "/slurm/uid_{0}/job_{1}".format(
job.acct['uid'], job.job_id)
else:
raise NotApplicableError
def process(self, nodemeta, timestamp, data, description):
hostidx = nodemeta.nodeindex
if len(data[0]) == 0:
# Skip data point with no data
return True
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[hostidx] = numpy.empty(
(TimeseriesAccumulator.MAX_DATAPOINTS, 1))
self._hostcounts[hostidx] = {'missing': 0, 'present': 0}
try:
dataidx = None
for idx, desc in enumerate(description[0][1]):
if re.match(r"^" + re.escape(self._expectedcgroup) + r"($|\.)",
desc):
dataidx = idx
break
# No cgroup info at this datapoint
if dataidx is None:
return True
nodemem_gb = data[0][dataidx] / 1073741824.0
self._hostcounts[hostidx]['present'] += 1
except ValueError:
self._hostcounts[hostidx]['missing'] += 1
# No cgroup info at this datapoint
return True
insertat = self._data.adddata(hostidx, timestamp, nodemem_gb)
if insertat != None:
self._hostdata[hostidx][insertat] = nodemem_gb
return True
def results(self):
if len(self._hostdata) != self._job.nodecount:
return {'error': ProcessingError.RAW_COUNTER_UNAVAILABLE}
for hcount in self._hostcounts.itervalues():
if hcount['missing'] > hcount['present']:
return {'error': ProcessingError.CPUSET_UNKNOWN}
values = self._data.get()
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
memdata = values[:, :, 1]
sortarr = numpy.argsort(memdata.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], memdata),
"max": self.collatedata(sortarr[:, -1], memdata),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2],
memdata),
"times": values[0, :, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {"times": values[0, :, 0].tolist(), "hosts": {}}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = values[hostidx, :,
1].tolist()
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
def __init__(self, job):
super(SlurmCgroupMemTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostcounts = {}
self._expectedcgroup = "/slurm/uid_{0}/job_{1}".format(job.acct['uid'], job.job_id)
class SlurmCgroupMemTimeseries(Plugin):
""" Generate timeseries summary for memory usage viewed from CGroup
This code is SLURM-specific because of the SLURM cgroup naming convention.
"""
name = property(lambda x: "process_mem_usage")
mode = property(lambda x: "timeseries")
requiredMetrics = property(lambda x: ["cgroup.memory.usage"])
optionalMetrics = property(lambda x: [])
derivedMetrics = property(lambda x: [])
def __init__(self, job):
super(SlurmCgroupMemTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostcounts = {}
self._expectedcgroup = "/slurm/uid_{0}/job_{1}".format(job.acct['uid'], job.job_id)
def process(self, nodemeta, timestamp, data, description):
hostidx = nodemeta.nodeindex
if len(data[0]) == 0:
# Skip data point with no data
return True
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[hostidx] = numpy.empty((TimeseriesAccumulator.MAX_DATAPOINTS, 1))
self._hostcounts[hostidx] = {'missing': 0, 'present': 0}
try:
dataidx = description[0][1].index(self._expectedcgroup)
nodemem_gb = data[0][dataidx] / 1073741824.0
self._hostcounts[hostidx]['present'] += 1
except ValueError:
self._hostcounts[hostidx]['missing'] += 1
# No cgroup info at this datapoint
return True
insertat = self._data.adddata(hostidx, timestamp, nodemem_gb)
if insertat != None:
self._hostdata[hostidx][insertat] = nodemem_gb
return True
def results(self):
if len(self._hostdata) != self._job.nodecount:
return {'error': ProcessingError.RAW_COUNTER_UNAVAILABLE}
for hcount in self._hostcounts.itervalues():
if hcount['missing'] > hcount['present']:
return {'error': ProcessingError.CPUSET_UNKNOWN}
values = self._data.get()
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
memdata = values[:, :, 1]
sortarr = numpy.argsort(memdata.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], memdata),
"max": self.collatedata(sortarr[:, -1], memdata),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2], memdata),
"times": values[0, :, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {
"times": values[0, :, 0].tolist(),
"hosts": {}
}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = values[hostidx, :, 1].tolist()
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
def __init__(self, job):
super(CpuUserTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
self._cpusallowed = None
class PowerUsageTimeseries(Plugin):
""" Generate the Power usage as a timeseries data """
