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The AMPERE Project: : A Model-driven development framework for highly Parallel and EneRgy-Efficient computation supporting multi-criteria optimization

机译：安培项目：：一种用于支持多标准优化的高度平行和节能计算的模型驱动的开发框架

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Eduardo Quinones; Sara Royuela; Claudio Scordino; Paolo Gai; Luis Miguel Pinho; Luis Nogueira; Jan Rollo; Tommaso Cucinotta; Alessandro Biondi; Arne Hamann; Dirk Ziegenbein; Hadi Saoud; Romain Soulat; Bjorn Forsberg; Luca Benini; Gianluca Mando; Luigi Rucher;
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2. A highly parallelized framework for computationally intensive MR data analysis [J] . Roland N. Boubela, Wolfgang Huf, Klaudius Kalcher, Magnetic Resonance Materials in Physics, Biology and Medicine . 2012,第4期

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3. Parallel Computing Framework for Optimizing Construction Planning in Large-Scale Projects [J] . Amr Kandil, Khaled El-Rayes Journal of Computing in Civil Engineering . 2005,第3期

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4. The AMPERE Project: : A Model-driven development framework for highly Parallel and EneRgy-Efficient computation supporting multi-criteria optimization [C] . Eduardo Quiñones, Sara Royuela, Claudio Scordino, IEEE International Symposium on Real-Time Distributed Computing . 2020

机译：AMPERE项目：：一种模型驱动的开发框架，用于高度并行和节能的计算，支持多准则优化
5. Improving performance and energy of parallel sparse computations through hybrid linear solvers and model-driven optimization. [D] . Booth, Joshua Dennis. 2014

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6. Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch [O] . Floris Laporte, Joni Dambre, Peter Bienstman -1

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The AMPERE Project: : A Model-driven development framework for highly Parallel and EneRgy-Efficient computation supporting multi-criteria optimization

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