Agenda¶

An agenda specifies what is to be done during a Workload Automation run, including which workloads will be run, with what configuration, which instruments and result processors will be enabled, etc. Agenda syntax is designed to be both succinct and expressive.

Agendas are specified using YAML notation. It is recommended that you familiarize yourself with the linked page.

Note

Earlier versions of WA have supported CSV-style agendas. These were there to facilitate transition from WA1 scripts. The format was more awkward and supported only a limited subset of the features. Support for it has now been removed.

Specifying which workloads to run¶

The central purpose of an agenda is to specify what workloads to run. A minimalist agenda contains a single entry at the top level called “workloads” that maps onto a list of workload names to run:

workloads:
        - dhrystone
        - memcpy
        - cyclictest

This specifies a WA run consisting of dhrystone followed by memcpy, followed by cyclictest workloads, and using instruments and result processors specified in config.py (see Configuration section).

Note

If you’re familiar with YAML, you will recognize the above as a single-key associative array mapping onto a list. YAML has two notations for both associative arrays and lists: block notation (seen above) and also in-line notation. This means that the above agenda can also be written in a single line as

workloads: [dhrystone, memcpy, cyclictest]

(with the list in-lined), or

{workloads: [dhrystone, memcpy, cyclictest]}

(with both the list and the associative array in-line). WA doesn’t care which of the notations is used as they all get parsed into the same structure by the YAML parser. You can use whatever format you find easier/clearer.

Multiple iterations¶

There will normally be some variability in workload execution when running on a real device. In order to quantify it, multiple iterations of the same workload are usually performed. You can specify the number of iterations for each workload by adding iterations field to the workload specifications (or “specs”):

workloads:
        - name: dhrystone
          iterations: 5
        - name: memcpy
          iterations: 5
        - name: cyclictest
          iterations: 5

Now that we’re specifying both the workload name and the number of iterations in each spec, we have to explicitly name each field of the spec.

It is often the case that, as in in the example above, you will want to run all workloads for the same number of iterations. Rather than having to specify it for each and every spec, you can do with a single entry by adding a global section to your agenda:

global:
        iterations: 5
workloads:
        - dhrystone
        - memcpy
        - cyclictest

The global section can contain the same fields as a workload spec. The fields in the global section will get added to each spec. If the same field is defined both in global section and in a spec, then the value in the spec will overwrite the global value. For example, suppose we wanted to run all our workloads for five iterations, except cyclictest which we want to run for ten (e.g. because we know it to be particularly unstable). This can be specified like this:

global:
        iterations: 5
workloads:
        - dhrystone
        - memcpy
        - name: cyclictest
          iterations: 10

Again, because we are now specifying two fields for cyclictest spec, we have to explicitly name them.

Configuring workloads¶

Some workloads accept configuration parameters that modify their behavior. These parameters are specific to a particular workload and can alter the workload in any number of ways, e.g. set the duration for which to run, or specify a media file to be used, etc. The vast majority of workload parameters will have some default value, so it is only necessary to specify the name of the workload in order for WA to run it. However, sometimes you want more control over how a workload runs.

For example, by default, dhrystone will execute 10 million loops across four threads. Suppose you device has six cores available and you want the workload to load them all. You also want to increase the total number of loops accordingly to 15 million. You can specify this using dhrystone’s parameters:

global:
        iterations: 5
workloads:
        - name: dhrystone
          params:
                threads: 6
                mloops: 15
        - memcpy
        - name: cyclictest
          iterations: 10

Note

You can find out what parameters a workload accepts by looking it up in the Workloads section. You can also look it up using WA itself with “show” command:

wa show dhrystone

see the Invocation section for details.

In addition to configuring the workload itself, we can also specify configuration for the underlying device. This can be done by setting runtime parameters in the workload spec. For example, suppose we want to ensure the maximum score for our benchmarks, at the expense of power consumption, by setting the cpufreq governor to “performance” on cpu0 (assuming all our cores are in the same DVFS domain and so setting the governor for cpu0 will affect all cores). This can be done like this:

global:
        iterations: 5
workloads:
        - name: dhrystone
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: performance
          workload_params:
                threads: 6
                mloops: 15
        - memcpy
        - name: cyclictest
          iterations: 10

Here, we’re specifying sysfile_values runtime parameter for the device. The value for this parameter is a mapping (an associative array, in YAML) of file paths onto values that should be written into those files. sysfile_values is the only runtime parameter that is available for any (Linux) device. Other runtime parameters will depend on the specifics of the device used (e.g. its CPU cores configuration). I’ve renamed params to workload_params for clarity, but that wasn’t strictly necessary as params is interpreted as workload_params inside a workload spec.

