Recent developments in modern computational accelerators like Graphics Processing Units (GPUs) and coprocessors provide great opportunities for making scientific applications run faster than ever before. However, efficient parallelization of scientific code using new programming tools like CUDA requires a high level of expertise that is not available to many scientists. This, plus the fact that parallelized code is usually not portable to different architectures, creates major challenges for exploiting the full capabilities of modern computational accelerators. In this work, we sought to overcome these challenges by studying how to achieve both automated parallelization using OpenACC and enhanced portability using OpenCL. We applied our parallelization schemes using GPUs as well as Intel Many Integrated Core (MIC) coprocessor to reduce the run time of wave propagation simulations. We used a well-established 2D cardiac action potential model as a specific case-study. To the best of our knowledge, we are the first to study auto-parallelization of 2D cardiac wave propagation simulations using OpenACC. Our results identify several approaches that provide substantial speedups. The OpenACC-generated GPU code achieved more than speedup above the sequential implementation and required the addition of only a few OpenACC pragmas to the code. An OpenCL implementation provided speedups on GPUs of at least faster than the sequential implementation and faster than a parallelized OpenMP implementation. An implementation of OpenMP on Intel MIC coprocessor provided speedups of with only a few code changes to the sequential implementation. We highlight that OpenACC provides an automatic, efficient, and portable approach to achieve parallelization of 2D cardiac wave simulations on GPUs. Our approach of using OpenACC, OpenCL, and OpenMP to parallelize this particular model on modern computational accelerators should be applicable to other computational models of wave propagation in multi-dimensional media.
展开▼
机译：图形计算单元（GPU）和协处理器等现代计算加速器的最新发展为使科学应用程序以前所未有的速度运行提供了巨大的机会。但是，使用像CUDA这样的新编程工具对科学代码进行有效的并行化需要许多科学家无法获得的高级专业知识。加上并行化的代码通常不能移植到不同的体系结构这一事实，对利用现代计算加速器的全部功能提出了重大挑战。在这项工作中，我们试图通过研究如何使用OpenACC实现自动并行化和使用OpenCL增强可移植性来克服这些挑战。我们使用GPU和Intel Many Integrated Core（MIC）协处理器应用了并行化方案，以减少波传播仿真的运行时间。我们使用了公认的2D心脏动作电位模型作为特定案例研究。据我们所知，我们是第一个使用OpenACC研究2D心脏波传播模拟的自动并行化的公司。我们的结果确定了可以大大提高速度的几种方法。 OpenACC生成的GPU代码在顺序实现之上实现了远远超过的加速，并且只需要在代码中添加一些OpenACC编译指示即可。 OpenCL实现在GPU上的加速至少比顺序实现快，并且比并行化OpenMP实现快。英特尔MIC协处理器上的OpenMP实施仅通过对顺序实施进行少量代码更改即可加快速度。我们强调，OpenACC提供了一种自动，高效且可移植的方法来实现GPU上2D心电波仿真的并行化。我们使用OpenACC，OpenCL和OpenMP在现代计算加速器上并行化此特定模型的方法应适用于多维介质中波传播的其他计算模型。
展开▼
