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<div class="">[Apologies for cross-posting]</div>
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<b class=""> <a href="https://www.sciencedirect.com/journal/engineering-applications-of-artificial-intelligence/about/call-for-papers" class="">Special Issue on Artificial Intelligence for High-performance Computing systems </a></b></div>
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<span class="Apple-tab-span" style="white-space: pre;"></span>in </b></div>
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<span class="Apple-tab-span" style="white-space: pre;"></span> Engineering Applications of Artificial Intelligence (Impact Factor 8.0)</b></div>
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<b class="">Description </b><br class="">
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<div class="">The need for High-Performance Computing (HPC) systems has boomed in the past years. The latter finds its roots in the recent advances in technology, ranging from billions of connected IoT devices generating and transmitting data to the massive
usage of Artificial Intelligence (AI) techniques requiring unprecedented processing power. </div>
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<div class="">AI and HPC are thus strongly intertwined and increasingly being used together to develop new and innovative solutions to a wide range of challenges. <br class="">
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<div class="">One of the main ways that AI is used in HPC is to accelerate the training and deployment of machine learning (ML) models. HPC systems can be used to train ML models much faster and often provide access to specialized hardware, such as GPUs, well-suited
for accelerating machine learning workloads. However designing and implementing algorithms efficiently exploiting such complex and heterogeneous systems opens numerous research challenges. </div>
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On the other hand, AI can also be used in HPC to optimize the performance of HPC systems themselves. AI can help develop new algorithms and scheduling strategies to improve the efficiency of HPC systems, making them better able to handle complex workloads or
more energy efficient. AI can also be used to monitor HPC systems and identify potential problems before they cause outages or performance degradation. </div>
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Topics of interest include, but are not limited to: </div>
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- Design and deployment of AI algorithms on HPC systems, e.g.: deep learning, reinforcement learning, evolutionary algorithms, swarm intelligence, ...); <br class="">
- Distributed and federated learning algorithms on HPC systems; <br class="">
- AI algorithms on GPU, TPU, FPGA, …; <br class="">
- AI for energy efficient HPC and Cloud systems; <br class="">
- AI for scheduling of HPC systems; <br class="">
- Real world applications of AI on HPC systems: e.g., cloud computing, planning, logistics, manufacturing, finance, telecommunications, bioinformatics, etc. <br class="">
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<div class=""><b class="">Manuscript submission information: </b><br class="">
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<div class="">- First Submission Date: 27th October 2023<br class="">
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<div class="">- Final Submission Deadline: 15th June 2024<br class="">
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<div class="">- Notification of Acceptance: 30th July 2024</div>
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Manuscripts must be submitted via the Engineering Applications of Artificial Intelligence online submission system (<a href="https://www2.cloud.editorialmanager.com/eaai/default2.aspx" class="">Editorial Manager®</a>). Please select the article type “<b class="">VSI:
AI for HPC Systems</b>” when submitting the manuscript online. </div>
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Please refer to the <a href="https://www.elsevier.com/journals/engineering-applications-of-artificial-intelligence/0952-1976/guide-for-authors" class="">Guide for Authors</a> to prepare your manuscript. </div>
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For any further information, the authors may contact the Guest Editors. </div>
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<div class=""><b class="">Guest Editors:</b></div>
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<div class="">Dr. Grégoire Danoy (Executive Guest Editor)</div>
<div class="">University of Luxembourg, Luxembourg </div>
<div class=""><a href="mailto:gregoire.danoy@uni.lu" class="">gregoire.danoy@uni.lu</a> </div>
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<div class="">Dr. Eng. Didier El Baz</div>
<div class="">Team SARA, LAAS-CNRS, France,</div>
<div class=""><a href="mailto:elbaz@laas.fr" class="">elbaz@laas.fr</a> </div>
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