LITMUS^RT: Linux Testbed for Multiprocessor Scheduling in Real-Time Systems


Björn B. Brandenburg, Mahircan Gül, and Manohar Vanga

LITMUSRT is the Linux Testbed for Multiprocessor Scheduling in Real-Time Systems. Its purpose is to provide a foundation for applied real-time systems research. To this end, LITMUSRT

  1. adds predictable scheduling and synchronization policies to the Linux kernel,
  2. reduces the effort involved in prototyping new scheduling and synchronization polices in the kernel by offering a simplified scheduling policy plugin interface,
  3. provides liblitmus a userspace library to expose the added kernel functionality to applications, and
  4. comes with Feather-Trace, a low-overhead tracing framework.

This guide provides an introduction to the user-facing parts of LITMUSRT, namely (3) and (4), and assumes that the kernel (with the LITMUSRT modifications in place) has already been compiled and is working. How to modify the kernel itself, and in particular how to develop new policy plugins, is beyond the scope of this guide. We instead refer those insterested in developing new plugins to the plugin creation tutorial.

This guide describes LITMUSRT as of version 2017.1 (released in May 2017).

Table of Contents


LITMUSRT boots just like any other Linux system. In most cases, no modifications need to be made to the Linux distribution’s init system.

Linux supports a large number of boot parameters that can be passed to the kernel as a kernel command line argument. Most of these parameters are irrelevant to LITMUSRT. One of them, however, the maxcpus parameter, is particularly useful and worth highlighting.

The maxcpus parameter allows limiting the number of processors used by the kernel. This can be used to instruct the kernel to leave some of the available processors unused. For instance, this is useful when conducting scalability experiments where the overhead of a scheduler (or some other component) needs to be obtained for a range of core counts.

(Note: there also exists a static maximum supported processor count called NR_CPUS that is set as part of the kernel configuration at compile time. The maxcpus parameter cannot exceed NR_CPUS.)


As already mentioned, LITMUSRT augments the Linux kernel with a number of predictable scheduling policies, which are realized as scheduler plugins.

At all times, the active plugin determines the current scheduling policy in a LITMUSRT kernel. When the system boots up, it initially uses the dummy Linux plugin, which simply disables all LITMUSRT functionality (which is not needed during bootup). Hence, before using any LITMUSRT features, one of the available policy plugins must be activated with a plugin switch (as discussed in more detail below).

As of version 2017.1, the following real-time scheduling policies are built into the LITMUSRT kernel:

While P-FP, PSN-EDF, P-RES, and GSN-EDF are built into the kernel by default, C-EDF and PFAIR are optional and can be de-selected during kernel configuration.

These policy plugins form two groups:

The classic plugins have been a part of LITMUSRT since the first version from 2006/2007 and have played a role in virtually all prior studies based on LITMUSRT. However, they impose certain limitations that may render them unsuitable for some workloads (e.g., under these plugins, when a real-time process forks, the child process is not a real-time process to avoid overload). We expect future developments to focus increasingly on reservation-based plugins.

During boot-up, built-in policies are loaded by the kernel and can then be later activated (one at a time) by the root user.

At runtime, scheduler plugins are managed via a simple /proc interface that is exposed under /proc/litmus. While all files under /proc/litmus can be manually read or written for management purposes, it is often easier and more convenient to use the higher-level tools offered by liblitmus. In the following, we discuss a list of basic user-space commands related to plugin management, followed by an example.

All the commands mentioned in the following must be executed as root.

Listing All Available Schedulers

All loaded schedulers can be listed by running cat /proc/litmus/plugins/loaded.

For example:

root@litmus:~ cat /proc/litmus/plugins/loaded 

Displaying the Active Scheduler Plugin

The currently active scheduler is reported by the showsched tool. Alternatively, it can be printed with cat /proc/litmus/active_plugin.


root@litmus:~ showsched
root@litmus:~ cat /proc/litmus/active_plugin

After boot-up, the active scheduler is set to Linux by default.

Changing the Active Scheduler Plugin

The active scheduler plugin can be selected with the setsched tool. Switching scheduling policies is permitted only when there are no active real-time tasks in the system.

To activate a specific policy, invoke setsched with the policy name as the first argument. For example, to activate the GSN-EDF plugin, run setsched GSN-EDF.

If setsched succeeds, then there is no output, as is typical for UNIX tools.


root@litmus:~ setsched GSN-EDF
root@litmus:~ showsched

To be presented with a text-based interface for selecting the active scheduler from the list of available plugins, run setsched without an argument.

There can be only one active policy at any time.

Selecting the C-EDF and PFAIR Cluster Sizes

Under clustered scheduling, non-overlapping sets of processors are scheduled independently with a “global” policy. In LITMUSRT, clusters are created based on cache topology.

The C-EDF scheduler builds clusters of processors at activation time based on the current value of the cluster file /proc/litmus/plugins/C-EDF/cluster. Valid values are “L1”, “L2”, “L3”, or “ALL”, where “ALL” indicates global scheduling (i.e., with “ALL” a single cluster containing all processor cores is built). On processors with private L1 caches, “L1” corresponds to partitioned scheduling (i.e., in this case, C-EDF builds one cluster for each processor).

For example, to enable L3-based clusters under C-EDF, run the following command:

echo L3 > /proc/litmus/plugins/C-EDF/cluster

The PFAIR plugin also supports clustered scheduling based on the corresponding cluster file /proc/litmus/plugins/PFAIR/cluster.

Note that clusters are configured when a plugin is activated, i.e, the cluster size cannot be changed while a plugin is active. To be effective, the desired cluster size has to be written to the cluster file before the scheduler switch.

If a change in cluster size is desired, first switch to the Linux plugin, update the cluster file, and then switch back to the clustered plugin. For example:

# switch to Linux plugin
setsched Linux
# configure L2-based clusters
echo L2 > /proc/litmus/plugins/C-EDF/cluster
# switch back to C-EDF
setsched C-EDF

Working with Reservations in P-RES

In contrast to the classic process-based LITMUSRT plugins, in the in P-RES plugin, reservations are first-class entities that exist independently of tasks. In particular, they must be created before any real-time task can be launched, and they continue to exist even after all tasks have terminated. Multiple tasks or threads can be assigned to the same reservation. If a task forks, both the parent and the child remain in the same reservation.