Note

params field is interpreted differently depending on whether it’s in a workload spec or the global section. In a workload spec, it translates to workload_params, in the global section it translates to runtime_params.

Runtime parameters do not automatically reset at the end of workload spec execution, so all subsequent iterations will also be affected unless they explicitly change the parameter (in the example above, performance governor will also be used for memcpy and cyclictest. There are two ways around this: either set reboot_policy WA setting (see Configuration section) such that the device gets rebooted between spec executions, thus being returned to its initial state, or set the default runtime parameter values in the global section of the agenda so that they get set for every spec that doesn’t explicitly override them.

Note

“In addition to runtime_params there are also boot_params that work in a similar way, but they get passed to the device when it reboots. At the moment TC2 is the only device that defines a boot parameter, which is explained in TC2 documentation, so boot parameters will not be mentioned further.

IDs and Labels¶

It is possible to list multiple specs with the same workload in an agenda. You may wish to this if you want to run a workload with different parameter values or under different runtime configurations of the device. The workload name therefore does not uniquely identify a spec. To be able to distinguish between different specs (e.g. in reported results), each spec has an ID which is unique to all specs within an agenda (and therefore with a single WA run). If an ID isn’t explicitly specified using id field (note that the field name is in lower case), one will be automatically assigned to the spec at the beginning of the WA run based on the position of the spec within the list. The first spec without an explicit ID will be assigned ID 1, the second spec without an explicit ID will be assigned ID 2, and so forth.

Numerical IDs aren’t particularly easy to deal with, which is why it is recommended that, for non-trivial agendas, you manually set the ids to something more meaningful (or use labels – see below). An ID can be pretty much anything that will pass through the YAML parser. The only requirement is that it is unique to the agenda. However, is usually better to keep them reasonably short (they don’t need to be globally unique), and to stick with alpha-numeric characters and underscores/dashes. While WA can handle other characters as well, getting too adventurous with your IDs may cause issues further down the line when processing WA results (e.g. when uploading them to a database that may have its own restrictions).

In addition to IDs, you can also specify labels for your workload specs. These are similar to IDs but do not have the uniqueness restriction. If specified, labels will be used by some result processes instead of (or in addition to) the workload name. For example, the csv result processor will put the label in the “workload” column of the CSV file.

It is up to you how you chose to use IDs and labels. WA itself doesn’t expect any particular format (apart from uniqueness for IDs). Below is the earlier example updated to specify explicit IDs and label dhrystone spec to reflect parameters used.

global:
        iterations: 5
workloads:
        - id: 01_dhry
          name: dhrystone
          label: dhrystone_15over6
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: performance
          workload_params:
                threads: 6
                mloops: 15
        - id: 02_memc
          name: memcpy
        - id: 03_cycl
          name: cyclictest
          iterations: 10

Result Processors and Instrumentation¶

Result Processors¶

Result processors, as the name suggests, handle the processing of results generated form running workload specs. By default, WA enables a couple of basic result processors (e.g. one generates a csv file with all scores reported by workloads), which you can see in ~/.workload_automation/config.py. However, WA has a number of other, more specialized, result processors (e.g. for uploading to databases). You can list available result processors with wa list result_processors command. If you want to permanently enable a result processor, you can add it to your config.py. You can also enable a result processor for a particular run by specifying it in the config section in the agenda. As the name suggests, config section mirrors the structure of config.py(although using YAML rather than Python), and anything that can be specified in the latter, can also be specified in the former.

As with workloads, result processors may have parameters that define their behavior. Parameters of result processors are specified a little differently, however. Result processor parameter values are listed in the config section, namespaced under the name of the result processor.

