[Updated] Mir – An outpost envisioned as a new home

Some time ago, Canonical started internal discussions about our convergence strategy, clearly spelling out the distant target of shaping and developing a single computing platform and operating system that is able to power the cloud, classic desktop machines, laptops, TV sets, phones and tablets (fridges anyone?). More to this, we stated that we want a single mobile device, a bundle of portable computing power together with the respective operating system, that seamlessly adapts to different form-factors and use-cases. Or to put it a little more catchy:

Your phone is your TV, is your desktop, is your … you name it.

And yes, we were aiming for the moon. We worked hard to draft a system that would allow us to reach the moon, while at the same time catering towards our other central goal of providing a beautiful and lean user experience. It became apparent in conversations and discussions that one of the cornerstones of the future system we were designing will be the graphics stack, including the Unity shell. After much discussions about existing solutions (X & Weston), and how we could leverage them, we took a step back and distilled down our (technical) requirements for a future graphics stack:

• Tailored towards an EGL/GL(ES) world.
• Minimal assumptions regarding the underlying driver model.
• Ability to leverage existing drivers implementing the Android driver model.
• Ability to leverage existing hardware compositors.
• Efficient, in terms of memory-, CPU- and GPU-consumption/usage.
• Tightly integrated with the Unity shell, fulfilling the shell’s requirements while at the same time not dictating any sort of semantics up the stack.
• An efficient and secure input subsystem supporting demanding mobile use-cases.
• Fully tested from the ground up.
• Adaptable to future requirements.

With these priorities in mind, we revisited and carefully evaluated existing solutions again and found that neither of them satisfies our requirements. In particular, X and its driver model is way too complicated and feature-laden, resulting in a less efficient system and a driver model that is unlikely to be widely supported on mobile platforms. In the case of Weston, the lack of a clearly defined driver model as well as the lack of a rigorous development process in terms of testing driven by a set of well-defined requirements gave us doubts whether it would help us to reach the “moon”. We looked further and found Google’s SurfaceFlinger, a standalone compositor that fulfilled some but not all of our requirements. It benefits from its consistent driver model that is widely adopted and supported within the industry and it fulfills a clear set of requirements. It’s rock-solid and stable, but we did not think that it would empower us to fulfill our mission of a tightly integrated user experience that scales across form-factors. However, SurfaceFlinger was chosen as our initial solution for getting started with the overall Ubuntu Touch project, planning to replace SurfaceFlinger with Mir as soon as possible.

In summary, our evaluation of existing solutions led us to the conclusion that neither of them fits our requirements and that adjusting/adapting them would require substantial efforts, too. For this, we decided to go for our own solution, catering directly towards our goals and our vision, effectively saying: Yes, we are going to do our own display server.

The project was born and we decided to name it Mir (as in the space station), as it would be our outpost that finally enables us to reach our goal of arriving at the moon, providing a seamless and beautiful user experience spanning multiple form-factors. The team set off to work on Mir while large parts of Canonical started working on the Ubuntu Touch project (relying on SurfaceFlinger). A lot of knowledge and experience was and is transferred from the Ubuntu Touch project to the Mir team, with the work on the phone and tablet revealing more and more technical requirements and subtleties that heavily influenced the Mir architecture and implementation. Thus, one of the earliest technical decisions that has been taken by the team concerned the protocol that applications rely upon to communicate to Mir. We evaluated Wayland and compared it to the SurfaceFlinger approach, realizing that neither of them fits our needs. Wayland is a very promising and sensible attempt at standardizing the way that applications talk to a display server, however, it exposes privileged sections like the shell integration that we planned to handle differently, both for security reasons and as we wanted to decouple the way the shell works on top of the display server from the application-facing protocol. In the end, we decided to implement Mir’s core in a protocol-agnostic way, considering the communication protocol a very important part of the overall story, but not the driving one. We have chosen Google’s protocol buffers as our data and interface description language (DDL & IDL) and implemented a lean RPC layer that abstract away transport-specific details (relying on an ordinary socket by default). It is well tested and the protobuf IDL and DDL provide us with forward- as well as backward-versioning capabilities. However, the communication component of the overall system is adaptable and can be adjusted to account for future requirements and developments.

A final word on timelines: At the time of this writing, we are preparing to deploy the next version of the Unity shell tightly integrated with Mir in the not-too-distant future on the phone and the tablet. However, regarding the desktop use-case: We have integrated X, leveraging the prior XWayland work, to run on top of Mir (XMir) and started initial porting efforts for toolkits with a focus on Qt5. With our X-integration in place, you can run Mir on your desktop machine if your system runs a GPU that supports the free driver stack. For the closed-source desktop drivers: We are in active conversations with GPU vendors to enable Mir on those drivers/GPUs, too. [Updated] More to this, we are working together with NVIDIA towards a more unified driver model sitting on top of EGL.

As we are moving along with the development of Mir, we will work on integrating existing toolkits with the new display stack to allow application developers a seamless transition between different form-factors and prevent them from the burden of supporting multiple different display servers. As noted before, Qt5 is our first target here, with GTK3 and XUL being in the pipeline. To support legacy X applications that cannot be adjusted to work against Mir, we will provide a translation-layer that leverages the existing XMir implementation and allows legacy X apps to use Mir transparently.

