The topology optimization method (TOM) is a valuable tool to obtain conceptual designs in many scientific fields. However, small-scale problems have traditionally been considered due to the high computational resources this method demands. For example, hundreds of costly optimization iterations are needed, in which millions of design variables are used and where simulation of complex multiphysics phenomena could be required. To address this difficulty, the computing capacity can be increased, or efficient code implementations can be used, or a combination of both. Herein, the computing capacity and efficiency are increased simultaneously by programming parallel codes for running on the central processing unit (CPU) and on the graphics processing unit (GPU). A multiphysics problem is used as the optimization application. Specifically, electro-thermo-mechanical (ETM) microactuators are designed by TOM. To achieve this goal, three computer code versions are developed: one optimized sequential code, another using the parallelism offered by the CPU, and a third one using parallel computing on the GPU. Typical code performance metrics such as the execution time and their acceleration are measured. Additionally, an energy consumption analysis is performed for the first time in the context of parallel computing for topology optimization, which is an important topic from large-scale supercomputers to laptops that seek energy-aware methods. The results show that topologies are obtained up to 25 times faster with up to 93% less power consumption when parallel computing is used. This time reduction in TOM allows increasing the topology resolution, the inclusion of multiple physics, and significant energy savings.

拓扑优化方法（TOM）是在许多科学领域中获得概念设计的宝贵工具。但是，由于该方法需要大量的计算资源，传统上一直在考虑小规模的问题。例如，需要数百次昂贵的优化迭代，其中使用了数百万个设计变量，并且可能需要模拟复杂的多物理现象。为了解决这个困难，可以增加计算能力，或者可以使用有效的代码实现方式，或者将两者结合使用。在此，通过对并行代码进行编程以同时在中央处理单元（CPU）和图形处理单元（GPU）上运行，可以同时提高计算能力和效率。多物理场问题用作优化应用程序。特别，电热机械（ETM）微执行器是由TOM设计的。为实现此目标，开发了三种计算机代码版本：一种是优化的顺序代码，另一种是使用CPU提供的并行性，第三种是在GPU上使用并行计算。测量典型的代码性能指标，例如执行时间及其加速。此外，首次在并行计算的上下文中进行能耗分析以进行拓扑优化，这是从大型超级计算机到寻求节能方法的笔记本电脑的重要课题。结果表明，使用并行计算时，拓扑的获得速度提高了25倍，功耗降低了93％。TOM的这种时间减少可以提高拓扑分辨率，包括多种物理原理，