1,090 research outputs found
Power Management Techniques for Data Centers: A Survey
With growing use of internet and exponential growth in amount of data to be
stored and processed (known as 'big data'), the size of data centers has
greatly increased. This, however, has resulted in significant increase in the
power consumption of the data centers. For this reason, managing power
consumption of data centers has become essential. In this paper, we highlight
the need of achieving energy efficiency in data centers and survey several
recent architectural techniques designed for power management of data centers.
We also present a classification of these techniques based on their
characteristics. This paper aims to provide insights into the techniques for
improving energy efficiency of data centers and encourage the designers to
invent novel solutions for managing the large power dissipation of data
centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy
Efficiency, Green Computing, DVFS, Server Consolidatio
The potential of programmable logic in the middle: cache bleaching
Consolidating hard real-time systems onto modern multi-core Systems-on-Chip (SoC) is an open challenge. The extensive sharing of hardware resources at the memory hierarchy raises important unpredictability concerns. The problem is exacerbated as more computationally demanding workload is expected to be handled with real-time guarantees in next-generation Cyber-Physical Systems (CPS). A large body of works has approached the problem by proposing novel hardware re-designs, and by proposing software-only solutions to mitigate performance interference. Strong from the observation that unpredictability arises from a lack of fine-grained control over the behavior of shared hardware components, we outline a promising new resource management approach. We demonstrate that it is possible to introduce Programmable Logic In-the-Middle (PLIM) between a traditional multi-core processor and main memory. This provides the unique capability of manipulating individual memory transactions. We propose a proof-of-concept system implementation of PLIM modules on a commercial multi-core SoC. The PLIM approach is then leveraged to solve long-standing issues with cache coloring. Thanks to PLIM, colored sparse addresses can be re-compacted in main memory. This is the base principle behind the technique we call Cache Bleaching. We evaluate our design on real applications and propose hypervisor-level adaptations to showcase the potential of the PLIM approach.Accepted manuscrip
A survey of techniques for reducing interference in real-time applications on multicore platforms
This survey reviews the scientific literature on techniques for reducing interference in real-time multicore systems, focusing on the approaches proposed between 2015 and 2020. It also presents proposals that use interference reduction techniques without considering the predictability issue. The survey highlights interference sources and categorizes proposals from the perspective of the shared resource. It covers techniques for reducing contentions in main memory, cache memory, a memory bus, and the integration of interference effects into schedulability analysis. Every section contains an overview of each proposal and an assessment of its advantages and disadvantages.This work was supported in part by the Comunidad de Madrid Government "Nuevas TĂ©cnicas de Desarrollo de Software de Tiempo Real Embarcado Para Plataformas. MPSoC de PrĂłxima GeneraciĂłn" under Grant IND2019/TIC-17261
Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study
In this paper, we address the industrial challenge put forth by ARM in ECRTS
2022. We systematically analyze the effect of shared resource contention to an
augmented reality head-up display (AR-HUD) case-study application of the
industrial challenge on a heterogeneous multicore platform, NVIDIA Jetson Nano.
We configure the AR-HUD application such that it can process incoming image
frames in real-time at 20Hz on the platform. We use micro-architectural
denial-of-service (DoS) attacks as aggressor tasks of the challenge and show
that they can dramatically impact the latency and accuracy of the AR-HUD
application, which results in significant deviations of the estimated
trajectories from the ground truth, despite our best effort to mitigate their
influence by using cache partitioning and real-time scheduling of the AR-HUD
application. We show that dynamic LLC (or DRAM depending on the aggressor)
bandwidth throttling of the aggressor tasks is an effective mean to ensure
real-time performance of the AR-HUD application without resorting to
over-provisioning the system
Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles
The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has
received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking
received support from the European Union’s Horizon 2020 research and innovation programme and Germany,
Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy,
Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL
Joint Undertaking under grant agreement No. 692455-2
Intelligent Management of Mobile Systems through Computational Self-Awareness
Runtime resource management for many-core systems is increasingly complex.
The complexity can be due to diverse workload characteristics with conflicting
demands, or limited shared resources such as memory bandwidth and power.
Resource management strategies for many-core systems must distribute shared
resource(s) appropriately across workloads, while coordinating the high-level
system goals at runtime in a scalable and robust manner.
To address the complexity of dynamic resource management in many-core
systems, state-of-the-art techniques that use heuristics have been proposed.
These methods lack the formalism in providing robustness against unexpected
runtime behavior. One of the common solutions for this problem is to deploy
classical control approaches with bounds and formal guarantees. Traditional
control theoretic methods lack the ability to adapt to (1) changing goals at
runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled
system (i.e., self-optimization).
In this chapter, we explore adaptive resource management techniques that
provide self-optimization and self-adaptivity by employing principles of
computational self-awareness, specifically reflection. By supporting these
self-awareness properties, the system can reason about the actions it takes by
considering the significance of competing objectives, user requirements, and
operating conditions while executing unpredictable workloads
- …