To create new reservations, use the tool resctl. The tool has an online help message that can be triggered with resctl -h, which explains all options. In the following, we explain how to create partitioned, per-processor reservations.

Reservation Types

The current version of the P-RES plugin supports three reservation types:

polling-periodic (PP)
polling-sporadic (PS)
table-driven (TD)

Additional common reservations types (e.g., CBS, sporadic servers, etc.) have been developed in a separate branch and are expected to be released in a future version of LITMUSRT.

The most simple reservation type is the polling reservation, which comes in two flavors: classic periodic polling reservations (PP), and more flexible sporadic polling reservations (PS). The latter is ideally suited for encapsulating sporadic and periodic real-time tasks, whereas the former is useful primarily if there is a need for fixed, known replenishment times.

In the following examples, we use sporadic polling reservations.

Creating a Reservation

Each reservation is identified by a reservation ID (RID), which is simply a non-negative number (like a PID). However, there are two important differences between PIDs and RIDs:

  1. In contrast to PIDs, RIDs are not automatically assigned. Rather, the desired RID must be specified when the reservation is created.
  2. The same RID may be used on multiple processors (i.e., RIDs are not unique systemwide). Hence it is important to specify the processor both when creating reservations and when attaching processes to reservations.

Use the following invocation to create a new sporadic polling reservation with RID 123 on core 0:

resctl -n 123 -t polling-sporadic -c 0

Unless there is an error, no output is generated.

The above command created a polling reservation with a default budget of 10ms and a replenishment period of 100ms.

To specify other periods and/or budgets, use the -p and -b options, respectively.

For instance, use the following invocation to create a sporadic polling reservation with RID 1000 on core 1 with a budget of 25ms and a replenishment period of 50ms:

resctl -n 1000 -t polling-sporadic -c 1 -b 25 -p 50

By default, resctl creates EDF-scheduled polling reservations. To create fixed-priority reservations, simply set a numeric priority explicitly with the -q option. In LITMUSRT, a lower numeric priority value corresponds to a higher priority, i.e., 1 is the highest priority in LITMUSRT and 511 is (currently) the lowest possible priority. (The number of distinct priority levels available can be easily changed by recompiling the kernel.)

Deleting a Reservation

There is currently no mechanism to delete individual reservations. Once created, a reservation persists until the scheduler plugin is switched.

To delete all reservations, simply switch the active scheduler plugin to Linux and back again.

setsched Linux
setsched P-RES

Real-time Tasks

There are two ways of running real-time tasks in LITMUSRT. The first is to use the liblitmus API to program a custom real-time application, and the second is to use the rt_launch tool for executing arbitrary Linux binaries as LITMUSRT real-time tasks.

Common to both cases, there are a couple of parameters that should be set before starting a real-time task:

It is further possible to specify a task’s “hardness” as its class; however, this parameter is ignored by all currently included schedulers and has no effect.

In the following, we provide a brief description of the tools in liblitmus related to running real-time tasks. For detailed usage information, run the mentioned commands without any parameters.

Simulating CPU-bound workloads with rtspin

The tool rtspin is a simple test task distributed with liblitmus that follows the first approach, i.e., it uses the API to programmatically “become” a real-time task when launched. The tool executes a simple, configurable spin loop that is useful for simulating CPU-bound workloads. It can be used for test and debugging purposes.

rtspin: simulate a periodic CPU-bound real-time task

	   (3) rtspin -l
	   (4) rtspin -B

Modes: (1) run as periodic task with given WCET and PERIOD
	   (2) as (1), but load per-job execution times from a CSV file
	   (3) Run calibration loop (how accurately are target runtimes met?)
	   (4) Run background, non-real-time cache-thrashing loop (w/ -m).

Required arguments:
	WCET, PERIOD      reservation parameters (in ms)
	DURATION          terminate the task after DURATION seconds

	-B                run non-real-time background loop
	-c be|srt|hrt     task class (best-effort, soft real-time, hard real-time)
	-d DEADLINE       relative deadline, equal to the period by default (in ms)
	-e                turn on budget enforcement (off by default)
	-h                show this help message
	-i                report interrupts (implies -v)
	-l                run calibration loop and report error
	-m FOOTPRINT      specify number of data pages to access
	-o OFFSET         offset (also known as phase), zero by default (in ms)
	-p CPU            partition or cluster to assign this task to
	-q PRIORITY       priority to use (ignored by EDF plugins)
	-r VCPU           virtual CPU or reservation to attach to
	-R                create sporadic reservation for task (with VCPU=PID)
	-s SCALE          fraction of WCET to spin for (1.0 means 100%)
	-u SLACK          randomly under-run WCET by up to SLACK milliseconds
	-v                verbose (print per-job statistics)
	-w                wait for synchronous release

	-F FILE           load per-job execution times from CSV file
	-C COLUMNS        specify column to read per-job execution times from (default: 1)

	-X PROTOCOL       access a shared resource protected by a locking protocol
	-L CS-LENGTH      simulate a critical section length of CS-LENGTH milliseconds
	-Q RESOURCE-ID    access the resource identified by RESOURCE-ID

	WCET and PERIOD are expected in milliseconds.
	SLACK is expected in milliseconds.
	DURATION is expected in seconds.
	CS-LENGTH is expected in milliseconds.
	FOOTPRINT is expected in number of pages

For example, the following command executes the task for 5 seconds on core 2 with a period of 10 ms and a WCET of 1.5 ms.

rtspin -p 2 1.5 10 5

For partitioned plugins (i.e., P-FP, PSN-EDF, P-RES), the partition number ranges from 0 to m - 1, where m is the number of CPUs.

For global schedulers (i.e., GSN-EDF, and PFAIR and C-EDF with the default cluster size “ALL”), the partition option (-p) is irrelevant and should be omitted.

Note that priority assignment is not carried out automatically under fixed-priority schedulers. To specify a task’s priority, use the -q option, which accepts priorities in the range 1–511, where 1 is the highest-possible priority. If the -q option is omitted, rtspin defaults to the lowest-possible priority (511 in the current LITMUSRT version).

When testing schedulers or learning to use LITMUSRT, we recommend to run rtspin with the -v option, which provides some useful feedback on the execution of each job, including the task’s PID and each job’s absolute deadline.