name = property(lambda x: "power")
mode = property(lambda x: "timeseries")
requiredMetrics = property(lambda x: ["ipmi.dcmi.power"])
optionalMetrics = property(lambda x: [])
derivedMetrics = property(lambda x: [])
def __init__(self, job):
super(PowerUsageTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
@staticmethod
def computetimepoint(data):
""" Get the power usage from the data """
if data[0][0] < numpy.finfo(numpy.float64).eps:
return None
return data[0][0]
def process(self, nodemeta, timestamp, data, description):
if not data[0]:
# Skip data point with no data
return True
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[nodemeta.nodeindex] = 1
datum = self.computetimepoint(data)
if datum != None:
self._data.adddata(nodemeta.nodeindex, timestamp, datum)
return True
def results(self):
if len(self._hostdata) != self._job.nodecount:
return {"error": ProcessingError.INSUFFICIENT_HOSTDATA}
values = self._data.get()
if len(values[0, :, 0]) < 3:
return {"error": ProcessingError.JOB_TOO_SHORT}
power = values[:, :, 1]
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
sortarr = numpy.argsort(power.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], power),
"max": self.collatedata(sortarr[:, -1], power),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2],
power),
"times": values[0, :, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {"times": values[0, :, 0].tolist(), "hosts": {}}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = power[hostidx, :].tolist()
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
class CpuUserTimeseries(Plugin):
""" Generate the CPU usage as a timeseries data """
name = property(lambda x: "cpuuser")
mode = property(lambda x: "timeseries")
requiredMetrics = property(lambda x: ["kernel.percpu.cpu.user"])
optionalMetrics = property(lambda x: [])
derivedMetrics = property(lambda x: [])
def __init__(self, job):
super(CpuUserTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
self._cpusallowed = None
def initcpus(self):
if self._job.getdata('proc'):
self._cpusallowed = self._job.getdata('proc')['cpusallowed']
else:
self._cpusallowed = {}
def process(self, nodemeta, timestamp, data, description):
if self._cpusallowed == None:
self.initcpus()
if len(data[0]) == 0:
# Skip datapoints that have no values
return True
if nodemeta.nodename in self._cpusallowed and 'error' not in self._cpusallowed[nodemeta.nodename]:
cpudata = data[0][self._cpusallowed[nodemeta.nodename]]
else:
cpudata = data[0]
hostidx = nodemeta.nodeindex
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[hostidx] = numpy.empty((TimeseriesAccumulator.MAX_DATAPOINTS, len(cpudata)))
if nodemeta.nodename in self._cpusallowed and 'error' not in self._cpusallowed[nodemeta.nodename]:
self._hostdevnames[hostidx] = {}
for i, cpuidx in enumerate(self._cpusallowed[nodemeta.nodename]):
self._hostdevnames[hostidx][str(i)] = description[0][1][cpuidx]
else:
self._hostdevnames[hostidx] = dict((str(k), v) for k, v in zip(description[0][0], description[0][1]))
insertat = self._data.adddata(hostidx, timestamp, numpy.mean(cpudata)/10.0)
if insertat != None:
self._hostdata[hostidx][insertat] = cpudata / 10.0
return True
def results(self):
values = self._data.get()
if len(values[0, :, 0]) < 3:
return {"error": ProcessingError.JOB_TOO_SHORT}
rates = numpy.diff(values[:, :, 1]) / numpy.diff(values[:, :, 0])
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
sortarr = numpy.argsort(rates.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], rates),
"max": self.collatedata(sortarr[:, -1], rates),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2], rates),
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = rates[hostidx, :].tolist()
retdata['hosts'][str(hostidx)]['dev'] = {}
for devid in self._hostdevnames[hostidx].iterkeys():
dpnts = len(values[hostidx, :, 0])
retdata['hosts'][str(hostidx)]['dev'][devid] = (numpy.diff(self._hostdata[hostidx][:dpnts, numpy.int(devid)]) / numpy.diff(values[hostidx, :, 0])).tolist()
retdata['hosts'][str(hostidx)]['names'] = self._hostdevnames[hostidx]
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
class CpuUserTimeseries(Plugin):
""" Generate the CPU usage as a timeseries data """
name = property(lambda x: "cpuuser")
mode = property(lambda x: "timeseries")
requiredMetrics = property(lambda x: ["kernel.percpu.cpu.user"])
optionalMetrics = property(lambda x: [])
