Titlebook: Euro-Par 2023: Parallel Processing Workshops; Euro-Par 2023 Intern Demetris Zeinalipour,Dora Blanco Heras,Pierangelo Conference proceeding

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Towards a Simulation as a Service Platform for the Cloud-to-Things Continuumy of their applications, which are getting more and more crucial. In this paper, we propose a vision for a unified Simulation as a Service platform, which will be able to model and investigate Blockchain-based smart systems exploiting IoT, Fog, and Cloud Computing infrastructures.

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Benchmarking the Parallel 1D Heat Equation Solver in Chapel, Charm,, C,, HPX, Go, Julia, Python, Rusnce of this model code on different computing architectures: Intel, AMD, and ARM64FX. As a result, Python was the slowest of the set we compared. Java, Go, Swift, and Julia were the intermediate performers. The higher performing platforms were C., Rust, Chapel, Charm., and HPX.

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Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Powerl networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were able to find non

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GPPRMon: GPU Runtime Memory Performance and Power Monitoring Toolrmance concerns drive the main optimization efforts, power issues become important for energy-efficient GPU executions. While performance profilers and architectural simulators offer statistics about the target execution, they either present only performance metrics in a coarse kernel function level

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The Implementation of Battery Charging Strategy for IoT Nodescharging process design is one of the focal points for the complete system design. Nowadays, battery charging, for such devices, usually relies on solar power which is not, unfortunately, the source of constant energy. Both environmental and constructive elements could easily make a negative impact

Corral

subMFL: Compatible subModel Generation for Federated Learning in Device Heterogeneous Environmentuting and storage capacities. FL training process enables such devices to update the weights of a shared model locally using their local data and then a trusted central server combines all of those models to generate a global model. In this way, a global model is generated while the data remains loc