Launching rtspin in a Reservation

The rtspin test application can be assigned to a pre-existing reservation with the -r option.

For example, to assign an rtspin process that runs for three seconds with period 100 and execution time 10 to reservation 1000 on core 1, launch rtspin like this:

rtspin -p 1 -r 1000 10 100 3

Under reservation-based schedulers, task priorities are irrelevant (i.e., ignored). Instead, priorities need to be assigned to reservations, as mentioned in the section “creating a reservation.”

Launching Arbitrary Binaries with rt_launch

Sometimes it is useful to run arbitrary tasks in LITMUSRT real-time mode, either to run “legacy” code or for testing purposes. For this purpose, liblitmus provides a tool called rt_launch, which allows executing arbitrary binaries as real-time tasks with arbitrary parameters. (In some sense, rt_launch can be thought of as the opposite of nice.)

The tool accepts mostly the same parameters as rtspin, but instead of specifying a test duration, simply specify a binary that is to be executed. Any parameters not consumed by rt_launch will be passed as arguments to the to-be-launched binary.

Usage: rt_launch OPTIONS wcet period program [arg1 arg2 ...]
    -w                wait for synchronous release
    -v                verbose (prints PID)
    -p CPU            physical partition or cluster to assign to
    -r VCPU           virtual CPU or reservation to attach to
    -R                create sporadic reservation for task (with VCPU=PID)
    -d DEADLINE       relative deadline, implicit by default (in ms)
    -o OFFSET         offset (also known as phase), zero by default (in ms)
    -q PRIORITY       priority to use (ignored by EDF plugins, highest=1, lowest=511)
    -c be|srt|hrt     task class (best-effort, soft real-time, hard real-time)
    -e                turn off budget enforcement (DANGEROUS: can result in lockup)
    wcet, period      reservation parameters (in ms)
    program           path to the binary to be launched

For example, the below command launches the find utility on processor 1 as a “real-time task” with a period of 100ms and a budget of 10ms.

rt_launch -p 1 10 100 find /

By default, the launched binary will be subject to budget enforcement to prevent runaway processes from taking over the processor. For example, by launching find with a large period (say, 5 seconds), the effects of budget enforcement become easy to notice.

rt_launch -p 1 10 5000 find /

Note that it may be required to pass -- (two dashes) after the last argument intended for rt_launch to stop it from processing options intended for the to-be-launched program. For example, suppose the goal is to print only files ending in .c. The following invocation will result in an error because rt_launch will try to interpret the option -iname and fail.

# Will fail; rt_launch does not understand -iname.
rt_launch -p 1 10 100 find / -iname '*.c'

Instead, use the following invocation to clearly separate options for rt_launch from those for find.

# Works as expected; rt_launch stops option processing at '--'.
rt_launch -p 1 10 100 -- find / -iname '*.c'

Budget enforcement can be turned off with the -e option, but this is dangerous: under a classic plugin, a CPU-bound task will monopolize the CPU and likely lock up the system. Under the newer reservation-based schedulers, the budget enforcement is provided by the reservations (and not on a per-task basis) and cannot be turned off.

Under fixed-priority scheduling, don’t forget to set a priority with the -q option.

Launching Arbitrary Binaries in a Reservation

The rtspin options related to reservations (-p and -r) are also understood by the rt_launch utility. Similar to the prior reservation example, the following command executes find in reservation 1000 on core 1:

rt_launch -p 1 -r 1000 10 100 find /

Attaching an Already Running Task to a Reservation

It is also possible to assign an already running, non-real-time process or thread to a reservation. This can be accomplished with the -a (attach) option of resctl.

For example, to move the current shell into reservation 1000 on core 1, execute the following command:

resctl -a $$ -r 1000 -c 1

As a result, the shell will now be running as a real-time task, subject to the priority and budget replenishment of the given reservation.

Setting Up a (Synchronous) Task System Release with release_ts

In many cases, tasks should not commence running before all tasks have finished initialization. Furthermore, it can simplify a system’s design and analysis if tasks are known to share a common “time zero”.

This is especially true for periodic tasks—typical schedulability analysis for periodic tasks assumes either synchronous task systems, where all tasks release their first job at a common time zero, or asynchronous task systems, where each task releases its first job at a known offset (or phase) relative to a shared time zero.

For these reasons, LITMUSRT has explicit support for setting up and triggering synchronous task system releases (which are basically a form of barrier synchronization).

The tools rtspin and rt_launch support this feature with the -w option, which means wait for a (synchronous) task system release. When set, tasks do not start executing immediately, but wait for a synchronous release point.

The synchronous task release can be triggered by running the release_ts utility. The number of tasks waiting for release, along with the total number of active real-time tasks, can be listed with cat /proc/litmus/stats.


$ setsched GSN-EDF
# launch four tasks waiting for release
$ rtspin -w 10 100 4 &
$ rtspin -w 10 100 4 &
$ rtspin -w 10 100 4 &
$ rtspin -w 10 100 4 &
$ cat /proc/litmus/stats 
real-time tasks   = 4
ready for release = 4
$ release_ts 
Released 4 real-time tasks.

Note that it is possible to specify a number of tasks to wait for before triggering the synchronous release with the -f option of release_ts.

Table-Driven Reservations

LITMUSRT’s P-RES plugin also supports table-driven reservations, which can be used to implement time partitioning (aka static scheduling or time-triggered scheduling).

The basic idea behind table-driven scheduling is to define a static scheduling table that periodically repeats in time. For each point in time, the static scheduling table determines which entity each processor should be executing (if any), and whether any processors are free to execute a background workload (or to idle).

In LITMUSRT, table-driven scheduling is realized by means of a special reservation type in the P-RES plugin. The schedule created by such a table-driven reservation is determined by two principal parameters:

  1. the major cycle M, which is the period of the scheduling table (i.e., at runtime, the schedule repeats every M time units), and
  2. a sequence of non-overlapping scheduling intervals (or slots), where each such scheduling slot [S, E) is defined by a
    • start offset S in the range [0, M) and an
    • end offset E in the range (S, M).

For example, suppose we are given a table-driven reservation with

At runtime, processes of this reservation are eligible to execute during all intervals

for k = 0, 1, 2, 3, …, relative to some reference time zero (e.g., usually the boot time of the system).