For example, suppose we want to be able to easily query the results generated by the workload specs we’ve defined so far. We can use sqlite result processor to have WA create an sqlite database file with the results. By default, this file will be generated in WA’s output directory (at the same level as results.csv); but suppose we want to store the results in the same file for every run of the agenda we do. This can be done by specifying an alternative database file with database parameter of the result processor:

config:
        result_processors: [sqlite]
        sqlite:
                database: ~/my_wa_results.sqlite
global:
        iterations: 5
workloads:
        - id: 01_dhry
          name: dhrystone
          label: dhrystone_15over6
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: performance
          workload_params:
                threads: 6
                mloops: 15
        - id: 02_memc
          name: memcpy
        - id: 03_cycl
          name: cyclictest
          iterations: 10

A couple of things to observe here:

There is no need to repeat the result processors listed in config.py. The processors listed in result_processors entry in the agenda will be used in addition to those defined in the config.py.
The database file is specified under “sqlite” entry in the config section. Note, however, that this entry alone is not enough to enable the result processor, it must be listed in result_processors, otherwise the “sqilte” config entry will be ignored.
The database file must be specified as an absolute path, however it may use the user home specifier ‘~’ and/or environment variables.

Instrumentation¶

WA can enable various “instruments” to be used during workload execution. Instruments can be quite diverse in their functionality, but the majority of instruments available in WA today are there to collect additional data (such as trace) from the device during workload execution. You can view the list of available instruments by using wa list instruments command. As with result processors, a few are enabled by default in the config.py and additional ones may be added in the same place, or specified in the agenda using instrumentation entry.

For example, we can collect core utilisation statistics (for what proportion of workload execution N cores were utilized above a specified threshold) using coreutil instrument.

config:
        instrumentation: [coreutil]
        coreutil:
                threshold: 80
        result_processors: [sqlite]
        sqlite:
                database: ~/my_wa_results.sqlite
global:
        iterations: 5
workloads:
        - id: 01_dhry
          name: dhrystone
          label: dhrystone_15over6
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: performance
          workload_params:
                threads: 6
                mloops: 15
        - id: 02_memc
          name: memcpy
        - id: 03_cycl
          name: cyclictest
          iterations: 10

Instrumentation isn’t “free” and it is advisable not to have too many instruments enabled at once as that might skew results. For example, you don’t want to have power measurement enabled at the same time as event tracing, as the latter may prevent cores from going into idle states and thus affecting the reading collected by the former.

Unlike result processors, instrumentation may be enabled (and disabled – see below) on per-spec basis. For example, suppose we want to collect /proc/meminfo from the device when we run memcpy workload, but not for the other two. We can do that using sysfs_extractor instrument, and we will only enable it for memcpy:

config:
        instrumentation: [coreutil]
        coreutil:
                threshold: 80
        sysfs_extractor:
                paths: [/proc/meminfo]
        result_processors: [sqlite]
        sqlite:
                database: ~/my_wa_results.sqlite
global:
        iterations: 5
workloads:
        - id: 01_dhry
          name: dhrystone
          label: dhrystone_15over6
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: performance
          workload_params:
                threads: 6
                mloops: 15
        - id: 02_memc
          name: memcpy
          instrumentation: [sysfs_extractor]
        - id: 03_cycl
          name: cyclictest
          iterations: 10

As with config sections, instrumentation entry in the spec needs only to list additional instruments and does not need to repeat instruments specified elsewhere.

Note

At present, it is only possible to enable/disable instrumentation on per-spec base. It is not possible to provide configuration on per-spec basis in the current version of WA (e.g. in our example, it is not possible to specify different sysfs_extractor paths for different workloads). This restriction may be lifted in future versions of WA.

Disabling result processors and instrumentation¶

As seen above, extensions specified with instrumentation and result_processor clauses get added to those already specified previously. Just because an instrument specified in config.py is not listed in the config section of the agenda, does not mean it will be disabled. If you do want to disable an instrument, you can always remove/comment it out from config.py. However that will be introducing a permanent configuration change to your environment (one that can be easily reverted, but may be just as easily forgotten). If you want to temporarily disable a result processor or an instrument for a particular run, you can do that in your agenda by prepending a tilde (~) to its name.