And that’s pretty much it. Thanks for reading. I hope I could give you some insight into the history of Mir, its motivation and its trajectory. If you want to dive even further into the Mir world, head over to wiki.ubuntu.com/MirSpec. A lot of work has already been done, but more of it lies ahead of us. So if you are curious, have questions, want to help us or simply want to get to know the team, join the Mir sessions at the upcoming virtual UDS. You might also want to read Olli’s description of the bigger picture, including our overall convergence strategy for Unity.

To the moon,

Thomas

Large-Scale MOO Experiments with SHARK – Oracle Grid Engine

This post explains how to conduct large-scale MOO experiments with the SHARK machine learning library on clusters running Oracle grid engine.

An experiment consists of three phases:

1. front approximation
2. performance indicator calculation
3. result accumulation and statistics calculation

Within this post, I’m going to focus on the first step.

Front Approximation

In this phases, the Pareto front approximations generated by applying multiple multi-objective evolutionary algorithms (MOEAs) to a set of objective functions are recorded.

Here, I assume that we want to evaluate the (µ+1)-MO-CMA-ES relying on the hypervolume indicator on the DTLZ suite of benchmark functions. A ready-to-use command-line application implementing the MO-CMA-ES is bundled with the default Shark installation. The executable is configurable via command-line arguments queryable by passing –help:

  --objectiveFunction arg
  --seed arg (=1)
  --storageInterval arg (=100)
  --searchSpaceDimension arg (=10)
  --maxNoEvaluations arg (=50000)
  --timeLimit arg (=1000)
  --fitnessLimit arg (=1e-10)
  --resultDir arg (=.)
  --algorithmConfigFile arg
  --algorithmUsage 
  --defaultAlgorithmUsage 
  --objectiveSpaceDimension arg (=2)
  --reportFitnessFunctions 

That is, to execute the MO-CMA-ES for DTLZ2 with 3 objectives and terminating after 50000 objective function evaluations, the following call is required:

  SteadyStateMOCMAMain --objectiveFunction=DTLZ2 --objectiveSpaceDimension=3 --maxNoEvaluations=50000 

Note that we do not specify the rng seed explicitly but rely on the default value 1.

For the scenario considered here, we want to run several independent trials of one specific MOEA and one specific objective function in parallel. To this end, we rely on the array job feature of the grid engine and submit an array of 25 independent trials to the grid engine with the following command:

  qsub -N 'DTLZ2_3' -t 1-25 RunAlgo.sh DTLZ2 /globally/known/path 3

Here, the script RunAlgo.sh is defined as follows:

#!/bin/bash
#$ -S /bin/bash
#$ -o /dev/null

SteadyStateMOCMAMain --seed $SGE_TASK_ID --resultDir=$2 --objectiveFunction=$1 --objectiveSpaceDimension=$3

In summary, the script takes care of actually running the algorithm and setting the seed to environment variable $SGE_TASK_ID. The variable is set by the grid engine to the unique job number and thus, we can ensure independent trials. There is one more thing to note: The result dir needs to be known across the whole cluster. Normally, your dev ops provide you with a scratch environment that is accessible from every computing node.

That’s it. Wait a few minutes until the experiment completes and stay tuned for the second post that explains how to evaluate the quality of the Pareto-front approximations.

(µ+1)-MO-CMA-ES

A brief video of the (µ+1)-MO-CMA-ES solving DTLZ2 with 3 objectives.

Shark 3.x – Continuous Integration

Taken from the SHARK website:

SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides methods for linear and nonlinear optimization, in particular evolutionary and gradient-based algorithms, kernel-based learning algorithms and neural networks, and various other machine learning techniques. SHARK serves as a toolbox to support real world applications as well as research in different domains of computational intelligence and machine learning. The sources are compatible with the following platforms: Windows, Solaris, MacOS X, and Linux.

The library has been in active development for over 10 years now and is in use by scientists all over the world. Last year, we, the core SHARK developers, decided that a rewrite of the library is necessary to support future use cases and provide a solid platform for users and contributors, alike. Our goals were simple:

• Unify and simplify the library structure.
• Rely on established components wherever feasible.
• Documentation, documentation and again, documentation
• Focus on quality.

In this post, I would like to dive a little deeper into the topic of quality and the processes that we established to ensure a constant and high level of quality. We decided to address quality both from a technical (read: testable) and from an API point of view.

In terms of API quality, we want the programming interface to be consistent, convenient to use and easy to extend. In equivalence to the user experience, we want potential developers to experience a welcoming and friendly environment. As we are a geographically distributed team of developers and scientists, we decided to go for a pre-commit code review approach implemented with the help of ReviewBoard. Despite initial concerns on behalf of the developers, the review process proved to be one of the most useful tools while rewriting the library with developers starting to like the final “Ship It” quickly.

In terms of “technical” quality, we decided to go for continous integration of all (reviewed) commits to the rewrite branch for all of our supported platforms. With the help of Jenkins and a bunch of virtual machines, we finally realized our idea of continous integration testing to prevent from regressions. Our unit test suite is implemented with the unit testing framework provided by boost. Test execution is handled by CTest. Static and dynamic analysis of the library is carried out with the help of cppcheck and valgrind, respectively. Code coverage metrics are calculated with the help of gcov. Finally, we are integrating all of the testing results in the job-specific views of our Jenkins instance, thereby providing developers a single source of information on the state of the library.

A first post

iN fAiRy dUsT wE tRuSt – the slogan of the Chaos Computer Club, one of the oldest hacker organizations in the world.