On a given processor, all scheduling slots must be disjoint. That is, in a correct scheduling table, at any point in time t and for each processor, there is at most one scheduling interval that contains t.

Creating a Table-Driven Reservation

Since time partitioning is realized with reservations in LITMUSRT, a scheduling table is comprised of one or more table-driven reservations. Each such table-driven reservation must be explicitly created prior to use with the resctl utility.

The general form of the command is:

resctl -n RID -c CPU -t table-driven -m MAJOR-CYCLE SLOT1 SLOT2 SLOT3… SLOT_N

where RID denotes the reservation ID, and CPU denotes the processor on which the reservation is created. Each slot is simply a semi-open interval of the form [START_TIME, END_TIME). All times are specified in (possibly fractional) milliseconds.

For example, the following command instructs resctl to allocate a new table-driven reservation (-t table-driven) with ID 123 (-n 123) on processor 2 (-c 2), with a major cycle of 250ms (-m 250) and scheduling slots as mentioned in the preceding example.

resctl -n 123 -c 2 -t table-driven -m 250 '[50, 60)' '[100, 125)' '[200, 205)'

When using bash or a similar shell, the slot specifications must be quoted to prevent the shell from interpreting the closing parentheses as shell syntax.

If the command succeeds in creating the reservation, there is no output. Otherwise, an error message is shown.

Installing a Scheduling Table

To define an entire scheduling table comprised of many reservations on multiple cores, execute an appropriate resctl command for each reservation in the table.

A typical approach is to store the system’s scheduling table as a simple shell script that enables the P-RES plugin and then runs the required resctl invocations. For example, a table-setup script such as the below example can be run as an init script during system boot, or when launching an experiment.


# Enable plugin
setsched P-RES

# Major cycle of the static schedule, in milliseconds.

# Install table for core 0
resctl -c 0 -n 1 -t table-driven -m $MAJOR_CYCLE …
resctl -c 0 -n 2 -t table-driven -m $MAJOR_CYCLE …
resctl -c 0 -n 3 -t table-driven -m $MAJOR_CYCLE …

# Install table for core 1
resctl -c 1 -n 1001 -t table-driven -m $MAJOR_CYCLE …
resctl -c 1 -n 1002 -t table-driven -m $MAJOR_CYCLE …
resctl -c 1 -n 1003 -t table-driven -m $MAJOR_CYCLE …

# Install table for core 2
resctl -c 2 -n 2001 -t table-driven -m $MAJOR_CYCLE …
resctl -c 2 -n 2002 -t table-driven -m $MAJOR_CYCLE …
resctl -c 2 -n 2003 -t table-driven -m $MAJOR_CYCLE …

While reservation IDs are arbitrary, it is often convenient to use a consistent reservation ID scheme such as ”processor ID” × 1000 + index.

WARNING: While resctl checks that each reservation’s slots do not overlap with other slots of the same reservation, it is possible to (accidentally) specify multiple reservations with overlapping slots. A table with such overlapping slots is erroneous and results in undefined behavior, but not automatically detected. It is the responsibility of the user to install a scheduling table in which all slots are non-overlapping.

Also note that it is possible to specify a different major cycle for each table-driven reservation. If used carefully, this offers some convenient flexibility. For instance, in some cases, it can be used to efficiently store tables that would otherwise consume large amounts of memory (e.g., if the workload’s hyper-period is large). However, this is an advanced feature and requires some careful consideration to be used correctly (i.e., without accidentally creating overlapping slots). For simplicity, it’s best to use a uniform major cycle parameter for all table-driven reservations.

Once all table-driven reservations have been set up, processes can be attached to them just as it is done with any other reservation type.

Coordinating Task Activations

It is often useful to ensure that a periodic task that is assigned to a table-driven reservation always wakes up at the beginning of each scheduling slot. While LITMUSRT does not provide a custom interface for this purpose, it can be accomplished with stock Linux APIs.

Specifically, appropriate task activations can be programmed with the clock_nanosleep() API. The reference time zero of the repeating table schedule is literally time zero on the timeline of CLOCK_MONOTONIC.

Combining Reservation Types

It is possible to combine table-driven scheduling with other reservation types by assigning appropriate reservation priorities. There are two possible use cases:

  1. event-driven reservations that serve as lower-priority background reservations, which are scheduled only when the table is idle, and
  2. higher-priority, throttled reservations that can preempt any tasks in table-driven reservations.

The first use case is the normal scenario (the table takes precedence at all times), but the second use case (the table can be briefly overruled) can also make sense when there is a need support low-latency applications. We briefly sketch both setups.

Lower-Priority Background Reservations

By default, table-driven reservations are given the highest-possible priority, which numerically is priority 1. This means that lower-priority, event-driven reservations can be trivially added simply by giving them any priority other than priority 1 (with resctl’s -q option).

For example, to add a sporadic polling reservation on processor 2 with ID 9999, priority 99, a budget of 100ms, and a period of 500ms, execute the following command:

resctl -n 9999 -c 2  -t polling-sporadic -q 99 -b 100 -p 500

EDF priorities are even lower, so it is possible to combine table-driven reservations with EDF-scheduled background reservations. For instance, the command (which omits the -q option)

resctl -n 8888 -c 2  -t polling-sporadic -b 100 -p 500

adds an EDF-scheduled background reservation.

Higher-Priority Foreground Reservations

It is possible to define fixed-priority reservations that can preempt table-driven reservations at any and all times during table-driven scheduling slots. The primary use case for such a setup is to ensure timely processor service for latency-sensitive tasks (such as certain interrupt threads) that do not consume a lot of processor bandwidth, but that need to be scheduled quickly when new data arrives. This, of course, makes sense only if the higher-priority reservations are tightly rate-limited to ensure that table allocations are not completely starved.

To create a sporadic polling reservation that can preempt table-driven reservations, do the following.

When preempted by a higher-priority reservation, the budget of table-driven reservations is not shifted: any cycles that cannot be used during a scheduling slot due to higher-priority interference are simply lost.

Note that it is possible to give each table-driven reservation a different priority. This allows for precise control over which table-driven reservations may be preempted by event-driven reservations.

In fact, it is even possible to install multiple, intentionally overlapping table-driven reservations at different priority levels. This could potentially be used to realize a notion of mixed-criticality tables such as those described by Baruah and Fohler (2011).

