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[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><br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>in <br class="">
Engineering Applications of Artificial Intelligence (Impact Factor 8.0)<br class="">
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<b style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); font-family: ArialMT;" class="">Description </b><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); font-family: ArialMT;" class="">
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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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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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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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The organization of this special issue is linked to the <a href="https://pdco2023.sciencesconf.org/" class="">13th IEEE Workshop Parallel / Distributed Combinatorics and Optimization (PDCO 2023)</a>, that took place together with the <a href="http://www.ipdps.org/" class="">37th
IEEE International Parallel and Distributed Symposium (IPDPS 2023)</a> in Saint Petersburg, Florida, USA, May 2023. The most outstanding papers from the workshop will be considered for possible publication in the special issue, after a peer review process. </div>
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<u class="">The special issue is open to any submissions, and not restricted to papers from the PDCO workshop.</u></div>
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<b class="">Manuscript submission information: </b><br class="">
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- Final Submission Deadline (extended): September 15th, 2024 <br class="">
- Notifications of Acceptance: December 31st, 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 “VSI: AI
for HPC Systems” when submitting the manuscript online. <br class="">
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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<b class="">Guest Editors:</b></div>
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Dr. Grégoire DANOY (Executive Guest Editor)</div>
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University of Luxembourg, Luxembourg </div>
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<a href="mailto:gregoire.danoy@uni.lu" class="">gregoire.danoy@uni.lu</a> </div>
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Dr. Eng. Didier El Baz</div>
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Team SARA, LAAS-CNRS, France,</div>
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<a href="mailto:elbaz@laas.fr" class="">elbaz@laas.fr</a> </div>
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