Iterative and time-step methods are spread far and wide in several mathematics and physics domains. At the same time, modern computers include multicore CPUs along with GPUs, so it is important to use all their computing capabilities for their efficient use. Aiming to improve performance of this kind of numerical methods, we introduce in this work a new heterogeneous parallelism CPU + GPU scheme which we call IHP. This new scheme has the advantage of being self-balanced and able to dynamically distribute the workload between CPU and GPU according to their performance on the fly. Also, it can be used with several contending technologies, like CUDA and OpenCL for GPUs or OpenMP and Intel TBB for CPUs. As a case in point, we analyse an image denoising problem based on time-step diffusion methods for brightness and chromaticity. Results show execution significant improvements in execution time using this scheme, with a minimal overhead.

中文翻译：

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IHP：迭代或时间步长方法的动态异构并行方案——以图像去噪为例研究

迭代和时间步长方法在多个数学和物理领域广泛传播。同时，现代计算机包括多核 CPU 和 GPU，因此重要的是要充分利用它们的所有计算能力来有效利用它们。为了提高这种数值方法的性能，我们在这项工作中引入了一种新的异构并行 CPU + GPU 方案，我们称之为 IHP。这种新方案具有自我平衡的优势，并且能够根据 CPU 和 GPU 的运行性能动态地在它们之间分配工作负载。此外，它还可以与多种竞争技术一起使用，例如用于 GPU 的 CUDA 和 OpenCL 或用于 CPU 的 OpenMP 和 Intel TBB。例如，我们分析了基于亮度和色度的时间步长扩散方法的图像去噪问题。