Overhead Tracing

We next discuss how to trace and process system overheads (such as context switch costs, scheduling costs, task wake-up latencies, etc.).

Recording Overheads with Feather-Trace

To record overheads, use the high-level wrapper script ft-trace-overheads in a system running a LITMUSRT kernel that has been compiled with overhead tracing enabled in the kernel configuration (i.e., CONFIG_SCHED_OVERHEAD_TRACE=y).

Use the script as follows. First, activate the scheduler that you are interested in (e.g., GSN-EDF). Then simply run ft-trace-overheads (as root) with a given name to identify the experiment. While ft-trace-overheads is running, execute your benchmark to exercise the kernel. When the benchmark has completed, terminate ft-trace-overheads by pressing the enter key.


$ setsched GSN-EDF
$ ft-trace-overheads my-experiment
[II] Recording /dev/litmus/ft_cpu_trace0 -> overheads_host=rts5_scheduler=GSN-EDF_trace=my-experiment_cpu=0.bin
[II] Recording /dev/litmus/ft_cpu_trace1 -> overheads_host=rts5_scheduler=GSN-EDF_trace=my-experiment_cpu=1.bin
[II] Recording /dev/litmus/ft_cpu_trace2 -> overheads_host=rts5_scheduler=GSN-EDF_trace=my-experiment_cpu=2.bin
[II] Recording /dev/litmus/ft_cpu_trace3 -> overheads_host=rts5_scheduler=GSN-EDF_trace=my-experiment_cpu=3.bin
[II] Recording /dev/litmus/ft_msg_trace0 -> overheads_host=rts5_scheduler=GSN-EDF_trace=my-experiment_msg=0.bin
[II] Recording /dev/litmus/ft_msg_trace1 -> overheads_host=rts5_scheduler=GSN-EDF_trace=my-experiment_msg=1.bin
[II] Recording /dev/litmus/ft_msg_trace2 -> overheads_host=rts5_scheduler=GSN-EDF_trace=my-experiment_msg=2.bin
[II] Recording /dev/litmus/ft_msg_trace3 -> overheads_host=rts5_scheduler=GSN-EDF_trace=my-experiment_msg=3.bin
Press Enter to end tracing...
# [run your benchmark]
# [press Enter when done]
Ending Trace...
Disabling 4 events.
Disabling 4 events.
Disabling 4 events.
Disabling 4 events.
Disabling 18 events.
Disabling 18 events.
Disabling 18 events.
Disabling 18 events.
/dev/litmus/ft_msg_trace3: XXX bytes read.
/dev/litmus/ft_msg_trace0: XXX bytes read.
/dev/litmus/ft_msg_trace1: XXX bytes read.
/dev/litmus/ft_cpu_trace2: XXX bytes read.
/dev/litmus/ft_cpu_trace1: XXX bytes read.
/dev/litmus/ft_cpu_trace3: XXX bytes read.
/dev/litmus/ft_cpu_trace0: XXX bytes read.
/dev/litmus/ft_msg_trace2: XXX bytes read.

For performance reasons, Feather-Trace records overhead data into separate per-processor trace buffers, and treats core-local events and inter-processor interrupts (IPIs) differently. Correspondingly, ft-trace-overheads records two trace files for each core in the system.

  1. The file overheads…cpu=$CPU.bin contains all overhead samples related to CPU-local events such as context switches.

  2. The file overheads…msg=$CPU.bin contains overhead samples stemming from IPIs such as reschedule notifications related to the processor $CPU.

Key=Value Encoding

To aid with keeping track of relevant setup information, the tool encodes the system’s host name and the currently active scheduler plugin in a simple key=value format in the filename.

We recommend to adopt the same encoding scheme in the experiment tags. For example, when running an experiment named “foo” with (say) 40 tasks and a total utilization of 75 percent, we recommend to launch ft-trace-overheads as ft-trace-overheads foo_n=40_u=75, as the additional parameters will be added transparently to the final trace file name.


ft-trace-overheads foo_n=40_u=75
[II] Recording /dev/litmus/ft_cpu_trace0 -> overheads_host=rts5_scheduler=GSN-EDF_trace=foo_n=40_u=75_cpu=0.bin

However, this convention is purely optional.

Automating ft-trace-overheads

It can be useful to terminate ft-trace-overheads from another script by sending a signal. For this purpose, provide the -s flag to ft-trace-overheads, which will make it terminate cleanly when it receives the SIGUSR1 signal.

When recording overheads on a large platform, it can take a few seconds until all tracer processes have finished initialization. To ensure that all overheads are being recorded, the benchmark workload should not be executed until initialization is complete. To this end, it is guaranteed that the string “to end tracing…” does not appear in the script’s output (on STDOUT) until initialization is complete on all cores.

What does Feather-Trace data look like?

Feather-Trace produces “raw” overhead files. Each file contains simple event records. Each event record consists of the following fields (➞ see definition):

The timestamp is typically a raw cycle count (e.g., obtained with rdtsc). However, for certain events such as RELEASE_LATENCY, the kernel records the time value directly in nanoseconds.

Note: Feather-Trace records data in native endianness. When processing data files on a machine with a different endianness, endianness swapping is required prior to further processing (see ftsort below).

Event Pairs

Most Feather-Trace events come as pairs. For example, context-switch overheads are measured by first recording a CXS_START event prior to the context switch, and then a CXS_END event just after the context switch. The context-switch overhead is given by the difference of the two timestamps.

There are two event pairs related to scheduling: the pair SCHED_START-SCHED_END records the scheduling overhead prior to a context switch, and the pair SCHED2_START-SCHE2D_END records the scheduling overhead after a context switch (i.e, any clean-up).

To see which event records are available, simply record a trace with ft-trace-overheads and look through it with ftdump (see below), or have a look at the list of event IDs (➞ see definitions).

Low-Level Tools

The feather-trace-tools repository provides three main low-level tools that operate on raw overhead trace files. These tools provide the basis for the higher-level tools discussed below.

  1. ftdump prints a human-readable version of a trace file’s contents. This is useful primarily for manual inspection. Run as ftdump <MY-TRACE-FILE>.