derivedMetrics = property(lambda x: [])
def __init__(self, job):
super(CpuUserTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
def process(self, nodemeta, timestamp, data, description):
if len(data[0]) == 0:
# Skip datapoints that have no values
return True
hostidx = nodemeta.nodeindex
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[hostidx] = numpy.empty((TimeseriesAccumulator.MAX_DATAPOINTS, len(data[0])))
self._hostdevnames[hostidx] = dict((str(k), v) for k, v in zip(description[0][0], description[0][1]))
insertat = self._data.adddata(hostidx, timestamp, numpy.mean(data[0])/10.0)
if insertat != None:
self._hostdata[hostidx][insertat] = data[0] / 10.0
return True
def results(self):
values = self._data.get()
if len(values[0, :, 0]) < 3:
return {"error": ProcessingError.JOB_TOO_SHORT}
rates = numpy.diff(values[:, :, 1]) / numpy.diff(values[:, :, 0])
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
sortarr = numpy.argsort(rates.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], rates),
"max": self.collatedata(sortarr[:, -1], rates),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2], rates),
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {
"times": values[0, 1:, 0].tolist(),
"hosts": {}
}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = rates[hostidx, :].tolist()
retdata['hosts'][str(hostidx)]['dev'] = {}
for devid in self._hostdevnames[hostidx].iterkeys():
dpnts = len(values[hostidx, :, 0])
retdata['hosts'][str(hostidx)]['dev'][devid] = (numpy.diff(self._hostdata[hostidx][:dpnts, devid]) / numpy.diff(values[hostidx, :, 0])).tolist()
retdata['hosts'][str(hostidx)]['names'] = self._hostdevnames[hostidx]
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
def __init__(self, job):
super(GpuUsageTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
class MemUsageTimeseries(Plugin):
""" Generate the CPU usage as a timeseries data """
name = property(lambda x: "memused_minus_diskcache")
mode = property(lambda x: "timeseries")
requiredMetrics = property(lambda x: ["mem.numa.util.used", "mem.numa.util.filePages", "mem.numa.util.slab"])
optionalMetrics = property(lambda x: [])
derivedMetrics = property(lambda x: [])
def __init__(self, job):
super(MemUsageTimeseries, self).__init__(job)
self._data = TimeseriesAccumulator(job.nodecount, self._job.walltime)
self._hostdata = {}
self._hostdevnames = {}
def process(self, nodemeta, timestamp, data, description):
hostidx = nodemeta.nodeindex
if len(data[0]) == 0:
# Skip data point with no data
return True
if nodemeta.nodeindex not in self._hostdata:
self._hostdata[hostidx] = numpy.empty((TimeseriesAccumulator.MAX_DATAPOINTS, len(data[0])))
self._hostdevnames[hostidx] = dict((str(k), v) for k, v in zip(description[0][0], description[0][1]))
nodemem_kb = numpy.sum(data[0]) - numpy.sum(data[1]) - numpy.sum(data[2])
insertat = self._data.adddata(hostidx, timestamp, nodemem_kb / 1048576.0)
if insertat != None:
self._hostdata[hostidx][insertat] = (data[0] - data[1] - data[2]) / 1048576.0
return True
def results(self):
values = self._data.get()
if len(self._hostdata) > 64:
# Compute min, max & median data and only save the host data
# for these hosts
memdata = values[:, :, 1]
sortarr = numpy.argsort(memdata.T, axis=1)
retdata = {
"min": self.collatedata(sortarr[:, 0], memdata),
"max": self.collatedata(sortarr[:, -1], memdata),
"med": self.collatedata(sortarr[:, sortarr.shape[1] / 2], memdata),
"times": values[0, :, 0].tolist(),
"hosts": {}
}
uniqhosts = Counter(sortarr[:, 0])
uniqhosts.update(sortarr[:, -1])
uniqhosts.update(sortarr[:, sortarr.shape[1] / 2])
includelist = uniqhosts.keys()
else:
# Save data for all hosts
retdata = {
"times": values[0, :, 0].tolist(),
"hosts": {}
}
includelist = self._hostdata.keys()
for hostidx in includelist:
retdata['hosts'][str(hostidx)] = {}
retdata['hosts'][str(hostidx)]['all'] = values[hostidx, :, 1].tolist()
retdata['hosts'][str(hostidx)]['dev'] = {}
for devid in self._hostdevnames[hostidx].iterkeys():
dpnts = len(values[hostidx, :, 0])
retdata['hosts'][str(hostidx)]['dev'][devid] = self._hostdata[hostidx][:dpnts, devid].tolist()
retdata['hosts'][str(hostidx)]['names'] = self._hostdevnames[hostidx]
return retdata
@staticmethod
def collatedata(args, rates):
""" build output data """
result = []
for timepoint, hostidx in enumerate(args):
try:
result.append([rates[hostidx, timepoint], int(hostidx)])
except IndexError:
pass
return result
