Architecture of Computing Systems (Lecture Notes in Computer ...

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44 P. Bellasi, W. Fornaciari, and D. Siorpaes Fig. 4. CPM: overhead of FSC identification and selection problem. These constraints are used to invalidate all FSCs that violate them. The optimal feasible solution is the first one valid in the list of the ordered FSCs. In the previous example, FSC3 is invalidated by the assertion of the constraint v3 and so the optimal solution is the next one valid: FSC1. 4 Experimental Results The proposed optimization model has been implemented in the “Constrained Power Manager” (CPM), a Linux framework based on kernel 2.6.32. This implementation has been used as a workbench for a worst-case complexity analysis of the developed optimization algorithms, using a demo-board with the Nomadik STn8815 MPSoC by STMicroelectronics. In this section we present: first the test results, and then a use-case to demonstrate the application of the framework in a real usage scenario targeting the power and performance optimization of a multimedia mobile device. 4.1 Overhead’s Evaluation We have measured the execution time of the algorithms for the identification and the selection of FSCs obtaining the results in Fig. 4. These overhead measurements refer to a 60s execution of the use-case and focus on the worst-case. This measures prove the negligible impact of the framework with respect to a system not using it. Indeed, the identification algorithm shows a maximum of 2.5% overhead for a quite complex system with 4096 feasible configurations, which is much more than the 415 of the considered use-case. This means that over the 60s of use-case execution, around 1.5s are devoted to the framework execution. However it should be considered that this algorithm runs just one time at system boot and can be easily replaced by a look-up table. Indeed,
Hierarchical Distributed Control of Power and Performances 45 especially in embedded systems, where the platform’s configuration for a final product does not change, all the FSC can be pre-computed and then just loaded at boot time. While the identification algorithm has a complexity which is exponential in the number of the FSCs, the selection algorithm not only has a better (linear) complexity but is also three orders of magnitude better in absolute values. This is also another important result, because the identification algorithm is the one executed more frequently, i.e. each time a new requirement is asserted by an application. The experimental setup considered one run every 10 seconds and the measurements show a really negligible overhead which is always less than 0.01%. 4.2 Use Case Definition The presented use case shows the benefits of using CPM to manage resources such as the Internet connection bandwidth according to the actual applications demand. Moreover it shows how CPM can keep track of dependency between subsystems. The STn8815 SoC has an ARM host CPU and two accelerators for multimedia: an audio DSP (DSP A) and a video DSP (DSP V). The host CPU is clocked by a signal identified with CPU CLK, while the DSPs are clocked with a signal labeled DSP CLK. The SoC architecture is characterized by a strict dependency between CPU CLK and DSP CLK which constrains the operational frequency of the accelerators. We considered the following ASMs in the use case: – connection bandwidth: a resource on which applications compete. An additive function is used to aggregate the requirements, asserted by applications, and identify a constraint on the QoS level for this resource. – audio and video codecs: an information that is related to multimedia content that the application has to play, thus it directly impacts on the operating mode of the DSPs. The PSMs (DSP CLK and CPU CLK) are platform specific informations defined in the platform code to keep track of hardware inter-dependencies described so far. Finally, the driver of each involved device defines its own DWRs as represented in Tab. 1a, while the platform code define the architectural constraints represented in Tab. 1b. The use case begins with the user selecting a video stream content to be played. As soon as the download of audio and video data starts, the player application collects informations about the connection bandwidth required to have good playback (264 Kbit/s) 1 , the audio codec (mp3) and video codec (h.263) of the encoded content and set a QoS requirement on the corresponding ASMs. These requirements are expressed as a lower bound value on the ASM bandwidth and as a single value on the ASMs audio codec and video codec. 1 i.e., no jitters and buffer underruns.
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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130 M. Kayaalp et al. INSTRUCTION 1
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Abdullah, Tariq 174 Alima, Luc Onan
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