As instrumentation can be enabled/disabled for a specfic workload
execution, it is sometimes not enough to verify that an instrument has
been installed for the run; one might need to check whether it is
currently enabled.
Since a command would still be running on time out, it would prevent
issuing subsequent commands in the same SSH shell, make it look like
the device has become unresponsive.
If a timeout condition is his, send ^C to kill the current foreground
process and make the shell available for subsequent commands.
- Adding "classifiers" field to Metric objects. This is a dict mapping
classifier names (arbitrary strings) to corresponding values for that
specific metrics. This is to allow extensions to add
extension-specific annotations to metric that could be handled in a
generic way (e.g. by result processors).
- Updating telemetry workload to add classifiers for the url and internal
iteration (or "time") for a particular result.
- Updating csv result processor with the option to use classifiers to
add columns to results.csv (either using all classifiers found, or
only for the specific ones listed).
On Unix, pip will change current working directory to whereever it has
extracted the downloaded package. On Mac OSX, it does not appear to do
that. To get around this difference, specify paths in setup.py relative
to the location of setup.py, rather than the current working directory.
The key file must only be readable by the owner. If the specified key
file has different access permissions, create a temporary copy with the
right permissions and use that.
np.vectorize was being unconditionally invoked at top level. On an
ImportError, np as set to None, so this was resuling in an
AttributeError when loading the module if one of the dependent libraries
was not present on the host system. This moves the invocation into the
try block with the imports to avoid an error when energy_model module is
loaded by the extension is not used.
- adding missing cpu id conversion to set_cpu_min_freq
- adding "exact" parameter to set_cpu_frequency; only produce an error
when the specified frequency is not supported by the cpu if "axact"
is set; (otherwise let cpufreq decide what the actual frequency will
be).
- adding missing cpu id conversion to set_cpu_min_freq
- adding "exact" parameter to set_cpu_frequency; only produce an error
when the specified frequency is not supported by the cpu if "axact"
is set; (otherwise let cpufreq decide what the actual frequency will
be).
Make sure when migrating sshd to root cgroup also migrate their
children, including the bash for the wa session. So the subsequent
processes kicked off from that shell can be taskset to any cluster.
Matplotlib defautls to the GTK backend. This can cause problems when
running in a headless session (e.g. over SSH). Since energy_model
istrument generates PNG plots, rather than rendering directly to UI, it
doesn't actually need GTK; set backend to AGG so that energy_model works
in headless environments.
This instrument can be used to generate an energy model for a device
based on collected power and performance measurments. The instrument
produces a C file with an energy model and an accompanying HTML report.
This instrument is very different from other instrumentation, as it
actually generates the run queue on the fly based on the operating
frequencies and idle states it discovers on the device. The agenda needs
only to contain the single "base" spec that defines the workload to be
used for performance measurement.
cpuidle module implements cpuidle state discovery, query and
manipulation for a Linux device. This replaces the more primitive
get_cpuidle_states method of LinuxDevice.
Renamed APIs (and added a couple of new ones) to be more consistent:
"core" APIs take a core name as the parameter (e.g. "a15") or whatever
is listed in core_names for that device.
"cluster" APIs take a numeric cluster ID (eg. 0) as the parameter. These
get mapped using core_clusters for that device.
"cpu" APIs take a cpufreq cpu ID as a parameter. These could be
integers, e.g. 0, or full string id, e.g. "cpu0".
integer and boolen are defined in wlauto.utils.types; they perform more
intuitive conversions from other types, particularly strings, so are
more suitable than int and bool for parameters. If, for whatever reason,
native types are in fact desired for a Parameter, this behavior can be
supressed by specifying convert_types=False when defining the parameter.
- list_to_range and range_to_list convert between lists of integers
and corresponding range strings, e.g. between [0,1,2,4] and '0-2,4'
- list_to_mask and mask_to_list convert between lists of integers and
corresponding integer masks, e.g. between [0,1,2,4] and 0x17
Conflicts:
wlauto/utils/misc.py
- added caseless_string type. This behaves exactly like a string, except
this ignores case in comparisons. It does, however, preserve case. E.g.
>>> s = caseless_string('Test')
>>> s == 'test'
True
>>> print s
Test
- added list_of type generating function. This allows to dynamically
generate type-safe list types based on an existing type. E.g.
>>> list_of_bool = list_of(bool)
>>> list_of_bool(['foo', 0, 1, '', True])
[True, False, True, False, True]
- Update core_names Device Parameter to be of type caseless_string
- added the ability to run based on time, rather than number of
requests.
- added a parameter to taskset to specific core(s).
Conflicts:
wlauto/workloads/sysbench/__init__.py