  2. ftsort sorts a Feather-Trace binary trace file by the recorded sequence numbers, which is useful to normalize traces prior to further processing in case events were stored out of order. Run as ftsort <MY-TRACE-FILE>. ftsort can also carry-out endianness swaps if needed. Run ftsort -h to see the available options.

  3. ft2csv is used to extract overhead data from raw trace files. For example, to extract all context-switch overhead samples, run ft2csv CXS <MY-TRACE-FILE>. Run ft2csv -h to see the available options.

By default, ft2csv produces CSV data. It can also produce binary output compatible with NumPy’s float32 format, which allows for efficient processing of overhead data with NumPy’s numpy.memmap() facility.

High-Level Tools

The feather-trace-tools repository provides a couple of scripts around ftsort and ft2csv that automate common post-processing steps. We recommend that novice users stick to these high-level scripts until they have acquired some familiarity with the LITMUSRT tracing infrastructure.

Post-processing of (a large collection of) overhead files typically involves:

  1. sorting all files with ftsort,

  2. splitting out all recorded overhead samples from all trace files,

  3. combining data from per-cpu trace files and from traces with different task counts, system utilizations, etc. into merged data files for further processing,

  4. counting how many events of each type were recorded,

  5. shuffling and truncating all sample files, and finally

  6. extracting simple statistics such as the observed median, mean, and maximum values.

Note that step 4 is required to allow a statistically meaningful comparison of the sampled maximum overheads. (That is, to avoid sampling bias, do not compare the maxima of trace files containing a different number of samples.)

Corresponding to the above steps, the feather-trace-tools repository provides a number of scripts that automate these tasks.

Sorting Feather-Trace Files

The ft-sort-traces script simply runs ftsort on all trace files. Invoke as ft-sort-traces <MY-TRACE-FILES>. We recommended to keep a log of all post-processing steps with tee.


ft-sort-traces overheads_*.bin 2>&1 | tee -a overhead-processing.log

Sorting used to be an essential step, but in recent versions of LITMUSRT, most traces do not contain any out-of-order samples.

Extracting Overhead Samples

The script ft-extract-samples extracts all samples from all provided files with ft2csv.


ft-extract-samples overheads_*.bin 2>&1 | tee -a overhead-processing.log

The underlying ft2csv tool automatically discards any samples that were disturbed by interrupts.

Combining Samples

The script ft-combine-samples combines several data files into a single data file for further processing. This script assumes that file names follow the specific key=value naming convention already mentioned above:


The script simply strips certain key=value pairs to concatenate files that have matching values for all parameters that were not stripped. For instance, to combine all trace data irrespective of task count, as specified by “n=”, invoke as ft-combine-samples -n <MY-DATA-FILES>. The option --std combines files with different task counts (_n=), different utilizations (_u=), for all sequence numbers (_seq=), and for all CPU IDs (_cpu= and _msg=).


ft-combine-samples --std overheads_*.float32 2>&1 | tee -a overhead-processing.log

Counting Samples

The script ft-count-samples simply looks at all provided trace files and, for each overhead type, determines the minimum number of samples recorded. The output is formatted as a CSV file.


ft-count-samples  combined-overheads_*.float32 > counts.csv

Random Sample Selection

To allow for an unbiased comparison of the sample maxima, it is important to use the same number of samples for all compared traces. For example, to compare scheduling overhead under different schedulers, make sure you use the same number of samples for all schedulers. If the traces contain a different number of samples (which is very likely), then a subset must be selected prior to computing any statistics.

The approach chosen here is to randomly shuffle and then truncate (a copy of) the files containing the samples. This is automated by the script ft-select-samples.

Note: the first argument to ft-select-samples must be a CSV file produced by ft-count-samples.


ft-select-samples counts.csv combined-overheads_*.float32 2>&1 | tee -a overhead-processing.log

The script does not modify the original sample files. Instead, it produces new files of uniform size containing the randomly selected samples. These files are given the extension sf32 (= shuffled float32).

Computing Simple Statistics

The script ft-compute-stats processes sf32 or float32 files to extract the maximum, average, median, and minimum observed overheads, as well as the standard deviation and variance. The output is provided in CSV format for further processing (e.g., formatting with a spreadsheet application).

Note: Feather-Trace records most overheads in cycles. To convert to microseconds, one must provide the speed of the experimental platform, measured in the number of processor cycles per microsecond, with the --cycles-per-usec option. The speed can be inferred from the processor’s spec sheet (e.g., a 2Ghz processor executes 2000 cycles per microsecond) or from /proc/cpuinfo (on x86 platforms). The LITMUSRT user-space library liblitmus also contains a tool cycles that can help measure this value.


ft-compute-stats combined-overheads_*.sf32 > stats.csv

Complete Example

Suppose all overhead files collected with ft-trace-overheads are located in the directory $DIR. Overhead statistics can be extracted as follows.

# (1) Sort
ft-sort-traces overheads_*.bin 2>&1 | tee -a overhead-processing.log

# (2) Split
ft-extract-samples overheads_*.bin 2>&1 | tee -a overhead-processing.log

# (3) Combine
ft-combine-samples --std overheads_*.float32 2>&1 | tee -a overhead-processing.log

# (4) Count available samples
ft-count-samples  combined-overheads_*.float32 > counts.csv

# (5) Shuffle & truncate
ft-select-samples counts.csv combined-overheads_*.float32 2>&1 | tee -a overhead-processing.log

# (6) Compute statistics
ft-compute-stats combined-overheads_*.sf32 > stats.csv

Tracing a Schedule

Whereas Feather-Trace data records how long a scheduling decision or context switch takes, the sched_trace interface instead records which tasks are scheduled at what point and corresponding job releases and deadlines.

Recording a Schedule with sched_trace

The main high-level tool for recording scheduling decisions is the script st-trace-schedule.

