Hyperspectral image registration is a relevant task for real-time applications such as environmental disaster management or search and rescue scenarios. The HYFMGPU algorithm was proposed as a single-GPU high-performance solution, but the need for a distributed version has arisen due to the continuous evolution of sensors that generate images with finer spatial and spectral resolutions. In a previous work, we simplified the programming of the multi-device parts of an initial MPI+CUDA multi-GPU implementation of HYFMGPU by means of Hitmap, a library to ease the programming of parallel applications based on distributed arrays. The performance of that Hitmap version was assessed in a homogeneous GPU cluster. In this paper, we extend this implementation by means of new functionalities added to the latest version of Hitmap in order to support arbitrary load distributions for multi-node heterogeneous GPU clusters. Three different load balancing layouts are tested, which prove that selecting a proper layout affects the performance of the code and how this performance is correlated with the use of the GPUs available in the cluster.

高光谱图像配准是实时应用程序的一项相关任务，例如环境灾难管理或搜索和救援方案。HYFMGPU算法是作为单GPU高性能解决方案提出的，但是由于传感器的不断发展，产生了具有更好的空间和光谱分辨率的图像的传感器已经引起了对分布式版本的需求。在先前的工作中，我们借助Hitmap简化了HYFMGPU的初始MPI + CUDA多GPU实现的多设备部分的编程，该库可简化基于分布式阵列的并行应用程序的编程。该Hitmap版本的性能是在同类GPU集群中评估的。在本文中，我们通过在最新版本的Hitmap中添加新功能来扩展此实现，以支持多节点异构GPU集群的任意负载分配。测试了三种不同的负载平衡布局，这证明选择适当的布局会影响代码的性能，以及性能与群集中可用GPU的使用如何关联。