Parameter Tuning Model for Optimizing Application Performance on GPU
Parameter Tuning Model for Optimizing Application Performance on GPU
复制标题
用于优化 GPU 上应用程序性能的参数调优模型
DOI：
10.1109/fas-w.2016.28
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Myungho Lee
中科院分区：
文献类型：
--
作者：
Nhat;Myungho Lee
近年来，图形处理单元（GPU）在高性能计算（HPC）应用中变得越来越流行。尽管GPU提供了高峰性能，但是，开发应用程序的全部性能潜力给程序员留下了具有挑战性的任务。当在GPU上启动应用程序的并行内核时，程序员需要仔细选择块的数量（网格大小）和每个块的线程数量（块大小），这会极大地影响性能。由于参数值的可能组合范围很大，因此选择正确的网格大小和块大小并不简单。在本文中，我们提出了一个模型来调整网格大小和块大小，通过它我们可以达到最佳的性能。该方法可以大大减少潜在的搜索空间，而不是以前的研究中的穷举搜索方法，这是不实用的真实的应用。
Recently, the Graphic Processing Units (GPUs) are becoming increasingly popular for the High Performance Computing (HPC) applications. Although the GPUs provide high peak performance, exploiting the full performance potential for application programs, however, leaves a challenging task to the programmers. When launching a parallel kernel of an application on the GPU, the programmer needs to carefully select the number of blocks (grid size) and the number of threads per block (block size) which greatly influence the performance. With a huge range of possible combinations of the parameter values, choosing the right grid size and the block size is not straightforward. In this paper, we propose a model for tuning the grid size and the block size through which we can reach the optimal performance. Our approach can significantly reduce the potential search space, instead of exhaustive search approaches in the previous research which are not practical in the real applications.