The simulation of ultrashort two-dimensional double gate metal-oxide semiconductor field-effect transistors and similar semiconductor devices through a deterministic mesoscopic, hence accurate, model can be very useful for the industry: It can provide reference results for macroscopic solvers and properly describe weakly charged zones of the device. For the scope of this work, we use a Boltzmann–Schrödinger–Poisson model. Its drawback is being particularly costly from the computational point of view, and a purely sequential code may take weeks to simulate high voltages. In this article, we develop a hybrid parallel solver for a graphics processing unit (GPU)-based platform. In order to accelerate the simulations, the Boltzmann transport equations are solved on GPU using the CUDA programing model, while the Schrödinger–Poisson block is performed on multicore CPUs using OpenMP. We have adapted the costliest computing phases to the GPU in an efficient manner, achieving high performance and drastically reducing the simulation time. We give details about the parallel-design strategy and show the performance results.

中文翻译：

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用于超短 DG-MOSFET 的确定性求解器的混合 OpenMP-CUDA 并行实现

通过确定性介观模拟超短二维双栅金属氧化物半导体场效应晶体管和类似的半导体器件，因此准确的模型对行业非常有用：它可以为宏观求解器提供参考结果，并正确地描述弱设备的充电区。对于这项工作的范围，我们使用 Boltzmann-Schrödinger-Poisson 模型。从计算的角度来看，它的缺点是成本特别高，而且纯顺序代码可能需要数周时间来模拟高电压。在本文中，我们为基于图形处理单元 (GPU) 的平台开发了一个混合并行求解器。为了加速模拟，玻尔兹曼传输方程使用 CUDA 编程模型在 GPU 上求解，而薛定谔-泊松块是在使用 OpenMP 的多核 CPU 上执行的。我们以高效的方式将最昂贵的计算阶段调整到 GPU，从而实现高性能并显着减少模拟时间。我们详细介绍了并行设计策略并展示了性能结果。