To record the execution of a task system, follow the following rough outline:

  1. start recording all scheduling decisions with st-trace-schedule;

  2. launch and initialize all real-time tasks such that they wait for a synchronous task system release (see the release_ts utility in liblitmus);

  3. release the task set with release_ts; and finally

  4. stop st-trace-schedule when the benchmark has completed.


st-trace-schedule my-trace
CPU 0: 17102 > schedule_host=rts5_scheduler=GSN-EDF_trace=my-trace_cpu=0.bin [0]
CPU 1: 17104 > schedule_host=rts5_scheduler=GSN-EDF_trace=my-trace_cpu=1.bin [0]
CPU 2: 17106 > schedule_host=rts5_scheduler=GSN-EDF_trace=my-trace_cpu=2.bin [0]
CPU 3: 17108 > schedule_host=rts5_scheduler=GSN-EDF_trace=my-trace_cpu=3.bin [0]
Press Enter to end tracing...
# [launch tasks]
# [kick off experiment with release_ts]
# [press Enter when done]
Ending Trace...
Disabling 10 events.
Disabling 10 events.
Disabling 10 events.
Disabling 10 events.
/dev/litmus/sched_trace2: XXX bytes read.
/dev/litmus/sched_trace3: XXX bytes read.
/dev/litmus/sched_trace1: XXX bytes read.
/dev/litmus/sched_trace0: XXX bytes read.

As the output suggests, st-trace-schedule records one trace file per processor.

What does sched_trace data look like?

A scheduling event is recorded whenever

Furthermore, the release time, deadline, and completion time of each job are recorded, as are each task’s parameters and the name of its executable (i.e., the comm field in the Linux kernel’s PCB struct task_struct). Finally, the time of a synchronous task system release (if any) is recorded as a reference of “time zero”.

The tool st-dump may be used to print traces in a human-readable format for debugging purposes.

Drawing a Traced Schedule

The pycairo-based st-draw tool renders a trace as a PDF. By default, it will render the first one thousand milliseconds after time zero, which is either the first synchronous task system release (if any) or the time of the first event in the trace (otherwise).


st-draw schedule_host=rts5_scheduler=GSN-EDF_trace=my-trace_cpu=*.bin
# Will render the schedule as schedule_host=rts5_scheduler=GSN-EDF_trace=my-trace.pdf

Invoke st-draw -h for a list of possible options. If the tool takes a long time to complete, run it in verbose mode (-v) and try to render a shorter schedule (-l).

Obtaining Job Statistics

The tool st-job-stats produces a CSV file with relevant per-job statistics for further processing with (for example) a spreadsheet application.


st-job-stats schedule_host=rts5_scheduler=GSN-EDF_trace=my-trace_cpu=*.bin
# Task,   Job,     Period,   Response, DL Miss?,   Lateness,  Tardiness, Forced?,       ACET
# task NAME=rtspin PID=17406 COST=590000 PERIOD=113000000 CPU=254
 17406,     3,  113000000,   17128309,        0,  -95871691,          0,       0,     388179
 17406,     4,  113000000,   12138793,        0, -100861207,          0,       0,     382776
 17406,     5,  113000000,    7137743,        0, -105862257,          0,       0,     382334
 17406,     6,  113000000,    2236774,        0, -110763226,          0,       0,     382352
 17406,     7,  113000000,     561701,        0, -112438299,          0,       0,     559208
 17406,     8,  113000000,     384752,        0, -112615248,          0,       0,     382539
 17406,     9,  113000000,     565317,        0, -112434683,          0,       0,     561602
 17406,    10,  113000000,     379963,        0, -112620037,          0,       0,     377526

There is one row for each job. The columns record:

  1. the task’s PID,
  2. the job sequence number,
  3. the task’s period,
  4. the job’s response time,
  5. a flag indicating whether the deadline was missed,
  6. the job’s lateness (i.e., response time minus relative deadline),
  7. the job’s tardiness (i.e., max(0, lateness)),
  8. a flag indicating whether the LITMUSRT budget enforcement mechanism inserted an artificial job completion, and finally
  9. the actual execution time (ACET) of the job.

Note that the Forced? flag is always zero for proper reservation-based schedulers (e.g., under P-RES). Forced job completion is an artefact of the LITMUSRT legacy budget enforcement mechanism under process-based schedulers (such as GSN-EDF or P-FP).

Writing Real-Time Tasks From Scratch

The tools used so far — primarily, rtspin and rt_launch — are useful for illustrative purposes and to exercise scheduler plugins. However, to actually deploy real real-time workloads on LITMUSRT, closer integration with the system is required. To this end, we discuss in the following how to write real-time tasks that use the LITMUSRT API “from scratch.”

Note that the following is intended as a brief tutorial to provide a starting point. Giving a comprehensive survey of the entire LITMUSRT API and user-space environment is beyond the scope of this guide.

We begin with the most basic case, a simple periodic task.

A Minimal Periodic Task

First, we require a couple of standard system headers for I/O and related tasks.

#include <stdio.h>
#include <unistd.h>
#include <sys/types.h>

The main header to include for functionality specific to LITMUSRT is litmus.h, which is provided by liblitmus.

#include <litmus.h>

We now define the task parameters. Note that this may be determined dynamically at runtime (e.g., via passed parameters, as done by rtspin and rt_launch). The main thing to note is that the LITMUSRT API expects times to be specified in nanoseconds. The ms2ns() helper function provided by liblitmus comes in handy in specifying these parameters more legibly in millisecond granularity.

#define PERIOD      ms2ns(1000)
#define DEADLINE    ms2ns(1000)
#define EXEC_COST   ms2ns(50)

Before going further, it is worth emphasizing that virtually all LITMUSRT APIs can fail at runtime. It is therefore good practice — and strongly recommended — to carefully check all return values. To this end, we define a simple macro for ensuring that syscalls do not fail.

#define CALL( exp ) do { \
        int ret; \
        ret = exp; \
        if (ret != 0) \
            fprintf(stderr, "%s failed: %m\n", #exp); \
        else \
            fprintf(stderr, "%s ok.\n", #exp); \
    } while(0)

The periodic task that we implement is very simple. It increments a counter and then signals exit after ten jobs have been processed.

int i = 0;
int job(void)
    if (i >= 10)
        return 1;
    return 0;

Next, we define the main program and loop that defines the period task. The variable params of type struct rt_task will hold all information related to this task relevant to the kernel.

int main()
    int do_exit;        
    struct rt_task params;

First, we call init_litmus(), which initializes the data structures within liblitmus and is mandatory in order to correctly use liblitmus.


