Analysis of Schedule and Layout Tuning for Sparse Matrices With Compound Entries on GPUs

Mueller-Roemer, Johannes Sebastian; Sourander, André; Fellner, Dieter W.

doi:10.1111/cgf.13957

Cited by 1 publication

(1 citation statement)

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“…Workload is generated via the finite element (FE) simulation RISTRA [17]. RISTRA is a highly optimized, GPUaccelerated FE simulation package that performs both system assembly [18] and solution [19] on the GPU, while making use of the 3×3-block structure of the system matrices [20].…”

Section: A Iot Devicementioning

confidence: 99%

Generative Machine Learning for Resource-Aware 5G and IoT Systems

Piatkowski

Mueller-Roemer

Hasse

et al. 2021

2021 IEEE International Conference on Communications Workshops (ICC Workshops)

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Extrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system-allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible-e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.

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Section: A Iot Devicementioning

confidence: 99%