videos

Media

MANGO DemosHARPA DemosUse casesFeatures STHORM Platform2PARMA Demos

This page collects a set of videos and screencasts related to the either features demonstration or demo of the BarbequeRTRM framework.

BarbequeRTRM Screencast

Features carousel showing some interesting BarbequeRTRM features.


MANGO Project Demos

The demo videos released for the H2020 MANGO project review (http://www.mango-project.eu/).

MANGO boot with test application execution

Early MANGO platform integration demo. The current version includes two “PEAK” many-core processors (based on MIPS). The BarbequeRTRM is responsible of starting the PEAK-side runtime (PEAKos) and enumerate the computing resources. A demo application (matrix multiplication) is then launched with the BarbequeRTRM allocating one of the two processors for the execution.

Video

HARPA Project Demos

The demo videos released for the FP7 HARPA project review (http://www.harpa-project.eu/).

CPU thermal control on a ARM big.LITTLE Octa-core CPU

The scenario includes four instances of an image processing application provided by Henesis (http://www.henesis.eu/) managed by the BarbequeRTRM exploiting the TEMPURA policy (version 1).The CPU temperature is kept under a configurable safety threshold.

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Performance-aware CPU resource allocation on multi-processor systems

The scenario includes four concurrently running instances of a flood prediction model, monitoring four areas. The application is provided by IT4Innovations (http://www.it4i.cz/). The BarbequeRTRM allocates the CPU resources according to the run-time varying performance requirements of each instance.

Video


Use-cases

Surveillance System Demo

This is an example of a BarbequeRTRM-managed multi-camera surveillance system which is capable of resource reshuffling according to target proximity to an engage zone.

In this scenario four instances of an OpenCL coded MultiView application are spawned to process four pairs of stereo video streams. The more a moving subject is near to a stereo-camera the more resources are assigned to that stream, thus enabling for an increased frame-rate. The scene has been created using Blender, the Open Source 3D modeling tool.

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Runtime Tunable MultiView - University Boot @ DATE13

This demo shows the results of combining application adaptivity capabilities to system-wide resource management.

By exploiting a DSE-profiled application (MultiView) and the run-time resource manager BarbequeRTRM, we highlight the benefits of this approach:

  • Effective resource usage according to dynamic performance requirements of the application
  • Resource contention solution by performing a multi-objective optimization in the scheduling/resource partitioning policy
  • Robustness and fault tolerance due to the overall system reconfigurability

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Multi-Video Playback Controlled by BarbequeRTRM

In this demo, four OpenCV applications are started concurrently. They are multiple instances of the same application code, competing among them on the usage of the available resources, which are represented by a single CPU since other resources are allocated to high-priority applications. Each video decoder receives a faire amount of 25% of CPU time. Each application run a QoE run-time manager policy which try to keep an actual 23 [FPS] rate.

After a while, a FAST feature extraction function is enabled on one of the video decoders. This kind of processing, while still matching the frame-rate target, requires more resources, which are asked to the BBQ system-wide resource manager, thus triggering a new resource partitioning among the four (equal priority) video streams. The result is a scheduling decision where the FAST enabled application is given more resources (i.e. processing time, 50%) which are reclaimed from others applications within the same priority level. Moreover, these applications scale down their resolution in order to keep in pace with the frame-rate goal.

This demo shows that the BarbequeRTRM is able to:

  • Fairly partition remaining resources on mid-prio applications
  • Re-allocate resources within each priority level based on application requirements
  • Efficiently support the development of self-adapting applications which tunes their parameters (e.g. resolution) to meet their QoE goal.

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FaceDetect on Android

In this demo the BarbequeRTRM has been deployed - as a native component - to Android OS (here running on Jelly Bean 4.2.1).

As master thesis project, it has been developed the glue code needed to let Android Apps (Java coded) exploiting BarbequeRTRM features, through Android NDK and JNI features.

This demo shows an Android App (Java coded) which relies on a specific Android Service, extending the BarbequeService, which interacts with the native BarbequeRTRM through a specific library. The face recognition algorithm is just emulated, no real coordinates are being computed.

The code is freely available on-line: https://bitbucket.org/atroina/bbquesthormfd/overview within the branch named “bosp_android”

The master thesis is available in PDF for download, as well as the presentation.

Video


Features

BarbequeRTRM controlled Android Applications using JNI wrapper

Testing BarbequeRTRM deployed under Android OS, through a Java Application which uses JNI interface to link the Java and the native worlds.

External Link


STHORM Platform by STMicroelectronics

First boot

This is the really first boot of the BarbequeRTRM on the SThorm platform demo board.

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Multiview on SThorm

A similar MultiView application but this time a version coded in OpenCL to run on the STHORM platform by STMicroelectronics.

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First power budget control policy

An example of execution of a test application with a resource allocation policy for the SThorm platform, aiming at assigning computing resources within a given power budget.

Video


2PARMA Project Demos

04/2013 - BarbequeRTRM Stability and Robustness Analysis of Control Policies

This demo shows the Stability & Robustness enforcement features of the BarbequeRTRM framework, targeting both applications requirements (AR) and resources availability (RA)

The goal of the demo is to show the BarbequeRTRM capabilities on:

  • Filter high variability GoalGap assertions
  • Constraint applications run-time management overheads
  • Trigger reconfigurations based on resources availability increases
  • Reject unstable resources usage

Video

03/2012 - BarbequeRTRM v0.8 (Betty Bacon)

This demo shows the setup of a generic x86 (4×4 cores) NUMA platform, to give the Barbeque RTRM control on a set of processing resources, and different scenarios of workload management.

This demo shows that the BarbequeRTRM is able to:

  • schedule resources considering multiple optimization metrics, i.e. performances, reconfiguration overheads, congestion control and fairness
  • assign resources to demanding applications and enforcement this assignment by exploiting a run-time dynamic configuration of Linux Control Groups (CGroups).

Video

11/2011 - BarbequeRTRM v0.6 (Angus)

In this demo, two SVC decoding are started concurrently but with different priorities: the upper video has high-prio (HR_SVC), while the lower one has low-prio (LR_SVC). The upper left screen show that a different cluster resource is assigned to each decoder.

After a while another low-priority workload (BW) is started, which spawns tree EXC competing on resources access with the SVC decoding application.

This demo shows that the BarbequeRTRM is able to:

  • Grant resources to the high-priority
  • Shrink LR_SVC resources, thus reducing playback quality, to make room for the BW

Video


videos.txt · Last modified: 2017/06/13 18:43 by jumanix

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