We now set up the parameters for this task by filling in the struct rt_task.

    params.exec_cost = EXEC_COST;
    params.period = PERIOD;
    params.relative_deadline = DEADLINE;

Note that we first call init_rt_task_param() to populate the struct with default values. It is recommended to always first call init_rt_task_param() because the layout of struct rt_task changes from version to version (e.g., sometimes newer LITMUSRT kernels require new fields to be added). Using init_rt_task_param() ensures that all fields have sensible defaults values and thus aids with portability.

After the struct rt_task has been populated, we invoke the system call set_rt_task_param() with the thread ID of the current process to communicate the desired configuration to the kernel.

    CALL(set_rt_task_param(gettid(), &params));

In LITMUSRT, processes start out as background processes and need to be “transitioned” into real-time mode. This is done via the task_mode() function in liblitmus. Note that set_rt_task_param() simply communicates the configuration to the kernel; it does not yet transition the task into real-time mode, which must be accomplished explicitly with the task_mode() API.


At this point, the process is running as a LITMUSRT real-time task.

However, we do not yet want the task to commence the actual real-time computation, for the following reason. In a real system, there is most likely more than one task, and various tasks may depend on one another in complex ways. To ensure that the system is in a consistent state and ready for execution, the real-time task should wait at this point until all tasks of the task system have finished initialization. For this purpose, LITMUSRT provides an API for signaling (synchronous) task system releases, as already mentioned before in the context of rtspin and rt_launch (recall the -w flag).

To await the release of the entire task system, which signals that the initialization phase has finished, we simply call the LITMUSRT API wait_for_ts_release().


This call returns only after the user has run release_ts (or some other process that calls the release_ts() API).

What follows is the main loop of the task. We expect the job function to return whether the loop should exit. (In our simple example, this is signaled by returning 1 when the counter reaches 10.) The key is the sleep_next_period() function, which ensures that the job function is invoked only once per period.

    // Do real-time stuff
    do {
        do_exit = job();
        printf("%d\n", do_exit);
    } while(!do_exit);

In a real application, do_exit could (for example) be set by a signal handler that is invoked when the system is shutting down the real-time task.

Once we are done, we simply transition back to non-real-time mode using again the task_mode() function and then exit from main().

    return 0;

This basic skeleton achieves periodic real-time execution under global schedulers.

Partitioned Schedulers

Under partitioned or clustered scheduling, the task needs to carry out two additional steps as part of its initialization. Let PARTITION denote the ID of the partition/cluster on which the task should be executed.

  1. Migrate to the correct partition/cluster with CALL(be_migrate_to_domain(PARTITION)) prior to transitioning into real-time mode.

  2. Fill in the field cpu of struct rt_task as follows:
    param.cpu = domain_to_first_cpu(PARTITION);

The call to be_migrate_to_domain() ensures that the process is running on (one of) the processor(s) that form(s) the partition/cluster after the call returns. The be_ prefix indicates that the function is available only to non-real-time tasks, i.e., it must be called before transitioning the process into real-time mode.

The call to domain_to_first_cpu() translates a logical domain or cluster ID into a physical core number. This translation layer is useful because, on some systems, physical core IDs are not stable across reboots, so it is more convenient to say “this task belongs to the 3rd cluster as defined by shared L2 caches” rather than identifying which physical cores actually share an L2 cache.

A Minimal Event-Driven Task

From the point of view of the LITMUSRT API, the structure and initialization of an event-driven task is identical to that of a periodic task. The only major difference to a periodic task is simply the main loop of the task.

In the interest of brevity, we do not report the common parts and focus on the main loop instead in the following.

Event-Driven Main Loop

For simplicity, we use STDIN as our “input event channel” (i.e., as the file descriptor from which we receive new events). In a real application, this could (for example) be a device file (e.g., /dev/my-sensor) or a UDP, raw Ethernet, or CAN socket.

The main difference to the periodic task example above is that we do not call sleep_next_period().

 // Do real-time stuff
 do {
    do_exit = job();
 } while(!do_exit);

Instead, the task simply blocks on the file descriptor from which it receives input events (STDIN in this case). To this end, we call read() on the standard input file descriptor at the beginning of each job.

int job(void) {
    char buffer[80];
    CALL(read(STDIN_FILENO, buffer, sizeof(buffer)));
    buffer[79] = 0;

When an event is triggered, the read() unblocks (i.e., the process is resumed) and the “event” is made available to the job, which prints the message unless it receives the word ‘exit’.

    if (strcmp(buffer, "exit") == 0)
        return 1;
    else {
        printf("%s\n", buffer);
        return 0;

Note that even though there is no explicit call to sleep_next_period(), the classic scheduler plugins still police budgets and enforce minimum inter-arrival times for new jobs (as specified by the parameter period in struct rt_task).

Reservation-based plugins such as P-RES also enforce budgets and inter-arrival times (or, really, minimum replenishment time separation), but do so based on the reservation parameters as configured with resctl (and ignore task parameters).

A Faux Event Source

For a quick way to demonstrate event-driven activation, we create a named FIFO using the mkfifo command and give that as the standard input to our event-driven task. As a result, it will block when trying to read from STDIN.

Suppose example_event is the binary of our event-driven real-time task. To manually trigger events, we work with two terminal windows from now on.

Terminal 1:
mkfifo input
tail -f input | ./example_event

Events can be triggered by redirecting STDOUT to the FIFO. However, due to the way named FIFOs work in Linux, we need a long-running process that keeps the FIFO open. Here we just call tail -f on the FIFO and redirect its output to our sporadic real-time task.

Terminal 2:
echo "hello" > input 
echo "world" > input
echo "exit" > input

It should be apparent that the example task running in the first terminal is indeed triggered by the events generated in the second terminal. By giving the task running in the first terminal a large period and a small budget (e.g., several seconds and one millisecond, respectively), the effects of budget enforcement and minimum inter-arrival-time separation can be easily observed by manually triggering many events with short inter-arrival times.

Skeleton Real-Time Tasks

To provide an easy starting point for the development of custom real-time tasks, liblitmus comes with two example skeletons of periodic tasks that are known to compile and work, namely base_task and base_mt_task.

The file bin/base_task.c contains an example sequential, single-threaded real-time task, which we recommend as a basis for developing real-time tasks using the liblitmus API.

The template in the file bin/base_mt_task.c is slightly more involved and shows how to build a multithreaded real-time process, where each thread corresponds to a sporadic task.

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