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Hybrid OpenMP-CUDA parallel implementation of a deterministic solver for ultrashort DG-MOSFETs

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

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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-Schrodinger-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 Schrodinger-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.
机译:通过确定的介观,因此精确的模型对超短二维双栅极金属氧化物半导体场效应晶体管和类似半导体器件进行仿真对于行业非常有用:它可以为宏观求解器提供参考结果,并能适当地弱描述设备的充电区。对于本文的范围,我们使用Boltzmann-Schrodinger-Poisson模型。从计算的角度来看,它的缺点是特别昂贵,并且纯粹的顺序代码可能需要数周时间才能模拟高压。在本文中,我们为基于图形处理单元(GPU)的平台开发了混合并行求解器。为了加速仿真,使用CUDA编程模型在GPU上求解了Boltzmann传输方程,而在使用OpenMP的多核CPU上执行了Schrodinger-Poisson块。我们已经以高效的方式将最昂贵的计算阶段适应了GPU,从而实现了高性能并大大减少了仿真时间。我们提供有关并行设计策略的详细信息并显示性能结果。

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