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Commit 6548e813 authored by Muddsair Sharif's avatar Muddsair Sharif
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Update img/hpc/bwhpc.jpg, img/hpc/concept.png, img/hpc/nontemp.jpg,... 

Update img/hpc/bwhpc.jpg, img/hpc/concept.png, img/hpc/nontemp.jpg, img/hpc/nontemp.png, img/hpc/tempapproach.png, img/hpc/gm4lab.jpg, img/hpc/ucicity2021.png, info_hpc.html, help/hpc.html files
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help/hpc.html
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<p>
To access the High-Performance Computing platform, one must have registered themselves with the organization that provides such services. In our case, users must have to register themselves with KIT. It is an organization that is providing services to access the high-performance cluster sponsored by Baden-Württemberg. Please visit the link: <a href="https://wiki.bwhpc.de/e/BwUniCluster_2.0_User_Access" target="_blank">how to register</a> to get the registration done.
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<img style="width:100%" src="img/hpc/bwhpc.JPG" alt="hpc picture here">
<img style="width:100%" src="/img/hpc/bwhpc.JPG" alt="hpc picture here">
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<p>We introduce a state of the art template-based approach using Gitlab-CI/CD in which the user has to copy the predefined template (link of an HPC test project that contains predefined template) where the user has to provide information such as login information, module, or application repository, objective and/or set of resources required to compute information and etc. over HPC-platform. We have already presented this approach at a scientific conference. For further information please read out our published work which explains concretely with a use-case study.
<br/>for more information.
</p>
<img style="width:100%" src="img/hpc/tempapproach.png" alt="template base appraoch">
<img style="width:100%" src="/img/hpc/tempapproach.png" alt="template base appraoch">
<br/>1. COaaS:Continuous Integration and Delivery framework for HPC using Gitlab-Runner, BDIOT2020, the fourth international conference on Big Data, Singapore.
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<p>
In a non-template-based approach, after login, the user must follow the appropriate instruction given in the following link: <a href="https://wiki.bwhpc.de/e/Batch_Jobs" target="_blank"> non-template-based instruction</a>, that is suitable for their application to compute over the HPC-platform. These instructions may contain(s) software-module(s), different compilers, numerical libraries, etc.
</p>
<img style="width:100%" src="img/hpc/nontemp.png" alt="non template appraoch">
<img style="width:100%" src="/img/hpc/nontemp.png" alt="non template appraoch">
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info_hpc.html
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Machine Learning can work very well with image recognition, But it can also be used to recognize audio patterns. Machine Listening can be used to identify audio patterns of different entities like car engine, human speaking, nature sounds etc. Aim of this thesis is to classify different vehicles based on their sounds and then further categorize them as either light weight, medium weight, heavy weight, rail- bound or two-wheeled vehicle using the applications of Machine Listening in the field of acoustics. In order to increase the speed and performance of the software program and algorithm, the program will run on a High Performance Computing (HPC) system containing cluster which in turn will have many compute servers also called as nodes which will unable faster and parallel computing.
<br/>for more information.
</p>
<img style="width:100%" src="img/hpc/acoustic.png" alt="template based approach">
<img style="width:100%" src="/img/hpc/acoustic.png" alt="template based approach">
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<p>
In an energy community, the prosumers' interactions are critical to ensure efficient resource distribution, e.g., renewable energy sources, and to reach ambitious climate and economic goals. A typical paradigm of a local energy sharing platform consists of many prosumers and an agent that coordinates the energy transactions between prosumers. The coordinating agent, typically known as the market agent, acts according to a set of rules that enable it to match one prosumer's renewable energy surplus with the deficit of another. This article describes an agent-based modeling strategy and a case-study to demonstrate the prosumer interactions in an energy community. Each prosumer agent in the modeled environment intends to maximize its renewable energy self-consumption. At the same time, the energy community, as a whole, also would like to maximize its collective renewable self-consumption. The prosumers attempt to achieve their individual and collective objectives by following either a locally optimal or rule-based strategy. In both scenarios, prosumers have no visibility of other prosumers; therefore, the market agent has the sole responsibility of orchestrating the energy exchanges between prosumers. Finally, we discuss the significance and future research outlook for energy interaction modeling at a community scale.
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<img style="width:100%" src="img/hpc/concept.png" alt="Energy co-planning">
<img style="width:100%" src="/img/hpc/concept.png" alt="Energy co-planning">
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<p>
Electromobility has profound economic and ecological impacts on human society. Much of the mobility sector's transformation is catalyzed by digitalization, enabling many stakeholders, such as vehicle users and infrastructure owners, to interact with each other in real-time. This article presents a new concept based on deep reinforcement learning to optimize agent interactions and decision making in a smart-mobility eco-system. The algorithm performs context-aware, constrained-optimization that fulfills on-demand requests from each agent. The algorithm can learn from the surrounding environment until the agent interactions reach an optimal equilibrium point in a given context. The methodology implements an automatic template-based approach via a CI/CD framework using a GitLab runner and transfers highly computationally intensive tasks over a high performance compute cluster automatically without manual intervention.
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<img style="width:100%" src="img/hpc/ucicity2021.png" alt="casmart2charge">
<img style="width:100%" src="/img/hpc/ucicity2021.png" alt="casmart2charge">
<br/>1. CA-Smart2Charge:Context-Aware optimal charging distribution using Deep Reinforcement Learning, BDIOT2020, the fourth international conference on Big Data, Singapore.
<br/>2.ARaaS:context aware optimal charging distribution as a service using deep reinforcement learning iCity_2021: Towards liveable, intelligent and sustainable future cities.
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