For example, let’s say we want to disable cpufreq instrument enabled in our config.py (suppose we’re going to send results via email and so want to reduce to total size of the output directory):

config:
        instrumentation: [coreutil, ~cpufreq]
        coreutil:
                threshold: 80
        sysfs_extractor:
                paths: [/proc/meminfo]
        result_processors: [sqlite]
        sqlite:
                database: ~/my_wa_results.sqlite
global:
        iterations: 5
workloads:
        - id: 01_dhry
          name: dhrystone
          label: dhrystone_15over6
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: performance
          workload_params:
                threads: 6
                mloops: 15
        - id: 02_memc
          name: memcpy
          instrumentation: [sysfs_extractor]
        - id: 03_cycl
          name: cyclictest
          iterations: 10

Sections¶

It is a common requirement to be able to run the same set of workloads under different device configurations. E.g. you may want to investigate impact of changing a particular setting to different values on the benchmark scores, or to quantify the impact of enabling a particular feature in the kernel. WA allows this by defining “sections” of configuration with an agenda.

For example, suppose what we really want, is to measure the impact of using interactive cpufreq governor vs the performance governor on the three benchmarks. We could create another three workload spec entries similar to the ones we already have and change the sysfile value being set to “interactive”. However, this introduces a lot of duplication; and what if we want to change spec configuration? We would have to change it in multiple places, running the risk of forgetting one.

A better way is to keep the three workload specs and define a section for each governor:

config:
        instrumentation: [coreutil, ~cpufreq]
        coreutil:
                threshold: 80
        sysfs_extractor:
                paths: [/proc/meminfo]
        result_processors: [sqlite]
        sqlite:
                database: ~/my_wa_results.sqlite
global:
        iterations: 5
sections:
        - id: perf
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: performance
        - id: inter
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: interactive
workloads:
        - id: 01_dhry
          name: dhrystone
          label: dhrystone_15over6
          workload_params:
                threads: 6
                mloops: 15
        - id: 02_memc
          name: memcpy
          instrumentation: [sysfs_extractor]
        - id: 03_cycl
          name: cyclictest
          iterations: 10

A section, just like an workload spec, needs to have a unique ID. Apart from that, a “section” is similar to the global section we’ve already seen – everything that goes into a section will be applied to each workload spec. Workload specs defined under top-level workloads entry will be executed for each of the sections listed under sections.

Note

It is also possible to have a workloads entry within a section, in which case, those workloads will only be executed for that specific section.

In order to maintain the uniqueness requirement of workload spec IDs, they will be namespaced under each section by prepending the section ID to the spec ID with an under score. So in the agenda above, we no longer have a workload spec with ID 01_dhry, instead there are two specs with IDs perf_01_dhry and inter_01_dhry.

Note that the global section still applies to every spec in the agenda. So the precedence order is – spec settings override section settings, which in turn override global settings.

Other Configuration¶

As mentioned previously, config section in an agenda can contain anything that can be defined in config.py (with Python syntax translated to the equivalent YAML). Certain configuration (e.g. run_name) makes more sense to define in an agenda than a config file. Refer to the Configuration section for details.

config:
        project: governor_comparison
        run_name: performance_vs_interactive

        device: generic_android
        reboot_policy: never

        instrumentation: [coreutil, ~cpufreq]
        coreutil:
                threshold: 80
        sysfs_extractor:
                paths: [/proc/meminfo]
        result_processors: [sqlite]
        sqlite:
                database: ~/my_wa_results.sqlite
global:
        iterations: 5
sections:
        - id: perf
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: performance
        - id: inter
          runtime_params:
                sysfile_values:
                        /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor: interactive
workloads:
        - id: 01_dhry
          name: dhrystone
          label: dhrystone_15over6
          workload_params:
                threads: 6
                mloops: 15
        - id: 02_memc
          name: memcpy
          instrumentation: [sysfs_extractor]
        - id: 03_cycl
          name: cyclictest
          iterations: 10

Agenda¶

Specifying which workloads to run¶

Multiple iterations¶

Configuring workloads¶

IDs and Labels¶

Result Processors and Instrumentation¶

Result Processors¶

Instrumentation¶

Disabling result processors and instrumentation¶

Sections¶

Other Configuration¶

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